diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8b5d032a2e162c8b0ce26b6be973fd08f6740b3
--- /dev/null
+++ b/app.py
@@ -0,0 +1,194 @@
+from vits.models import SynthesizerInfer
+from omegaconf import OmegaConf
+import torchcrepe
+import torch
+import io
+import os
+import gradio as gr
+import librosa
+import numpy as np
+import soundfile
+
+import logging
+
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger('markdown_it').setLevel(logging.WARNING)
+logging.getLogger('urllib3').setLevel(logging.WARNING)
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+
+def load_svc_model(checkpoint_path, model):
+ assert os.path.isfile(checkpoint_path)
+ checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+ saved_state_dict = checkpoint_dict["model_g"]
+ state_dict = model.state_dict()
+ new_state_dict = {}
+ for k, v in state_dict.items():
+ new_state_dict[k] = saved_state_dict[k]
+ model.load_state_dict(new_state_dict)
+ return model
+
+
+def compute_f0_nn(filename, device):
+ audio, sr = librosa.load(filename, sr=16000)
+ assert sr == 16000
+ # Load audio
+ audio = torch.tensor(np.copy(audio))[None]
+ # Here we'll use a 20 millisecond hop length
+ hop_length = 320
+ # Provide a sensible frequency range for your domain (upper limit is 2006 Hz)
+ # This would be a reasonable range for speech
+ fmin = 50
+ fmax = 1000
+ # Select a model capacity--one of "tiny" or "full"
+ model = "full"
+ # Pick a batch size that doesn't cause memory errors on your gpu
+ batch_size = 512
+ # Compute pitch using first gpu
+ pitch, periodicity = torchcrepe.predict(
+ audio,
+ sr,
+ hop_length,
+ fmin,
+ fmax,
+ model,
+ batch_size=batch_size,
+ device=device,
+ return_periodicity=True,
+ )
+ pitch = np.repeat(pitch, 2, -1) # 320 -> 160 * 2
+ periodicity = np.repeat(periodicity, 2, -1) # 320 -> 160 * 2
+ # CREPE was not trained on silent audio. some error on silent need filter.
+ periodicity = torchcrepe.filter.median(periodicity, 9)
+ pitch = torchcrepe.filter.mean(pitch, 9)
+ pitch[periodicity < 0.1] = 0
+ pitch = pitch.squeeze(0)
+ return pitch
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+hp = OmegaConf.load("configs/base.yaml")
+model = SynthesizerInfer(
+ hp.data.filter_length // 2 + 1,
+ hp.data.segment_size // hp.data.hop_length,
+ hp)
+load_svc_model("vits_pretrain/sovits5.0-48k-debug.pth", model)
+model.eval()
+model.to(device)
+
+
+def svc_change(argswave, argsspk):
+
+ argsppg = "svc_tmp.ppg.npy"
+ os.system(f"python whisper/inference.py -w {argswave} -p {argsppg}")
+
+ spk = np.load(argsspk)
+ spk = torch.FloatTensor(spk)
+
+ ppg = np.load(argsppg)
+ ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2
+ ppg = torch.FloatTensor(ppg)
+
+ pit = compute_f0_nn(argswave, device)
+ pit = torch.FloatTensor(pit)
+
+ len_pit = pit.size()[0]
+ len_ppg = ppg.size()[0]
+ len_min = min(len_pit, len_ppg)
+ pit = pit[:len_min]
+ ppg = ppg[:len_min, :]
+
+ with torch.no_grad():
+
+ spk = spk.unsqueeze(0).to(device)
+ source = pit.unsqueeze(0).to(device)
+ source = model.pitch2source(source)
+
+ hop_size = hp.data.hop_length
+ all_frame = len_min
+ hop_frame = 10
+ out_chunk = 2500 # 25 S
+ out_index = 0
+ out_audio = []
+
+ while (out_index + out_chunk < all_frame):
+ if (out_index == 0): # start frame
+ cut_s = out_index
+ cut_s_48k = 0
+ else:
+ cut_s = out_index - hop_frame
+ cut_s_48k = hop_frame * hop_size
+
+ if (out_index + out_chunk + hop_frame > all_frame): # end frame
+ cut_e = out_index + out_chunk
+ cut_e_48k = 0
+ else:
+ cut_e = out_index + out_chunk + hop_frame
+ cut_e_48k = -1 * hop_frame * hop_size
+
+ sub_ppg = ppg[cut_s:cut_e, :].unsqueeze(0).to(device)
+ sub_pit = pit[cut_s:cut_e].unsqueeze(0).to(device)
+ sub_len = torch.LongTensor([cut_e - cut_s]).to(device)
+ sub_har = source[:, :, cut_s *
+ hop_size:cut_e * hop_size].to(device)
+ sub_out = model.inference(sub_ppg, sub_pit, spk, sub_len, sub_har)
+ sub_out = sub_out[0, 0].data.cpu().detach().numpy()
+
+ sub_out = sub_out[cut_s_48k:cut_e_48k]
+ out_audio.extend(sub_out)
+ out_index = out_index + out_chunk
+
+ if (out_index < all_frame):
+ cut_s = out_index - hop_frame
+ cut_s_48k = hop_frame * hop_size
+ sub_ppg = ppg[cut_s:, :].unsqueeze(0).to(device)
+ sub_pit = pit[cut_s:].unsqueeze(0).to(device)
+ sub_len = torch.LongTensor([all_frame - cut_s]).to(device)
+ sub_har = source[:, :, cut_s * hop_size:].to(device)
+ sub_out = model.inference(sub_ppg, sub_pit, spk, sub_len, sub_har)
+ sub_out = sub_out[0, 0].data.cpu().detach().numpy()
+
+ sub_out = sub_out[cut_s_48k:]
+ out_audio.extend(sub_out)
+ out_audio = np.asarray(out_audio)
+
+ return out_audio
+
+
+def svc_main(sid, input_audio):
+ if input_audio is None:
+ return "You need to upload an audio", None
+ sampling_rate, audio = input_audio
+ audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+ if len(audio.shape) > 1:
+ audio = librosa.to_mono(audio.transpose(1, 0))
+ if sampling_rate != 16000:
+ audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+ if (len(audio) > 16000*100):
+ audio = audio[:16000*100]
+ wav_path = "temp.wav"
+ soundfile.write(wav_path, audio, 16000, format="wav")
+ out_audio = svc_change(wav_path, f"configs/singers/singer00{sid}.npy")
+ return "Success", (48000, out_audio)
+
+
+app = gr.Blocks()
+with app:
+ with gr.Tabs():
+ with gr.TabItem("sovits 5.0"):
+ gr.Markdown(value="""
+ 基于开源数据:Multi-Singer
+
+ https://github.com/Multi-Singer/Multi-Singer.github.io
+
+ [轻度伴奏可以无需去伴奏]就能直接进行歌声转换的SVC库
+ """)
+ sid = gr.Dropdown(label="音色", choices=[
+ "22", "33", "47", "51"], value="47")
+ vc_input3 = gr.Audio(label="上传音频")
+ vc_submit = gr.Button("转换", variant="primary")
+ vc_output1 = gr.Textbox(label="Output Message")
+ vc_output2 = gr.Audio(label="准换后音频")
+ vc_submit.click(svc_main, [sid, vc_input3], [vc_output1, vc_output2])
+
+ app.launch()
diff --git a/configs/base.yaml b/configs/base.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c57681508bbd98d5df6c6d866d2825018fb7280
--- /dev/null
+++ b/configs/base.yaml
@@ -0,0 +1,69 @@
+train:
+ model: "sovits"
+ seed: 1234
+ epochs: 10000
+ learning_rate: 2e-4
+ betas: [0.8, 0.99]
+ lr_decay: 0.999875
+ eps: 1e-9
+ batch_size: 8
+ c_stft: 5
+ c_mel: 2.5
+ c_kl: 1.0
+ port: 8001
+ pretrain: ""
+#############################
+data:
+ training_files: "files/train.txt"
+ validation_files: "files/valid.txt"
+ segment_size: 12000 # WARNING: base on hop_length
+ max_wav_value: 32768.0
+ sampling_rate: 48000
+ filter_length: 2048
+ hop_length: 480
+ win_length: 2048
+ mel_channels: 80
+ mel_fmin: 0.0
+ mel_fmax: 24000.0
+#############################
+vits:
+ ppg_dim: 1024
+ spk_dim: 256
+ gin_channels: 256
+ inter_channels: 192
+ hidden_channels: 192
+ filter_channels: 512
+#############################
+gen:
+ upsample_input: 192
+ upsample_rates: [6,5,4,2,2]
+ upsample_kernel_sizes: [20,15,8,4,4]
+ upsample_initial_channel: 256
+ resblock_kernel_sizes: [3,7,11]
+ resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
+#############################
+mpd:
+ periods: [2,3,5,7,11]
+ kernel_size: 5
+ stride: 3
+ use_spectral_norm: False
+ lReLU_slope: 0.2
+#############################
+mrd:
+ resolutions: "[(1024, 120, 600), (2048, 240, 1200), (512, 50, 240)]" # (filter_length, hop_length, win_length)
+ use_spectral_norm: False
+ lReLU_slope: 0.2
+#############################
+log:
+ info_interval: 100
+ eval_interval: 5
+ save_interval: 5
+ num_audio: 6
+ pth_dir: 'chkpt'
+ log_dir: 'logs'
+#############################
+dist_config:
+ dist_backend: "nccl"
+ dist_url: "tcp://localhost:54321"
+ world_size: 1
+
diff --git a/configs/singers/singer0001.npy b/configs/singers/singer0001.npy
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diff --git a/configs/singers/singer0028.npy b/configs/singers/singer0028.npy
new file mode 100644
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--- /dev/null
+++ b/configs/singers/singer0028.npy
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diff --git a/configs/singers/singer0029.npy b/configs/singers/singer0029.npy
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0034.npy b/configs/singers/singer0034.npy
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--- /dev/null
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diff --git a/configs/singers/singer0035.npy b/configs/singers/singer0035.npy
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diff --git a/configs/singers/singer0036.npy b/configs/singers/singer0036.npy
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0037.npy b/configs/singers/singer0037.npy
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--- /dev/null
+++ b/configs/singers/singer0037.npy
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0038.npy b/configs/singers/singer0038.npy
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--- /dev/null
+++ b/configs/singers/singer0038.npy
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0039.npy b/configs/singers/singer0039.npy
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--- /dev/null
+++ b/configs/singers/singer0039.npy
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0040.npy b/configs/singers/singer0040.npy
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0041.npy b/configs/singers/singer0041.npy
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0042.npy b/configs/singers/singer0042.npy
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0043.npy b/configs/singers/singer0043.npy
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0044.npy b/configs/singers/singer0044.npy
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--- /dev/null
+++ b/configs/singers/singer0044.npy
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0045.npy b/configs/singers/singer0045.npy
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0046.npy b/configs/singers/singer0046.npy
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--- /dev/null
+++ b/configs/singers/singer0046.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0047.npy b/configs/singers/singer0047.npy
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--- /dev/null
+++ b/configs/singers/singer0047.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c8bb2fb993a55d13996df74463408c8e7e8d5e24b391887813e2ac1c204c9c4
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diff --git a/configs/singers/singer0048.npy b/configs/singers/singer0048.npy
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index 0000000000000000000000000000000000000000..71f0fbde409976dbc079d27b957d269d3dd59129
--- /dev/null
+++ b/configs/singers/singer0048.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0049.npy b/configs/singers/singer0049.npy
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--- /dev/null
+++ b/configs/singers/singer0049.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1152
diff --git a/configs/singers/singer0050.npy b/configs/singers/singer0050.npy
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--- /dev/null
+++ b/configs/singers/singer0050.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0051.npy b/configs/singers/singer0051.npy
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--- /dev/null
+++ b/configs/singers/singer0051.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0052.npy b/configs/singers/singer0052.npy
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--- /dev/null
+++ b/configs/singers/singer0052.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0053.npy b/configs/singers/singer0053.npy
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--- /dev/null
+++ b/configs/singers/singer0053.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0054.npy b/configs/singers/singer0054.npy
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--- /dev/null
+++ b/configs/singers/singer0054.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0055.npy b/configs/singers/singer0055.npy
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--- /dev/null
+++ b/configs/singers/singer0055.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/configs/singers/singer0056.npy b/configs/singers/singer0056.npy
new file mode 100644
index 0000000000000000000000000000000000000000..79f339eb5ef8319b5410749e19a5c647d7c2e81f
--- /dev/null
+++ b/configs/singers/singer0056.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07022464dcb678dddd3957ea431e53b4c79fd1904927d2622faef5c521da1b5e
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..992de39d92daf3b117a270ccb512161a5aa77a01
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+ffmpeg
+ffmpeg-python
+omegaconf
+praat-parselmouth
+pyffmpeg
+scikit-learn
+scipy
+soundfile
+toolz
+torch
+torchaudio
+torchcrepe
+transformers
+tqdm
+librosa
diff --git a/vits/LICENSE b/vits/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6a6c3181fcdc4e20901a6ecbee5a406b78a5b560
--- /dev/null
+++ b/vits/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Jaehyeon Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/vits/__init__.py b/vits/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/vits/attentions.py b/vits/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..26624519b01497cc402dea5f860cf5022d1e7c89
--- /dev/null
+++ b/vits/attentions.py
@@ -0,0 +1,416 @@
+import copy
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from vits import commons
+from vits.modules import LayerNorm
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size=1,
+ p_dropout=0.0,
+ window_size=4,
+ **kwargs
+ ):
+ super().__init__()
+ self.hidden_channels = hidden_channels
+ self.filter_channels = filter_channels
+ self.n_heads = n_heads
+ self.n_layers = n_layers
+ self.kernel_size = kernel_size
+ self.p_dropout = p_dropout
+ self.window_size = window_size
+
+ self.drop = nn.Dropout(p_dropout)
+ self.attn_layers = nn.ModuleList()
+ self.norm_layers_1 = nn.ModuleList()
+ self.ffn_layers = nn.ModuleList()
+ self.norm_layers_2 = nn.ModuleList()
+ for i in range(self.n_layers):
+ self.attn_layers.append(
+ MultiHeadAttention(
+ hidden_channels,
+ hidden_channels,
+ n_heads,
+ p_dropout=p_dropout,
+ window_size=window_size,
+ )
+ )
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
+ self.ffn_layers.append(
+ FFN(
+ hidden_channels,
+ hidden_channels,
+ filter_channels,
+ kernel_size,
+ p_dropout=p_dropout,
+ )
+ )
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+ def forward(self, x, x_mask):
+ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+ x = x * x_mask
+ for i in range(self.n_layers):
+ y = self.attn_layers[i](x, x, attn_mask)
+ y = self.drop(y)
+ x = self.norm_layers_1[i](x + y)
+
+ y = self.ffn_layers[i](x, x_mask)
+ y = self.drop(y)
+ x = self.norm_layers_2[i](x + y)
+ x = x * x_mask
+ return x
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size=1,
+ p_dropout=0.0,
+ proximal_bias=False,
+ proximal_init=True,
+ **kwargs
+ ):
+ super().__init__()
+ self.hidden_channels = hidden_channels
+ self.filter_channels = filter_channels
+ self.n_heads = n_heads
+ self.n_layers = n_layers
+ self.kernel_size = kernel_size
+ self.p_dropout = p_dropout
+ self.proximal_bias = proximal_bias
+ self.proximal_init = proximal_init
+
+ self.drop = nn.Dropout(p_dropout)
+ self.self_attn_layers = nn.ModuleList()
+ self.norm_layers_0 = nn.ModuleList()
+ self.encdec_attn_layers = nn.ModuleList()
+ self.norm_layers_1 = nn.ModuleList()
+ self.ffn_layers = nn.ModuleList()
+ self.norm_layers_2 = nn.ModuleList()
+ for i in range(self.n_layers):
+ self.self_attn_layers.append(
+ MultiHeadAttention(
+ hidden_channels,
+ hidden_channels,
+ n_heads,
+ p_dropout=p_dropout,
+ proximal_bias=proximal_bias,
+ proximal_init=proximal_init,
+ )
+ )
+ self.norm_layers_0.append(LayerNorm(hidden_channels))
+ self.encdec_attn_layers.append(
+ MultiHeadAttention(
+ hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+ )
+ )
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
+ self.ffn_layers.append(
+ FFN(
+ hidden_channels,
+ hidden_channels,
+ filter_channels,
+ kernel_size,
+ p_dropout=p_dropout,
+ causal=True,
+ )
+ )
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+ def forward(self, x, x_mask, h, h_mask):
+ """
+ x: decoder input
+ h: encoder output
+ """
+ self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
+ device=x.device, dtype=x.dtype
+ )
+ encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+ x = x * x_mask
+ for i in range(self.n_layers):
+ y = self.self_attn_layers[i](x, x, self_attn_mask)
+ y = self.drop(y)
+ x = self.norm_layers_0[i](x + y)
+
+ y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+ y = self.drop(y)
+ x = self.norm_layers_1[i](x + y)
+
+ y = self.ffn_layers[i](x, x_mask)
+ y = self.drop(y)
+ x = self.norm_layers_2[i](x + y)
+ x = x * x_mask
+ return x
+
+
+class MultiHeadAttention(nn.Module):
+ def __init__(
+ self,
+ channels,
+ out_channels,
+ n_heads,
+ p_dropout=0.0,
+ window_size=None,
+ heads_share=True,
+ block_length=None,
+ proximal_bias=False,
+ proximal_init=False,
+ ):
+ super().__init__()
+ assert channels % n_heads == 0
+
+ self.channels = channels
+ self.out_channels = out_channels
+ self.n_heads = n_heads
+ self.p_dropout = p_dropout
+ self.window_size = window_size
+ self.heads_share = heads_share
+ self.block_length = block_length
+ self.proximal_bias = proximal_bias
+ self.proximal_init = proximal_init
+ self.attn = None
+
+ self.k_channels = channels // n_heads
+ self.conv_q = nn.Conv1d(channels, channels, 1)
+ self.conv_k = nn.Conv1d(channels, channels, 1)
+ self.conv_v = nn.Conv1d(channels, channels, 1)
+ self.conv_o = nn.Conv1d(channels, out_channels, 1)
+ self.drop = nn.Dropout(p_dropout)
+
+ if window_size is not None:
+ n_heads_rel = 1 if heads_share else n_heads
+ rel_stddev = self.k_channels**-0.5
+ self.emb_rel_k = nn.Parameter(
+ torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+ * rel_stddev
+ )
+ self.emb_rel_v = nn.Parameter(
+ torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+ * rel_stddev
+ )
+
+ nn.init.xavier_uniform_(self.conv_q.weight)
+ nn.init.xavier_uniform_(self.conv_k.weight)
+ nn.init.xavier_uniform_(self.conv_v.weight)
+ if proximal_init:
+ with torch.no_grad():
+ self.conv_k.weight.copy_(self.conv_q.weight)
+ self.conv_k.bias.copy_(self.conv_q.bias)
+
+ def forward(self, x, c, attn_mask=None):
+ q = self.conv_q(x)
+ k = self.conv_k(c)
+ v = self.conv_v(c)
+
+ x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+ x = self.conv_o(x)
+ return x
+
+ def attention(self, query, key, value, mask=None):
+ # reshape [b, d, t] -> [b, n_h, t, d_k]
+ b, d, t_s, t_t = (*key.size(), query.size(2))
+ query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+ key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+ value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+ scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+ if self.window_size is not None:
+ assert (
+ t_s == t_t
+ ), "Relative attention is only available for self-attention."
+ key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+ rel_logits = self._matmul_with_relative_keys(
+ query / math.sqrt(self.k_channels), key_relative_embeddings
+ )
+ scores_local = self._relative_position_to_absolute_position(rel_logits)
+ scores = scores + scores_local
+ if self.proximal_bias:
+ assert t_s == t_t, "Proximal bias is only available for self-attention."
+ scores = scores + self._attention_bias_proximal(t_s).to(
+ device=scores.device, dtype=scores.dtype
+ )
+ if mask is not None:
+ scores = scores.masked_fill(mask == 0, -1e4)
+ if self.block_length is not None:
+ assert (
+ t_s == t_t
+ ), "Local attention is only available for self-attention."
+ block_mask = (
+ torch.ones_like(scores)
+ .triu(-self.block_length)
+ .tril(self.block_length)
+ )
+ scores = scores.masked_fill(block_mask == 0, -1e4)
+ p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+ p_attn = self.drop(p_attn)
+ output = torch.matmul(p_attn, value)
+ if self.window_size is not None:
+ relative_weights = self._absolute_position_to_relative_position(p_attn)
+ value_relative_embeddings = self._get_relative_embeddings(
+ self.emb_rel_v, t_s
+ )
+ output = output + self._matmul_with_relative_values(
+ relative_weights, value_relative_embeddings
+ )
+ output = (
+ output.transpose(2, 3).contiguous().view(b, d, t_t)
+ ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+ return output, p_attn
+
+ def _matmul_with_relative_values(self, x, y):
+ """
+ x: [b, h, l, m]
+ y: [h or 1, m, d]
+ ret: [b, h, l, d]
+ """
+ ret = torch.matmul(x, y.unsqueeze(0))
+ return ret
+
+ def _matmul_with_relative_keys(self, x, y):
+ """
+ x: [b, h, l, d]
+ y: [h or 1, m, d]
+ ret: [b, h, l, m]
+ """
+ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+ return ret
+
+ def _get_relative_embeddings(self, relative_embeddings, length):
+ max_relative_position = 2 * self.window_size + 1
+ # Pad first before slice to avoid using cond ops.
+ pad_length = max(length - (self.window_size + 1), 0)
+ slice_start_position = max((self.window_size + 1) - length, 0)
+ slice_end_position = slice_start_position + 2 * length - 1
+ if pad_length > 0:
+ padded_relative_embeddings = F.pad(
+ relative_embeddings,
+ commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+ )
+ else:
+ padded_relative_embeddings = relative_embeddings
+ used_relative_embeddings = padded_relative_embeddings[
+ :, slice_start_position:slice_end_position
+ ]
+ return used_relative_embeddings
+
+ def _relative_position_to_absolute_position(self, x):
+ """
+ x: [b, h, l, 2*l-1]
+ ret: [b, h, l, l]
+ """
+ batch, heads, length, _ = x.size()
+ # Concat columns of pad to shift from relative to absolute indexing.
+ x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+ # Concat extra elements so to add up to shape (len+1, 2*len-1).
+ x_flat = x.view([batch, heads, length * 2 * length])
+ x_flat = F.pad(
+ x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+ )
+
+ # Reshape and slice out the padded elements.
+ x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+ :, :, :length, length - 1 :
+ ]
+ return x_final
+
+ def _absolute_position_to_relative_position(self, x):
+ """
+ x: [b, h, l, l]
+ ret: [b, h, l, 2*l-1]
+ """
+ batch, heads, length, _ = x.size()
+ # padd along column
+ x = F.pad(
+ x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+ )
+ x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+ # add 0's in the beginning that will skew the elements after reshape
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+ x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+ return x_final
+
+ def _attention_bias_proximal(self, length):
+ """Bias for self-attention to encourage attention to close positions.
+ Args:
+ length: an integer scalar.
+ Returns:
+ a Tensor with shape [1, 1, length, length]
+ """
+ r = torch.arange(length, dtype=torch.float32)
+ diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+ return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ filter_channels,
+ kernel_size,
+ p_dropout=0.0,
+ activation=None,
+ causal=False,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.filter_channels = filter_channels
+ self.kernel_size = kernel_size
+ self.p_dropout = p_dropout
+ self.activation = activation
+ self.causal = causal
+
+ if causal:
+ self.padding = self._causal_padding
+ else:
+ self.padding = self._same_padding
+
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+ self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+ self.drop = nn.Dropout(p_dropout)
+
+ def forward(self, x, x_mask):
+ x = self.conv_1(self.padding(x * x_mask))
+ if self.activation == "gelu":
+ x = x * torch.sigmoid(1.702 * x)
+ else:
+ x = torch.relu(x)
+ x = self.drop(x)
+ x = self.conv_2(self.padding(x * x_mask))
+ return x * x_mask
+
+ def _causal_padding(self, x):
+ if self.kernel_size == 1:
+ return x
+ pad_l = self.kernel_size - 1
+ pad_r = 0
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+ x = F.pad(x, commons.convert_pad_shape(padding))
+ return x
+
+ def _same_padding(self, x):
+ if self.kernel_size == 1:
+ return x
+ pad_l = (self.kernel_size - 1) // 2
+ pad_r = self.kernel_size // 2
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+ x = F.pad(x, commons.convert_pad_shape(padding))
+ return x
diff --git a/vits/commons.py b/vits/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..045a538d5a3ef8033eca70639a894346b11d5f61
--- /dev/null
+++ b/vits/commons.py
@@ -0,0 +1,187 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def slice_pitch_segments(x, ids_str, segment_size=4):
+ ret = torch.zeros_like(x[:, :segment_size])
+ for i in range(x.size(0)):
+ idx_str = ids_str[i]
+ idx_end = idx_str + segment_size
+ ret[i] = x[i, idx_str:idx_end]
+ return ret
+
+
+def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
+ b, d, t = x.size()
+ if x_lengths is None:
+ x_lengths = t
+ ids_str_max = x_lengths - segment_size + 1
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+ ret = slice_segments(x, ids_str, segment_size)
+ ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
+ return ret, ret_pitch, ids_str
+
+
+def rand_spec_segments(x, x_lengths=None, segment_size=4):
+ b, d, t = x.size()
+ if x_lengths is None:
+ x_lengths = t
+ ids_str_max = x_lengths - segment_size
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+ ret = slice_segments(x, ids_str, segment_size)
+ return ret, ids_str
+
+
+def init_weights(m, mean=0.0, std=0.01):
+ classname = m.__class__.__name__
+ if classname.find("Conv") != -1:
+ m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+ return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+ l = pad_shape[::-1]
+ pad_shape = [item for sublist in l for item in sublist]
+ return pad_shape
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+ """KL(P||Q)"""
+ kl = (logs_q - logs_p) - 0.5
+ kl += (
+ 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+ )
+ return kl
+
+
+def rand_gumbel(shape):
+ """Sample from the Gumbel distribution, protect from overflows."""
+ uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+ return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+ g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+ return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+ ret = torch.zeros_like(x[:, :, :segment_size])
+ for i in range(x.size(0)):
+ idx_str = ids_str[i]
+ idx_end = idx_str + segment_size
+ ret[i] = x[i, :, idx_str:idx_end]
+ return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+ b, d, t = x.size()
+ if x_lengths is None:
+ x_lengths = t
+ ids_str_max = x_lengths - segment_size + 1
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+ ret = slice_segments(x, ids_str, segment_size)
+ return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+ position = torch.arange(length, dtype=torch.float)
+ num_timescales = channels // 2
+ log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+ num_timescales - 1
+ )
+ inv_timescales = min_timescale * torch.exp(
+ torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+ )
+ scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+ signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+ signal = F.pad(signal, [0, 0, 0, channels % 2])
+ signal = signal.view(1, channels, length)
+ return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+ b, channels, length = x.size()
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+ return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+ b, channels, length = x.size()
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+ return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+ mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+ return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+ n_channels_int = n_channels[0]
+ in_act = input_a + input_b
+ t_act = torch.tanh(in_act[:, :n_channels_int, :])
+ s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+ acts = t_act * s_act
+ return acts
+
+
+def convert_pad_shape(pad_shape):
+ l = pad_shape[::-1]
+ pad_shape = [item for sublist in l for item in sublist]
+ return pad_shape
+
+
+def shift_1d(x):
+ x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+ return x
+
+
+def sequence_mask(length, max_length=None):
+ if max_length is None:
+ max_length = length.max()
+ x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+ return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+ """
+ duration: [b, 1, t_x]
+ mask: [b, 1, t_y, t_x]
+ """
+ device = duration.device
+
+ b, _, t_y, t_x = mask.shape
+ cum_duration = torch.cumsum(duration, -1)
+
+ cum_duration_flat = cum_duration.view(b * t_x)
+ path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+ path = path.view(b, t_x, t_y)
+ path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+ path = path.unsqueeze(1).transpose(2, 3) * mask
+ return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+ if isinstance(parameters, torch.Tensor):
+ parameters = [parameters]
+ parameters = list(filter(lambda p: p.grad is not None, parameters))
+ norm_type = float(norm_type)
+ if clip_value is not None:
+ clip_value = float(clip_value)
+
+ total_norm = 0
+ for p in parameters:
+ param_norm = p.grad.data.norm(norm_type)
+ total_norm += param_norm.item() ** norm_type
+ if clip_value is not None:
+ p.grad.data.clamp_(min=-clip_value, max=clip_value)
+ total_norm = total_norm ** (1.0 / norm_type)
+ return total_norm
diff --git a/vits/data_utils.py b/vits/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e324f287603a82db668f679f74caf4ac5df9cf35
--- /dev/null
+++ b/vits/data_utils.py
@@ -0,0 +1,306 @@
+import os
+import numpy as np
+import random
+import torch
+import torch.utils.data
+
+
+from vits.utils import load_wav_to_torch
+
+
+def load_filepaths(filename, split="|"):
+ with open(filename, encoding='utf-8') as f:
+ filepaths = [line.strip().split(split) for line in f]
+ return filepaths
+
+
+class TextAudioSpeakerSet(torch.utils.data.Dataset):
+ def __init__(self, filename, hparams):
+ self.items = load_filepaths(filename)
+ self.max_wav_value = hparams.max_wav_value
+ self.sampling_rate = hparams.sampling_rate
+ self.segment_size = hparams.segment_size
+ self.hop_length = hparams.hop_length
+ self._filter()
+ print(f'----------{len(self.items)}----------')
+
+ def _filter(self):
+ lengths = []
+ items_new = []
+ items_min = int(self.segment_size / self.hop_length * 2) # 1 S
+ items_max = int(self.segment_size / self.hop_length * 9) # 4.5 S
+ for wavpath, spec, pitch, ppg, spk in self.items:
+ if not os.path.isfile(wavpath):
+ continue
+ if not os.path.isfile(spec):
+ continue
+ if not os.path.isfile(pitch):
+ continue
+ if not os.path.isfile(ppg):
+ continue
+ if not os.path.isfile(spk):
+ continue
+ temp = np.load(pitch)
+ usel = int(temp.shape[0] - 1) # useful length
+ if (usel < items_min):
+ continue
+ if (usel >= items_max):
+ usel = items_max
+ items_new.append([wavpath, spec, pitch, ppg, spk, usel])
+ lengths.append(usel)
+ self.items = items_new
+ self.lengths = lengths
+
+ def read_wav(self, filename):
+ audio, sampling_rate = load_wav_to_torch(filename)
+ assert sampling_rate == self.sampling_rate, f"error: this sample rate of {filename} is {sampling_rate}"
+ audio_norm = audio / self.max_wav_value
+ audio_norm = audio_norm.unsqueeze(0)
+ return audio_norm
+
+ def __getitem__(self, index):
+ return self.my_getitem(index)
+
+ def __len__(self):
+ return len(self.items)
+
+ def my_getitem(self, idx):
+ item = self.items[idx]
+ # print(item)
+ wav = item[0]
+ spe = item[1]
+ pit = item[2]
+ ppg = item[3]
+ spk = item[4]
+ use = item[5]
+
+ wav = self.read_wav(wav)
+ spe = torch.load(spe)
+
+ pit = np.load(pit)
+ ppg = np.load(ppg)
+ ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2
+ spk = np.load(spk)
+
+ pit = torch.FloatTensor(pit)
+ ppg = torch.FloatTensor(ppg)
+ spk = torch.FloatTensor(spk)
+
+ len_pit = pit.size()[0]
+ len_ppg = ppg.size()[0]
+ len_min = min(len_pit, len_ppg)
+ len_wav = len_min * self.hop_length
+
+ pit = pit[:len_min]
+ ppg = ppg[:len_min, :]
+ spe = spe[:, :len_min]
+ wav = wav[:, :len_wav]
+ if len_min > use:
+ max_frame_start = ppg.size(0) - use - 1
+ frame_start = random.randint(0, max_frame_start)
+ frame_end = frame_start + use
+
+ pit = pit[frame_start:frame_end]
+ ppg = ppg[frame_start:frame_end, :]
+ spe = spe[:, frame_start:frame_end]
+
+ wav_start = frame_start * self.hop_length
+ wav_end = frame_end * self.hop_length
+ wav = wav[:, wav_start:wav_end]
+ # print(spe.shape)
+ # print(wav.shape)
+ # print(ppg.shape)
+ # print(pit.shape)
+ # print(spk.shape)
+ return spe, wav, ppg, pit, spk
+
+
+class TextAudioSpeakerCollate:
+ """Zero-pads model inputs and targets"""
+
+ def __call__(self, batch):
+ # Right zero-pad all one-hot text sequences to max input length
+ # mel: [freq, length]
+ # wav: [1, length]
+ # ppg: [len, 1024]
+ # pit: [len]
+ # spk: [256]
+ _, ids_sorted_decreasing = torch.sort(
+ torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
+ )
+
+ max_spe_len = max([x[0].size(1) for x in batch])
+ max_wav_len = max([x[1].size(1) for x in batch])
+ spe_lengths = torch.LongTensor(len(batch))
+ wav_lengths = torch.LongTensor(len(batch))
+ spe_padded = torch.FloatTensor(
+ len(batch), batch[0][0].size(0), max_spe_len)
+ wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+ spe_padded.zero_()
+ wav_padded.zero_()
+
+ max_ppg_len = max([x[2].size(0) for x in batch])
+ ppg_lengths = torch.FloatTensor(len(batch))
+ ppg_padded = torch.FloatTensor(
+ len(batch), max_ppg_len, batch[0][2].size(1))
+ pit_padded = torch.FloatTensor(len(batch), max_ppg_len)
+ ppg_padded.zero_()
+ pit_padded.zero_()
+ spk = torch.FloatTensor(len(batch), batch[0][4].size(0))
+
+ for i in range(len(ids_sorted_decreasing)):
+ row = batch[ids_sorted_decreasing[i]]
+
+ spe = row[0]
+ spe_padded[i, :, : spe.size(1)] = spe
+ spe_lengths[i] = spe.size(1)
+
+ wav = row[1]
+ wav_padded[i, :, : wav.size(1)] = wav
+ wav_lengths[i] = wav.size(1)
+
+ ppg = row[2]
+ ppg_padded[i, : ppg.size(0), :] = ppg
+ ppg_lengths[i] = ppg.size(0)
+
+ pit = row[3]
+ pit_padded[i, : pit.size(0)] = pit
+
+ spk[i] = row[4]
+ # print(ppg_padded.shape)
+ # print(ppg_lengths.shape)
+ # print(pit_padded.shape)
+ # print(spk.shape)
+ # print(spe_padded.shape)
+ # print(spe_lengths.shape)
+ # print(wav_padded.shape)
+ # print(wav_lengths.shape)
+ return (
+ ppg_padded,
+ ppg_lengths,
+ pit_padded,
+ spk,
+ spe_padded,
+ spe_lengths,
+ wav_padded,
+ wav_lengths,
+ )
+
+
+class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
+ """
+ Maintain similar input lengths in a batch.
+ Length groups are specified by boundaries.
+ Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
+ It removes samples which are not included in the boundaries.
+ Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
+ """
+
+ def __init__(
+ self,
+ dataset,
+ batch_size,
+ boundaries,
+ num_replicas=None,
+ rank=None,
+ shuffle=True,
+ ):
+ super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+ self.lengths = dataset.lengths
+ self.batch_size = batch_size
+ self.boundaries = boundaries
+
+ self.buckets, self.num_samples_per_bucket = self._create_buckets()
+ self.total_size = sum(self.num_samples_per_bucket)
+ self.num_samples = self.total_size // self.num_replicas
+
+ def _create_buckets(self):
+ buckets = [[] for _ in range(len(self.boundaries) - 1)]
+ for i in range(len(self.lengths)):
+ length = self.lengths[i]
+ idx_bucket = self._bisect(length)
+ if idx_bucket != -1:
+ buckets[idx_bucket].append(i)
+
+ for i in range(len(buckets) - 1, 0, -1):
+ if len(buckets[i]) == 0:
+ buckets.pop(i)
+ self.boundaries.pop(i + 1)
+
+ num_samples_per_bucket = []
+ for i in range(len(buckets)):
+ len_bucket = len(buckets[i])
+ total_batch_size = self.num_replicas * self.batch_size
+ rem = (
+ total_batch_size - (len_bucket % total_batch_size)
+ ) % total_batch_size
+ num_samples_per_bucket.append(len_bucket + rem)
+ return buckets, num_samples_per_bucket
+
+ def __iter__(self):
+ # deterministically shuffle based on epoch
+ g = torch.Generator()
+ g.manual_seed(self.epoch)
+
+ indices = []
+ if self.shuffle:
+ for bucket in self.buckets:
+ indices.append(torch.randperm(
+ len(bucket), generator=g).tolist())
+ else:
+ for bucket in self.buckets:
+ indices.append(list(range(len(bucket))))
+
+ batches = []
+ for i in range(len(self.buckets)):
+ bucket = self.buckets[i]
+ len_bucket = len(bucket)
+ ids_bucket = indices[i]
+ num_samples_bucket = self.num_samples_per_bucket[i]
+
+ # add extra samples to make it evenly divisible
+ rem = num_samples_bucket - len_bucket
+ ids_bucket = (
+ ids_bucket
+ + ids_bucket * (rem // len_bucket)
+ + ids_bucket[: (rem % len_bucket)]
+ )
+
+ # subsample
+ ids_bucket = ids_bucket[self.rank:: self.num_replicas]
+
+ # batching
+ for j in range(len(ids_bucket) // self.batch_size):
+ batch = [
+ bucket[idx]
+ for idx in ids_bucket[
+ j * self.batch_size: (j + 1) * self.batch_size
+ ]
+ ]
+ batches.append(batch)
+
+ if self.shuffle:
+ batch_ids = torch.randperm(len(batches), generator=g).tolist()
+ batches = [batches[i] for i in batch_ids]
+ self.batches = batches
+
+ assert len(self.batches) * self.batch_size == self.num_samples
+ return iter(self.batches)
+
+ def _bisect(self, x, lo=0, hi=None):
+ if hi is None:
+ hi = len(self.boundaries) - 1
+
+ if hi > lo:
+ mid = (hi + lo) // 2
+ if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
+ return mid
+ elif x <= self.boundaries[mid]:
+ return self._bisect(x, lo, mid)
+ else:
+ return self._bisect(x, mid + 1, hi)
+ else:
+ return -1
+
+ def __len__(self):
+ return self.num_samples // self.batch_size
diff --git a/vits/losses.py b/vits/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..9244de65482650fedea4de6e3cc26d4700ee76e9
--- /dev/null
+++ b/vits/losses.py
@@ -0,0 +1,79 @@
+import torch
+
+
+def feature_loss(fmap_r, fmap_g):
+ loss = 0
+ for dr, dg in zip(fmap_r, fmap_g):
+ for rl, gl in zip(dr, dg):
+ rl = rl.float().detach()
+ gl = gl.float()
+ loss += torch.mean(torch.abs(rl - gl))
+
+ return loss * 2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+ loss = 0
+ r_losses = []
+ g_losses = []
+ for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+ dr = dr.float()
+ dg = dg.float()
+ r_loss = torch.mean((1 - dr) ** 2)
+ g_loss = torch.mean(dg**2)
+ loss += r_loss + g_loss
+ r_losses.append(r_loss.item())
+ g_losses.append(g_loss.item())
+
+ return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+ loss = 0
+ gen_losses = []
+ for dg in disc_outputs:
+ dg = dg.float()
+ l = torch.mean((1 - dg) ** 2)
+ gen_losses.append(l)
+ loss += l
+
+ return loss, gen_losses
+
+
+def kl_loss(z_p, logs_q, m_p, logs_p, total_logdet, z_mask):
+ """
+ z_p, logs_q: [b, h, t_t]
+ m_p, logs_p: [b, h, t_t]
+ total_logdet: [b] - total_logdet summed over each batch
+ """
+ z_p = z_p.float()
+ logs_q = logs_q.float()
+ m_p = m_p.float()
+ logs_p = logs_p.float()
+ z_mask = z_mask.float()
+
+ kl = logs_p - logs_q - 0.5
+ kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
+ kl = torch.sum(kl * z_mask)
+ # add total_logdet (Negative LL)
+ kl -= torch.sum(total_logdet)
+ l = kl / torch.sum(z_mask)
+ return l
+
+
+def kl_loss_back(z_p, logs_q, m_p, logs_p, z_mask):
+ """
+ z_p, logs_q: [b, h, t_t]
+ m_p, logs_p: [b, h, t_t]
+ """
+ z_p = z_p.float()
+ logs_q = logs_q.float()
+ m_p = m_p.float()
+ logs_p = logs_p.float()
+ z_mask = z_mask.float()
+
+ kl = logs_p - logs_q - 0.5
+ kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
+ kl = torch.sum(kl * z_mask)
+ l = kl / torch.sum(z_mask)
+ return l
diff --git a/vits/models.py b/vits/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..9de6f7cce8512aa1bb73c9f78a0a23d219f8e8ec
--- /dev/null
+++ b/vits/models.py
@@ -0,0 +1,240 @@
+
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+from vits import attentions
+from vits import commons
+from vits import modules
+from vits.utils import f0_to_coarse
+from vits_decoder.generator import Generator
+
+
+class TextEncoder(nn.Module):
+ def __init__(self,
+ in_channels,
+ out_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout):
+ super().__init__()
+ self.out_channels = out_channels
+ self.pre = nn.Conv1d(in_channels, hidden_channels, kernel_size=5, padding=2)
+ self.pit = nn.Embedding(256, hidden_channels)
+ self.enc = attentions.Encoder(
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout)
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+ def forward(self, x, x_lengths, f0):
+ x = torch.transpose(x, 1, -1) # [b, h, t]
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+ x.dtype
+ )
+ x = self.pre(x) * x_mask
+ x = x + self.pit(f0).transpose(1, 2)
+ x = self.enc(x * x_mask, x_mask)
+ stats = self.proj(x) * x_mask
+ m, logs = torch.split(stats, self.out_channels, dim=1)
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+ return z, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+ def __init__(
+ self,
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ n_flows=4,
+ gin_channels=0,
+ ):
+ super().__init__()
+ self.flows = nn.ModuleList()
+ for i in range(n_flows):
+ self.flows.append(
+ modules.ResidualCouplingLayer(
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=gin_channels,
+ mean_only=True,
+ )
+ )
+ self.flows.append(modules.Flip())
+
+ def forward(self, x, x_mask, g=None, reverse=False):
+ if not reverse:
+ total_logdet = 0
+ for flow in self.flows:
+ x, log_det = flow(x, x_mask, g=g, reverse=reverse)
+ total_logdet += log_det
+ return x, total_logdet
+ else:
+ total_logdet = 0
+ for flow in reversed(self.flows):
+ x, log_det = flow(x, x_mask, g=g, reverse=reverse)
+ total_logdet += log_det
+ return x, total_logdet
+
+ def remove_weight_norm(self):
+ for i in range(self.n_flows):
+ self.flows[i * 2].remove_weight_norm()
+
+
+class PosteriorEncoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=0,
+ ):
+ super().__init__()
+ self.out_channels = out_channels
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+ self.enc = modules.WN(
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=gin_channels,
+ )
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+ def forward(self, x, x_lengths, g=None):
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+ x.dtype
+ )
+ x = self.pre(x) * x_mask
+ x = self.enc(x, x_mask, g=g)
+ stats = self.proj(x) * x_mask
+ m, logs = torch.split(stats, self.out_channels, dim=1)
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+ return z, m, logs, x_mask
+
+ def remove_weight_norm(self):
+ self.enc.remove_weight_norm()
+
+
+class SynthesizerTrn(nn.Module):
+ def __init__(
+ self,
+ spec_channels,
+ segment_size,
+ hp
+ ):
+ super().__init__()
+ self.segment_size = segment_size
+ self.emb_g = nn.Linear(hp.vits.spk_dim, hp.vits.gin_channels)
+ self.enc_p = TextEncoder(
+ hp.vits.ppg_dim,
+ hp.vits.inter_channels,
+ hp.vits.hidden_channels,
+ hp.vits.filter_channels,
+ 2,
+ 6,
+ 3,
+ 0.1,
+ )
+ self.enc_q = PosteriorEncoder(
+ spec_channels,
+ hp.vits.inter_channels,
+ hp.vits.hidden_channels,
+ 5,
+ 1,
+ 16,
+ gin_channels=hp.vits.gin_channels,
+ )
+ self.flow = ResidualCouplingBlock(
+ hp.vits.inter_channels,
+ hp.vits.hidden_channels,
+ 5,
+ 1,
+ 4,
+ gin_channels=hp.vits.spk_dim
+ )
+ self.dec = Generator(hp=hp)
+
+ def forward(self, ppg, pit, spec, spk, ppg_l, spec_l):
+ g = self.emb_g(F.normalize(spk)).unsqueeze(-1)
+ z_p, m_p, logs_p, ppg_mask = self.enc_p(
+ ppg, ppg_l, f0=f0_to_coarse(pit))
+ z_q, m_q, logs_q, spec_mask = self.enc_q(spec, spec_l, g=g)
+
+ z_slice, pit_slice, ids_slice = commons.rand_slice_segments_with_pitch(
+ z_q, pit, spec_l, self.segment_size)
+ audio = self.dec(spk, z_slice, pit_slice)
+
+ # SNAC to flow
+ z_f, logdet_f = self.flow(z_q, spec_mask, g=spk)
+ z_r, logdet_r = self.flow(z_p, spec_mask, g=spk, reverse=True)
+ return audio, ids_slice, spec_mask, (z_f, z_r, z_p, m_p, logs_p, z_q, m_q, logs_q, logdet_f, logdet_r)
+
+ def infer(self, ppg, pit, spk, ppg_l):
+ z_p, m_p, logs_p, ppg_mask = self.enc_p(
+ ppg, ppg_l, f0=f0_to_coarse(pit))
+ z, _ = self.flow(z_p, ppg_mask, g=spk, reverse=True)
+ o = self.dec(spk, z * ppg_mask, f0=pit)
+ return o
+
+
+class SynthesizerInfer(nn.Module):
+ def __init__(
+ self,
+ spec_channels,
+ segment_size,
+ hp
+ ):
+ super().__init__()
+ self.segment_size = segment_size
+ self.enc_p = TextEncoder(
+ hp.vits.ppg_dim,
+ hp.vits.inter_channels,
+ hp.vits.hidden_channels,
+ hp.vits.filter_channels,
+ 2,
+ 6,
+ 3,
+ 0.1,
+ )
+ self.flow = ResidualCouplingBlock(
+ hp.vits.inter_channels,
+ hp.vits.hidden_channels,
+ 5,
+ 1,
+ 4,
+ gin_channels=hp.vits.spk_dim
+ )
+ self.dec = Generator(hp=hp)
+
+ def remove_weight_norm(self):
+ self.flow.remove_weight_norm()
+ self.dec.remove_weight_norm()
+
+ def pitch2source(self, f0):
+ return self.dec.pitch2source(f0)
+
+ def source2wav(self, source):
+ return self.dec.source2wav(source)
+
+ def inference(self, ppg, pit, spk, ppg_l, source):
+ z_p, m_p, logs_p, ppg_mask = self.enc_p(
+ ppg, ppg_l, f0=f0_to_coarse(pit))
+ z, _ = self.flow(z_p, ppg_mask, g=spk, reverse=True)
+ o = self.dec.inference(spk, z * ppg_mask, source)
+ return o
diff --git a/vits/modules.py b/vits/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fe730b8bd45de6ae7a25cb8213b50a6f662db8e
--- /dev/null
+++ b/vits/modules.py
@@ -0,0 +1,475 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from vits import commons
+from vits.commons import init_weights, get_padding
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+ def __init__(self, channels, eps=1e-5):
+ super().__init__()
+ self.channels = channels
+ self.eps = eps
+
+ self.gamma = nn.Parameter(torch.ones(channels))
+ self.beta = nn.Parameter(torch.zeros(channels))
+
+ def forward(self, x):
+ x = x.transpose(1, -1)
+ x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+ return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ hidden_channels,
+ out_channels,
+ kernel_size,
+ n_layers,
+ p_dropout,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.hidden_channels = hidden_channels
+ self.out_channels = out_channels
+ self.kernel_size = kernel_size
+ self.n_layers = n_layers
+ self.p_dropout = p_dropout
+ assert n_layers > 1, "Number of layers should be larger than 0."
+
+ self.conv_layers = nn.ModuleList()
+ self.norm_layers = nn.ModuleList()
+ self.conv_layers.append(
+ nn.Conv1d(
+ in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+ )
+ )
+ self.norm_layers.append(LayerNorm(hidden_channels))
+ self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+ for _ in range(n_layers - 1):
+ self.conv_layers.append(
+ nn.Conv1d(
+ hidden_channels,
+ hidden_channels,
+ kernel_size,
+ padding=kernel_size // 2,
+ )
+ )
+ self.norm_layers.append(LayerNorm(hidden_channels))
+ self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+ self.proj.weight.data.zero_()
+ self.proj.bias.data.zero_()
+
+ def forward(self, x, x_mask):
+ x_org = x
+ for i in range(self.n_layers):
+ x = self.conv_layers[i](x * x_mask)
+ x = self.norm_layers[i](x)
+ x = self.relu_drop(x)
+ x = x_org + self.proj(x)
+ return x * x_mask
+
+
+class DDSConv(nn.Module):
+ """
+ Dialted and Depth-Separable Convolution
+ """
+
+ def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+ super().__init__()
+ self.channels = channels
+ self.kernel_size = kernel_size
+ self.n_layers = n_layers
+ self.p_dropout = p_dropout
+
+ self.drop = nn.Dropout(p_dropout)
+ self.convs_sep = nn.ModuleList()
+ self.convs_1x1 = nn.ModuleList()
+ self.norms_1 = nn.ModuleList()
+ self.norms_2 = nn.ModuleList()
+ for i in range(n_layers):
+ dilation = kernel_size**i
+ padding = (kernel_size * dilation - dilation) // 2
+ self.convs_sep.append(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ groups=channels,
+ dilation=dilation,
+ padding=padding,
+ )
+ )
+ self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+ self.norms_1.append(LayerNorm(channels))
+ self.norms_2.append(LayerNorm(channels))
+
+ def forward(self, x, x_mask, g=None):
+ if g is not None:
+ x = x + g
+ for i in range(self.n_layers):
+ y = self.convs_sep[i](x * x_mask)
+ y = self.norms_1[i](y)
+ y = F.gelu(y)
+ y = self.convs_1x1[i](y)
+ y = self.norms_2[i](y)
+ y = F.gelu(y)
+ y = self.drop(y)
+ x = x + y
+ return x * x_mask
+
+
+class WN(torch.nn.Module):
+ def __init__(
+ self,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=0,
+ p_dropout=0,
+ ):
+ super(WN, self).__init__()
+ assert kernel_size % 2 == 1
+ self.hidden_channels = hidden_channels
+ self.kernel_size = (kernel_size,)
+ self.dilation_rate = dilation_rate
+ self.n_layers = n_layers
+ self.gin_channels = gin_channels
+ self.p_dropout = p_dropout
+
+ self.in_layers = torch.nn.ModuleList()
+ self.res_skip_layers = torch.nn.ModuleList()
+ self.drop = nn.Dropout(p_dropout)
+
+ if gin_channels != 0:
+ cond_layer = torch.nn.Conv1d(
+ gin_channels, 2 * hidden_channels * n_layers, 1
+ )
+ self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+ for i in range(n_layers):
+ dilation = dilation_rate**i
+ padding = int((kernel_size * dilation - dilation) / 2)
+ in_layer = torch.nn.Conv1d(
+ hidden_channels,
+ 2 * hidden_channels,
+ kernel_size,
+ dilation=dilation,
+ padding=padding,
+ )
+ in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+ self.in_layers.append(in_layer)
+
+ # last one is not necessary
+ if i < n_layers - 1:
+ res_skip_channels = 2 * hidden_channels
+ else:
+ res_skip_channels = hidden_channels
+
+ res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+ res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+ self.res_skip_layers.append(res_skip_layer)
+
+ def forward(self, x, x_mask, g=None, **kwargs):
+ output = torch.zeros_like(x)
+ n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+ if g is not None:
+ g = self.cond_layer(g)
+
+ for i in range(self.n_layers):
+ x_in = self.in_layers[i](x)
+ if g is not None:
+ cond_offset = i * 2 * self.hidden_channels
+ g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+ else:
+ g_l = torch.zeros_like(x_in)
+
+ acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+ acts = self.drop(acts)
+
+ res_skip_acts = self.res_skip_layers[i](acts)
+ if i < self.n_layers - 1:
+ res_acts = res_skip_acts[:, : self.hidden_channels, :]
+ x = (x + res_acts) * x_mask
+ output = output + res_skip_acts[:, self.hidden_channels:, :]
+ else:
+ output = output + res_skip_acts
+ return output * x_mask
+
+ def remove_weight_norm(self):
+ if self.gin_channels != 0:
+ torch.nn.utils.remove_weight_norm(self.cond_layer)
+ for l in self.in_layers:
+ torch.nn.utils.remove_weight_norm(l)
+ for l in self.res_skip_layers:
+ torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+ super(ResBlock1, self).__init__()
+ self.convs1 = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=get_padding(kernel_size, dilation[0]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=get_padding(kernel_size, dilation[1]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[2],
+ padding=get_padding(kernel_size, dilation[2]),
+ )
+ ),
+ ]
+ )
+ self.convs1.apply(init_weights)
+
+ self.convs2 = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ ]
+ )
+ self.convs2.apply(init_weights)
+
+ def forward(self, x, x_mask=None):
+ for c1, c2 in zip(self.convs1, self.convs2):
+ xt = F.leaky_relu(x, LRELU_SLOPE)
+ if x_mask is not None:
+ xt = xt * x_mask
+ xt = c1(xt)
+ xt = F.leaky_relu(xt, LRELU_SLOPE)
+ if x_mask is not None:
+ xt = xt * x_mask
+ xt = c2(xt)
+ x = xt + x
+ if x_mask is not None:
+ x = x * x_mask
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.convs1:
+ remove_weight_norm(l)
+ for l in self.convs2:
+ remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+ super(ResBlock2, self).__init__()
+ self.convs = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=get_padding(kernel_size, dilation[0]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=get_padding(kernel_size, dilation[1]),
+ )
+ ),
+ ]
+ )
+ self.convs.apply(init_weights)
+
+ def forward(self, x, x_mask=None):
+ for c in self.convs:
+ xt = F.leaky_relu(x, LRELU_SLOPE)
+ if x_mask is not None:
+ xt = xt * x_mask
+ xt = c(xt)
+ x = xt + x
+ if x_mask is not None:
+ x = x * x_mask
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.convs:
+ remove_weight_norm(l)
+
+
+class Log(nn.Module):
+ def forward(self, x, x_mask, reverse=False, **kwargs):
+ if not reverse:
+ y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+ logdet = torch.sum(-y, [1, 2])
+ return y, logdet
+ else:
+ x = torch.exp(x) * x_mask
+ return x
+
+
+class Flip(nn.Module):
+ def forward(self, x, *args, reverse=False, **kwargs):
+ x = torch.flip(x, [1])
+ logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+ return x, logdet
+
+
+class ElementwiseAffine(nn.Module):
+ def __init__(self, channels):
+ super().__init__()
+ self.channels = channels
+ self.m = nn.Parameter(torch.zeros(channels, 1))
+ self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+ def forward(self, x, x_mask, reverse=False, **kwargs):
+ if not reverse:
+ y = self.m + torch.exp(self.logs) * x
+ y = y * x_mask
+ logdet = torch.sum(self.logs * x_mask, [1, 2])
+ return y, logdet
+ else:
+ x = (x - self.m) * torch.exp(-self.logs) * x_mask
+ return x
+
+
+class ResidualCouplingLayer(nn.Module):
+ def __init__(
+ self,
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ p_dropout=0,
+ gin_channels=0,
+ mean_only=False,
+ ):
+ assert channels % 2 == 0, "channels should be divisible by 2"
+ super().__init__()
+ self.channels = channels
+ self.hidden_channels = hidden_channels
+ self.kernel_size = kernel_size
+ self.dilation_rate = dilation_rate
+ self.n_layers = n_layers
+ self.half_channels = channels // 2
+ self.mean_only = mean_only
+
+ self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+ # no use gin_channels
+ self.enc = WN(
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ p_dropout=p_dropout,
+ )
+ self.post = nn.Conv1d(
+ hidden_channels, self.half_channels * (2 - mean_only), 1)
+ self.post.weight.data.zero_()
+ self.post.bias.data.zero_()
+ # SNAC Speaker-normalized Affine Coupling Layer
+ self.snac = nn.Conv1d(gin_channels, 2 * self.half_channels, 1)
+
+ def forward(self, x, x_mask, g=None, reverse=False):
+ speaker = self.snac(g.unsqueeze(-1))
+ speaker_m, speaker_v = speaker.chunk(2, dim=1) # (B, half_channels, 1)
+ x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+ # x0 norm
+ x0_norm = (x0 - speaker_m) * torch.exp(-speaker_v) * x_mask
+ h = self.pre(x0_norm) * x_mask
+ # don't use global condition
+ h = self.enc(h, x_mask)
+ stats = self.post(h) * x_mask
+ if not self.mean_only:
+ m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+ else:
+ m = stats
+ logs = torch.zeros_like(m)
+
+ if not reverse:
+ # x1 norm before affine xform
+ x1_norm = (x1 - speaker_m) * torch.exp(-speaker_v) * x_mask
+ x1 = (m + x1_norm * torch.exp(logs)) * x_mask
+ x = torch.cat([x0, x1], 1)
+ # speaker var to logdet
+ logdet = torch.sum(logs * x_mask, [1, 2]) - torch.sum(
+ speaker_v.expand(-1, -1, logs.size(-1)) * x_mask, [1, 2])
+ return x, logdet
+ else:
+ x1 = (x1 - m) * torch.exp(-logs) * x_mask
+ # x1 denorm before output
+ x1 = (speaker_m + x1 * torch.exp(speaker_v)) * x_mask
+ x = torch.cat([x0, x1], 1)
+ # speaker var to logdet
+ logdet = torch.sum(logs * x_mask, [1, 2]) + torch.sum(
+ speaker_v.expand(-1, -1, logs.size(-1)) * x_mask, [1, 2])
+ return x, logdet
+
+ def remove_weight_norm(self):
+ self.enc.remove_weight_norm()
diff --git a/vits/spectrogram.py b/vits/spectrogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b54b1757f977f840ba97e0ad28b241fceeecd7
--- /dev/null
+++ b/vits/spectrogram.py
@@ -0,0 +1,140 @@
+import torch
+import torch.utils.data
+
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+ """
+ PARAMS
+ ------
+ C: compression factor
+ """
+ return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+ """
+ PARAMS
+ ------
+ C: compression factor used to compress
+ """
+ return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+ output = dynamic_range_compression_torch(magnitudes)
+ return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+ output = dynamic_range_decompression_torch(magnitudes)
+ return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+ if torch.min(y) < -1.0:
+ print("min value is ", torch.min(y))
+ if torch.max(y) > 1.0:
+ print("max value is ", torch.max(y))
+
+ global hann_window
+ dtype_device = str(y.dtype) + "_" + str(y.device)
+ wnsize_dtype_device = str(win_size) + "_" + dtype_device
+ if wnsize_dtype_device not in hann_window:
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+ dtype=y.dtype, device=y.device
+ )
+
+ y = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+ mode="reflect",
+ )
+ y = y.squeeze(1)
+
+ spec = torch.stft(
+ y,
+ n_fft,
+ hop_length=hop_size,
+ win_length=win_size,
+ window=hann_window[wnsize_dtype_device],
+ center=center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=False,
+ )
+
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+ return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+ global mel_basis
+ dtype_device = str(spec.dtype) + "_" + str(spec.device)
+ fmax_dtype_device = str(fmax) + "_" + dtype_device
+ if fmax_dtype_device not in mel_basis:
+ mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+ dtype=spec.dtype, device=spec.device
+ )
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+ spec = spectral_normalize_torch(spec)
+ return spec
+
+
+def mel_spectrogram_torch(
+ y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+ if torch.min(y) < -1.0:
+ print("min value is ", torch.min(y))
+ if torch.max(y) > 1.0:
+ print("max value is ", torch.max(y))
+
+ global mel_basis, hann_window
+ dtype_device = str(y.dtype) + "_" + str(y.device)
+ fmax_dtype_device = str(fmax) + "_" + dtype_device
+ wnsize_dtype_device = str(win_size) + "_" + dtype_device
+ if fmax_dtype_device not in mel_basis:
+ mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+ dtype=y.dtype, device=y.device
+ )
+ if wnsize_dtype_device not in hann_window:
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+ dtype=y.dtype, device=y.device
+ )
+
+ y = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+ mode="reflect",
+ )
+ y = y.squeeze(1)
+
+ spec = torch.stft(
+ y,
+ n_fft,
+ hop_length=hop_size,
+ win_length=win_size,
+ window=hann_window[wnsize_dtype_device],
+ center=center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=False,
+ )
+
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+ spec = spectral_normalize_torch(spec)
+
+ return spec
diff --git a/vits/utils.py b/vits/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcd2a496090d41d759340cacd3819372867214b9
--- /dev/null
+++ b/vits/utils.py
@@ -0,0 +1,52 @@
+import os
+import argparse
+import numpy as np
+import torch
+
+from scipy.io.wavfile import read
+from omegaconf import OmegaConf
+
+MATPLOTLIB_FLAG = False
+
+
+def load_wav_to_torch(full_path):
+ sampling_rate, data = read(full_path)
+ return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+f0_bin = 256
+f0_max = 1100.0
+f0_min = 50.0
+f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+
+
+def f0_to_coarse(f0):
+ is_torch = isinstance(f0, torch.Tensor)
+ f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * \
+ np.log(1 + f0 / 700)
+ f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * \
+ (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
+
+ f0_mel[f0_mel <= 1] = 1
+ f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+ f0_coarse = (
+ f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
+ assert f0_coarse.max() <= 255 and f0_coarse.min(
+ ) >= 1, (f0_coarse.max(), f0_coarse.min())
+ return f0_coarse
+
+
+def get_hparams(init=True):
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-c', '--config', type=str, default="./configs/base.yaml",
+ help='YAML file for configuration')
+ args = parser.parse_args()
+ hparams = OmegaConf.load(args.config)
+ model_dir = os.path.join("./logs", hparams.train.model)
+ if not os.path.exists(model_dir):
+ os.makedirs(model_dir)
+ config_save_path = os.path.join(model_dir, "config.json")
+ os.system(f"cp {args.config} {config_save_path}")
+ hparams.model_dir = model_dir
+ return hparams
diff --git a/vits_decoder/__init__.py b/vits_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/vits_decoder/alias/__init__.py b/vits_decoder/alias/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2318b63198250856809c0cb46210a4147b829bc
--- /dev/null
+++ b/vits_decoder/alias/__init__.py
@@ -0,0 +1,6 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+# LICENSE is in incl_licenses directory.
+
+from .filter import *
+from .resample import *
+from .act import *
\ No newline at end of file
diff --git a/vits_decoder/alias/act.py b/vits_decoder/alias/act.py
new file mode 100644
index 0000000000000000000000000000000000000000..028debd697dd60458aae75010057df038bd3518a
--- /dev/null
+++ b/vits_decoder/alias/act.py
@@ -0,0 +1,28 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+# LICENSE is in incl_licenses directory.
+
+import torch.nn as nn
+from .resample import UpSample1d, DownSample1d
+
+
+class Activation1d(nn.Module):
+ def __init__(self,
+ activation,
+ up_ratio: int = 2,
+ down_ratio: int = 2,
+ up_kernel_size: int = 12,
+ down_kernel_size: int = 12):
+ super().__init__()
+ self.up_ratio = up_ratio
+ self.down_ratio = down_ratio
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ # x: [B,C,T]
+ def forward(self, x):
+ x = self.upsample(x)
+ x = self.act(x)
+ x = self.downsample(x)
+
+ return x
\ No newline at end of file
diff --git a/vits_decoder/alias/activations.py b/vits_decoder/alias/activations.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/vits_decoder/alias/filter.py b/vits_decoder/alias/filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ad6ea87c1f10ddd94c544037791d7a4634d5ae1
--- /dev/null
+++ b/vits_decoder/alias/filter.py
@@ -0,0 +1,95 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+# LICENSE is in incl_licenses directory.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+if 'sinc' in dir(torch):
+ sinc = torch.sinc
+else:
+ # This code is adopted from adefossez's julius.core.sinc under the MIT License
+ # https://adefossez.github.io/julius/julius/core.html
+ # LICENSE is in incl_licenses directory.
+ def sinc(x: torch.Tensor):
+ """
+ Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+ __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+ """
+ return torch.where(x == 0,
+ torch.tensor(1., device=x.device, dtype=x.dtype),
+ torch.sin(math.pi * x) / math.pi / x)
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+# LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
+ even = (kernel_size % 2 == 0)
+ half_size = kernel_size // 2
+
+ #For kaiser window
+ delta_f = 4 * half_width
+ A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+ if A > 50.:
+ beta = 0.1102 * (A - 8.7)
+ elif A >= 21.:
+ beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
+ else:
+ beta = 0.
+ window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+ # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+ if even:
+ time = (torch.arange(-half_size, half_size) + 0.5)
+ else:
+ time = torch.arange(kernel_size) - half_size
+ if cutoff == 0:
+ filter_ = torch.zeros_like(time)
+ else:
+ filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+ # Normalize filter to have sum = 1, otherwise we will have a small leakage
+ # of the constant component in the input signal.
+ filter_ /= filter_.sum()
+ filter = filter_.view(1, 1, kernel_size)
+
+ return filter
+
+
+class LowPassFilter1d(nn.Module):
+ def __init__(self,
+ cutoff=0.5,
+ half_width=0.6,
+ stride: int = 1,
+ padding: bool = True,
+ padding_mode: str = 'replicate',
+ kernel_size: int = 12):
+ # kernel_size should be even number for stylegan3 setup,
+ # in this implementation, odd number is also possible.
+ super().__init__()
+ if cutoff < -0.:
+ raise ValueError("Minimum cutoff must be larger than zero.")
+ if cutoff > 0.5:
+ raise ValueError("A cutoff above 0.5 does not make sense.")
+ self.kernel_size = kernel_size
+ self.even = (kernel_size % 2 == 0)
+ self.pad_left = kernel_size // 2 - int(self.even)
+ self.pad_right = kernel_size // 2
+ self.stride = stride
+ self.padding = padding
+ self.padding_mode = padding_mode
+ filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+ self.register_buffer("filter", filter)
+
+ #input [B, C, T]
+ def forward(self, x):
+ _, C, _ = x.shape
+
+ if self.padding:
+ x = F.pad(x, (self.pad_left, self.pad_right),
+ mode=self.padding_mode)
+ out = F.conv1d(x, self.filter.expand(C, -1, -1),
+ stride=self.stride, groups=C)
+
+ return out
\ No newline at end of file
diff --git a/vits_decoder/alias/resample.py b/vits_decoder/alias/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..750e6c3402cc5ac939c4b9d075246562e0e1d1a7
--- /dev/null
+++ b/vits_decoder/alias/resample.py
@@ -0,0 +1,49 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+# LICENSE is in incl_licenses directory.
+
+import torch.nn as nn
+from torch.nn import functional as F
+from .filter import LowPassFilter1d
+from .filter import kaiser_sinc_filter1d
+
+
+class UpSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ self.stride = ratio
+ self.pad = self.kernel_size // ratio - 1
+ self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+ self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+ filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
+ half_width=0.6 / ratio,
+ kernel_size=self.kernel_size)
+ self.register_buffer("filter", filter)
+
+ # x: [B, C, T]
+ def forward(self, x):
+ _, C, _ = x.shape
+
+ x = F.pad(x, (self.pad, self.pad), mode='replicate')
+ x = self.ratio * F.conv_transpose1d(
+ x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+ x = x[..., self.pad_left:-self.pad_right]
+
+ return x
+
+
+class DownSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
+ half_width=0.6 / ratio,
+ stride=ratio,
+ kernel_size=self.kernel_size)
+
+ def forward(self, x):
+ xx = self.lowpass(x)
+
+ return xx
\ No newline at end of file
diff --git a/vits_decoder/bigv.py b/vits_decoder/bigv.py
new file mode 100644
index 0000000000000000000000000000000000000000..f55bb8bdc26cd4249512514c5eec4a05cda88f28
--- /dev/null
+++ b/vits_decoder/bigv.py
@@ -0,0 +1,131 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from torch import nn, sin, pow
+from torch.nn import Parameter
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+
+from .alias import *
+
+
+def init_weights(m, mean=0.0, std=0.01):
+ classname = m.__class__.__name__
+ if classname.find("Conv") != -1:
+ m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+ return int((kernel_size*dilation - dilation)/2)
+
+
+class SnakeBeta(nn.Module):
+ '''
+ A modified Snake function which uses separate parameters for the magnitude of the periodic components
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ References:
+ - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://arxiv.org/abs/2006.08195
+ Examples:
+ >>> a1 = snakebeta(256)
+ >>> x = torch.randn(256)
+ >>> x = a1(x)
+ '''
+
+ def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+ '''
+ Initialization.
+ INPUT:
+ - in_features: shape of the input
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ alpha is initialized to 1 by default, higher values = higher-frequency.
+ beta is initialized to 1 by default, higher values = higher-magnitude.
+ alpha will be trained along with the rest of your model.
+ '''
+ super(SnakeBeta, self).__init__()
+ self.in_features = in_features
+ # initialize alpha
+ self.alpha_logscale = alpha_logscale
+ if self.alpha_logscale: # log scale alphas initialized to zeros
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ self.beta = Parameter(torch.zeros(in_features) * alpha)
+ else: # linear scale alphas initialized to ones
+ self.alpha = Parameter(torch.ones(in_features) * alpha)
+ self.beta = Parameter(torch.ones(in_features) * alpha)
+ self.alpha.requires_grad = alpha_trainable
+ self.beta.requires_grad = alpha_trainable
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, x):
+ '''
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ SnakeBeta ∶= x + 1/b * sin^2 (xa)
+ '''
+ alpha = self.alpha.unsqueeze(
+ 0).unsqueeze(-1) # line up with x to [B, C, T]
+ beta = self.beta.unsqueeze(0).unsqueeze(-1)
+ if self.alpha_logscale:
+ alpha = torch.exp(alpha)
+ beta = torch.exp(beta)
+ x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+ return x
+
+
+class AMPBlock(torch.nn.Module):
+ def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+ super(AMPBlock, self).__init__()
+ self.h = h
+ self.convs1 = nn.ModuleList([
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+ padding=get_padding(kernel_size, dilation[0]))),
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+ padding=get_padding(kernel_size, dilation[1]))),
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+ padding=get_padding(kernel_size, dilation[2])))
+ ])
+ self.convs1.apply(init_weights)
+
+ self.convs2 = nn.ModuleList([
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+ padding=get_padding(kernel_size, 1))),
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+ padding=get_padding(kernel_size, 1))),
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+ padding=get_padding(kernel_size, 1)))
+ ])
+ self.convs2.apply(init_weights)
+
+ # total number of conv layers
+ self.num_layers = len(self.convs1) + len(self.convs2)
+
+ # periodic nonlinearity with snakebeta function and anti-aliasing
+ self.activations = nn.ModuleList([
+ Activation1d(
+ activation=SnakeBeta(channels, alpha_logscale=True))
+ for _ in range(self.num_layers)
+ ])
+
+ def forward(self, x):
+ acts1, acts2 = self.activations[::2], self.activations[1::2]
+ for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+ xt = a1(x)
+ xt = c1(xt)
+ xt = a2(xt)
+ xt = c2(xt)
+ x = xt + x
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.convs1:
+ remove_weight_norm(l)
+ for l in self.convs2:
+ remove_weight_norm(l)
\ No newline at end of file
diff --git a/vits_decoder/discriminator.py b/vits_decoder/discriminator.py
new file mode 100644
index 0000000000000000000000000000000000000000..576f8d0a3893d5c78b00d514961d6585298d0d86
--- /dev/null
+++ b/vits_decoder/discriminator.py
@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+
+from .mpd import MultiPeriodDiscriminator
+from .mrd import MultiResolutionDiscriminator
+from omegaconf import OmegaConf
+
+class Discriminator(nn.Module):
+ def __init__(self, hp):
+ super(Discriminator, self).__init__()
+ self.MRD = MultiResolutionDiscriminator(hp)
+ self.MPD = MultiPeriodDiscriminator(hp)
+
+ def forward(self, x):
+ return self.MRD(x), self.MPD(x)
+
+if __name__ == '__main__':
+ hp = OmegaConf.load('../config/default.yaml')
+ model = Discriminator(hp)
+
+ x = torch.randn(3, 1, 16384)
+ print(x.shape)
+
+ mrd_output, mpd_output = model(x)
+ for features, score in mpd_output:
+ for feat in features:
+ print(feat.shape)
+ print(score.shape)
+
+ pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ print(pytorch_total_params)
+
diff --git a/vits_decoder/generator.py b/vits_decoder/generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e122ce33c0af295ab6a683a10cdf6314ca2702f3
--- /dev/null
+++ b/vits_decoder/generator.py
@@ -0,0 +1,201 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+from torch.nn import Conv1d
+from torch.nn import ConvTranspose1d
+from torch.nn.utils import weight_norm
+from torch.nn.utils import remove_weight_norm
+
+from .nsf import SourceModuleHnNSF
+from .bigv import init_weights, SnakeBeta, AMPBlock
+from .alias import Activation1d
+
+
+class SpeakerAdapter(nn.Module):
+
+ def __init__(self,
+ speaker_dim,
+ adapter_dim,
+ epsilon=1e-5
+ ):
+ super(SpeakerAdapter, self).__init__()
+ self.speaker_dim = speaker_dim
+ self.adapter_dim = adapter_dim
+ self.epsilon = epsilon
+ self.W_scale = nn.Linear(self.speaker_dim, self.adapter_dim)
+ self.W_bias = nn.Linear(self.speaker_dim, self.adapter_dim)
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ torch.nn.init.constant_(self.W_scale.weight, 0.0)
+ torch.nn.init.constant_(self.W_scale.bias, 1.0)
+ torch.nn.init.constant_(self.W_bias.weight, 0.0)
+ torch.nn.init.constant_(self.W_bias.bias, 0.0)
+
+ def forward(self, x, speaker_embedding):
+ x = x.transpose(1, -1)
+ mean = x.mean(dim=-1, keepdim=True)
+ var = ((x - mean) ** 2).mean(dim=-1, keepdim=True)
+ std = (var + self.epsilon).sqrt()
+ y = (x - mean) / std
+ scale = self.W_scale(speaker_embedding)
+ bias = self.W_bias(speaker_embedding)
+ y *= scale.unsqueeze(1)
+ y += bias.unsqueeze(1)
+ y = y.transpose(1, -1)
+ return y
+
+
+class Generator(torch.nn.Module):
+ # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+ def __init__(self, hp):
+ super(Generator, self).__init__()
+ self.hp = hp
+ self.num_kernels = len(hp.gen.resblock_kernel_sizes)
+ self.num_upsamples = len(hp.gen.upsample_rates)
+ # speaker adaper, 256 should change by what speaker encoder you use
+ self.adapter = SpeakerAdapter(hp.vits.spk_dim, hp.gen.upsample_input)
+ # pre conv
+ self.conv_pre = nn.utils.weight_norm(
+ Conv1d(hp.gen.upsample_input, hp.gen.upsample_initial_channel, 7, 1, padding=3))
+ # nsf
+ self.f0_upsamp = torch.nn.Upsample(
+ scale_factor=np.prod(hp.gen.upsample_rates))
+ self.m_source = SourceModuleHnNSF()
+ self.noise_convs = nn.ModuleList()
+ # transposed conv-based upsamplers. does not apply anti-aliasing
+ self.ups = nn.ModuleList()
+ for i, (u, k) in enumerate(zip(hp.gen.upsample_rates, hp.gen.upsample_kernel_sizes)):
+ # print(f'ups: {i} {k}, {u}, {(k - u) // 2}')
+ # base
+ self.ups.append(nn.ModuleList([
+ weight_norm(ConvTranspose1d(hp.gen.upsample_initial_channel // (2 ** i),
+ hp.gen.upsample_initial_channel // (
+ 2 ** (i + 1)),
+ k, u, padding=(k - u) // 2))
+ ]))
+ # nsf
+ if i + 1 < len(hp.gen.upsample_rates):
+ stride_f0 = np.prod(hp.gen.upsample_rates[i + 1:])
+ stride_f0 = int(stride_f0)
+ self.noise_convs.append(
+ Conv1d(
+ 1,
+ hp.gen.upsample_initial_channel // (2 ** (i + 1)),
+ kernel_size=stride_f0 * 2,
+ stride=stride_f0,
+ padding=stride_f0 // 2,
+ )
+ )
+ else:
+ self.noise_convs.append(
+ Conv1d(1, hp.gen.upsample_initial_channel //
+ (2 ** (i + 1)), kernel_size=1)
+ )
+
+ # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+ self.resblocks = nn.ModuleList()
+ for i in range(len(self.ups)):
+ ch = hp.gen.upsample_initial_channel // (2 ** (i + 1))
+ for k, d in zip(hp.gen.resblock_kernel_sizes, hp.gen.resblock_dilation_sizes):
+ self.resblocks.append(AMPBlock(hp, ch, k, d))
+
+ # post conv
+ activation_post = SnakeBeta(ch, alpha_logscale=True)
+ self.activation_post = Activation1d(activation=activation_post)
+ self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+
+ # weight initialization
+ for i in range(len(self.ups)):
+ self.ups[i].apply(init_weights)
+ self.conv_post.apply(init_weights)
+
+ def forward(self, spk, x, f0):
+ # adapter
+ x = self.adapter(x, spk)
+ # nsf
+ f0 = f0[:, None]
+ f0 = self.f0_upsamp(f0).transpose(1, 2)
+ har_source = self.m_source(f0)
+ har_source = har_source.transpose(1, 2)
+ x = self.conv_pre(x)
+
+ for i in range(self.num_upsamples):
+ # upsampling
+ for i_up in range(len(self.ups[i])):
+ x = self.ups[i][i_up](x)
+ # nsf
+ x_source = self.noise_convs[i](har_source)
+ x = x + x_source
+ # AMP blocks
+ xs = None
+ for j in range(self.num_kernels):
+ if xs is None:
+ xs = self.resblocks[i * self.num_kernels + j](x)
+ else:
+ xs += self.resblocks[i * self.num_kernels + j](x)
+ x = xs / self.num_kernels
+
+ # post conv
+ x = self.activation_post(x)
+ x = self.conv_post(x)
+ x = torch.tanh(x)
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.ups:
+ for l_i in l:
+ remove_weight_norm(l_i)
+ for l in self.resblocks:
+ l.remove_weight_norm()
+ remove_weight_norm(self.conv_pre)
+ remove_weight_norm(self.conv_post)
+
+ def eval(self, inference=False):
+ super(Generator, self).eval()
+ # don't remove weight norm while validation in training loop
+ if inference:
+ self.remove_weight_norm()
+
+ def pitch2source(self, f0):
+ f0 = f0[:, None]
+ f0 = self.f0_upsamp(f0).transpose(1, 2) # [1,len,1]
+ har_source = self.m_source(f0)
+ har_source = har_source.transpose(1, 2) # [1,1,len]
+ return har_source
+
+ def source2wav(self, audio):
+ MAX_WAV_VALUE = 32768.0
+ audio = audio.squeeze()
+ audio = MAX_WAV_VALUE * audio
+ audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE-1)
+ audio = audio.short()
+ return audio.cpu().detach().numpy()
+
+ def inference(self, spk, x, har_source):
+ # adapter
+ x = self.adapter(x, spk)
+ x = self.conv_pre(x)
+
+ for i in range(self.num_upsamples):
+ # upsampling
+ for i_up in range(len(self.ups[i])):
+ x = self.ups[i][i_up](x)
+ # nsf
+ x_source = self.noise_convs[i](har_source)
+ x = x + x_source
+ # AMP blocks
+ xs = None
+ for j in range(self.num_kernels):
+ if xs is None:
+ xs = self.resblocks[i * self.num_kernels + j](x)
+ else:
+ xs += self.resblocks[i * self.num_kernels + j](x)
+ x = xs / self.num_kernels
+
+ # post conv
+ x = self.activation_post(x)
+ x = self.conv_post(x)
+ x = torch.tanh(x)
+ return x
diff --git a/vits_decoder/mpd.py b/vits_decoder/mpd.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dc63e859dd2920f9d02b285ebc4dae8cf318d6a
--- /dev/null
+++ b/vits_decoder/mpd.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm, spectral_norm
+
+class DiscriminatorP(nn.Module):
+ def __init__(self, hp, period):
+ super(DiscriminatorP, self).__init__()
+
+ self.LRELU_SLOPE = hp.mpd.lReLU_slope
+ self.period = period
+
+ kernel_size = hp.mpd.kernel_size
+ stride = hp.mpd.stride
+ norm_f = weight_norm if hp.mpd.use_spectral_norm == False else spectral_norm
+
+ self.convs = nn.ModuleList([
+ norm_f(nn.Conv2d(1, 64, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
+ norm_f(nn.Conv2d(64, 128, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
+ norm_f(nn.Conv2d(128, 256, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
+ norm_f(nn.Conv2d(256, 512, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
+ norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), 1, padding=(kernel_size // 2, 0))),
+ ])
+ self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+ def forward(self, x):
+ fmap = []
+
+ # 1d to 2d
+ b, c, t = x.shape
+ if t % self.period != 0: # pad first
+ n_pad = self.period - (t % self.period)
+ x = F.pad(x, (0, n_pad), "reflect")
+ t = t + n_pad
+ x = x.view(b, c, t // self.period, self.period)
+
+ for l in self.convs:
+ x = l(x)
+ x = F.leaky_relu(x, self.LRELU_SLOPE)
+ fmap.append(x)
+ x = self.conv_post(x)
+ fmap.append(x)
+ x = torch.flatten(x, 1, -1)
+
+ return fmap, x
+
+
+class MultiPeriodDiscriminator(nn.Module):
+ def __init__(self, hp):
+ super(MultiPeriodDiscriminator, self).__init__()
+
+ self.discriminators = nn.ModuleList(
+ [DiscriminatorP(hp, period) for period in hp.mpd.periods]
+ )
+
+ def forward(self, x):
+ ret = list()
+ for disc in self.discriminators:
+ ret.append(disc(x))
+
+ return ret # [(feat, score), (feat, score), (feat, score), (feat, score), (feat, score)]
diff --git a/vits_decoder/mrd.py b/vits_decoder/mrd.py
new file mode 100644
index 0000000000000000000000000000000000000000..da6db1a416366603d2e65b400d66c44262e2baef
--- /dev/null
+++ b/vits_decoder/mrd.py
@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm, spectral_norm
+
+class DiscriminatorR(torch.nn.Module):
+ def __init__(self, hp, resolution):
+ super(DiscriminatorR, self).__init__()
+
+ self.resolution = resolution
+ self.LRELU_SLOPE = hp.mpd.lReLU_slope
+
+ norm_f = weight_norm if hp.mrd.use_spectral_norm == False else spectral_norm
+
+ self.convs = nn.ModuleList([
+ norm_f(nn.Conv2d(1, 32, (3, 9), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))),
+ ])
+ self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
+
+ def forward(self, x):
+ fmap = []
+
+ x = self.spectrogram(x)
+ x = x.unsqueeze(1)
+ for l in self.convs:
+ x = l(x)
+ x = F.leaky_relu(x, self.LRELU_SLOPE)
+ fmap.append(x)
+ x = self.conv_post(x)
+ fmap.append(x)
+ x = torch.flatten(x, 1, -1)
+
+ return fmap, x
+
+ def spectrogram(self, x):
+ n_fft, hop_length, win_length = self.resolution
+ x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
+ x = x.squeeze(1)
+ x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=False) #[B, F, TT, 2]
+ mag = torch.norm(x, p=2, dim =-1) #[B, F, TT]
+
+ return mag
+
+
+class MultiResolutionDiscriminator(torch.nn.Module):
+ def __init__(self, hp):
+ super(MultiResolutionDiscriminator, self).__init__()
+ self.resolutions = eval(hp.mrd.resolutions)
+ self.discriminators = nn.ModuleList(
+ [DiscriminatorR(hp, resolution) for resolution in self.resolutions]
+ )
+
+ def forward(self, x):
+ ret = list()
+ for disc in self.discriminators:
+ ret.append(disc(x))
+
+ return ret # [(feat, score), (feat, score), (feat, score)]
diff --git a/vits_decoder/nsf.py b/vits_decoder/nsf.py
new file mode 100644
index 0000000000000000000000000000000000000000..88b9c22f9d296a869ac6e94618910c3264d006fd
--- /dev/null
+++ b/vits_decoder/nsf.py
@@ -0,0 +1,409 @@
+import torch
+import numpy as np
+import sys
+import torch.nn.functional as torch_nn_func
+
+
+class PulseGen(torch.nn.Module):
+ """Definition of Pulse train generator
+
+ There are many ways to implement pulse generator.
+ Here, PulseGen is based on SinGen. For a perfect
+ """
+
+ def __init__(self, samp_rate, pulse_amp=0.1, noise_std=0.003, voiced_threshold=0):
+ super(PulseGen, self).__init__()
+ self.pulse_amp = pulse_amp
+ self.sampling_rate = samp_rate
+ self.voiced_threshold = voiced_threshold
+ self.noise_std = noise_std
+ self.l_sinegen = SineGen(
+ self.sampling_rate,
+ harmonic_num=0,
+ sine_amp=self.pulse_amp,
+ noise_std=0,
+ voiced_threshold=self.voiced_threshold,
+ flag_for_pulse=True,
+ )
+
+ def forward(self, f0):
+ """Pulse train generator
+ pulse_train, uv = forward(f0)
+ input F0: tensor(batchsize=1, length, dim=1)
+ f0 for unvoiced steps should be 0
+ output pulse_train: tensor(batchsize=1, length, dim)
+ output uv: tensor(batchsize=1, length, 1)
+
+ Note: self.l_sine doesn't make sure that the initial phase of
+ a voiced segment is np.pi, the first pulse in a voiced segment
+ may not be at the first time step within a voiced segment
+ """
+ with torch.no_grad():
+ sine_wav, uv, noise = self.l_sinegen(f0)
+
+ # sine without additive noise
+ pure_sine = sine_wav - noise
+
+ # step t corresponds to a pulse if
+ # sine[t] > sine[t+1] & sine[t] > sine[t-1]
+ # & sine[t-1], sine[t+1], and sine[t] are voiced
+ # or
+ # sine[t] is voiced, sine[t-1] is unvoiced
+ # we use torch.roll to simulate sine[t+1] and sine[t-1]
+ sine_1 = torch.roll(pure_sine, shifts=1, dims=1)
+ uv_1 = torch.roll(uv, shifts=1, dims=1)
+ uv_1[:, 0, :] = 0
+ sine_2 = torch.roll(pure_sine, shifts=-1, dims=1)
+ uv_2 = torch.roll(uv, shifts=-1, dims=1)
+ uv_2[:, -1, :] = 0
+
+ loc = (pure_sine > sine_1) * (pure_sine > sine_2) \
+ * (uv_1 > 0) * (uv_2 > 0) * (uv > 0) \
+ + (uv_1 < 1) * (uv > 0)
+
+ # pulse train without noise
+ pulse_train = pure_sine * loc
+
+ # additive noise to pulse train
+ # note that noise from sinegen is zero in voiced regions
+ pulse_noise = torch.randn_like(pure_sine) * self.noise_std
+
+ # with additive noise on pulse, and unvoiced regions
+ pulse_train += pulse_noise * loc + pulse_noise * (1 - uv)
+ return pulse_train, sine_wav, uv, pulse_noise
+
+
+class SignalsConv1d(torch.nn.Module):
+ """Filtering input signal with time invariant filter
+ Note: FIRFilter conducted filtering given fixed FIR weight
+ SignalsConv1d convolves two signals
+ Note: this is based on torch.nn.functional.conv1d
+
+ """
+
+ def __init__(self):
+ super(SignalsConv1d, self).__init__()
+
+ def forward(self, signal, system_ir):
+ """output = forward(signal, system_ir)
+
+ signal: (batchsize, length1, dim)
+ system_ir: (length2, dim)
+
+ output: (batchsize, length1, dim)
+ """
+ if signal.shape[-1] != system_ir.shape[-1]:
+ print("Error: SignalsConv1d expects shape:")
+ print("signal (batchsize, length1, dim)")
+ print("system_id (batchsize, length2, dim)")
+ print("But received signal: {:s}".format(str(signal.shape)))
+ print(" system_ir: {:s}".format(str(system_ir.shape)))
+ sys.exit(1)
+ padding_length = system_ir.shape[0] - 1
+ groups = signal.shape[-1]
+
+ # pad signal on the left
+ signal_pad = torch_nn_func.pad(signal.permute(0, 2, 1), (padding_length, 0))
+ # prepare system impulse response as (dim, 1, length2)
+ # also flip the impulse response
+ ir = torch.flip(system_ir.unsqueeze(1).permute(2, 1, 0), dims=[2])
+ # convolute
+ output = torch_nn_func.conv1d(signal_pad, ir, groups=groups)
+ return output.permute(0, 2, 1)
+
+
+class CyclicNoiseGen_v1(torch.nn.Module):
+ """CyclicnoiseGen_v1
+ Cyclic noise with a single parameter of beta.
+ Pytorch v1 implementation assumes f_t is also fixed
+ """
+
+ def __init__(self, samp_rate, noise_std=0.003, voiced_threshold=0):
+ super(CyclicNoiseGen_v1, self).__init__()
+ self.samp_rate = samp_rate
+ self.noise_std = noise_std
+ self.voiced_threshold = voiced_threshold
+
+ self.l_pulse = PulseGen(
+ samp_rate,
+ pulse_amp=1.0,
+ noise_std=noise_std,
+ voiced_threshold=voiced_threshold,
+ )
+ self.l_conv = SignalsConv1d()
+
+ def noise_decay(self, beta, f0mean):
+ """decayed_noise = noise_decay(beta, f0mean)
+ decayed_noise = n[t]exp(-t * f_mean / beta / samp_rate)
+
+ beta: (dim=1) or (batchsize=1, 1, dim=1)
+ f0mean (batchsize=1, 1, dim=1)
+
+ decayed_noise (batchsize=1, length, dim=1)
+ """
+ with torch.no_grad():
+ # exp(-1.0 n / T) < 0.01 => n > -log(0.01)*T = 4.60*T
+ # truncate the noise when decayed by -40 dB
+ length = 4.6 * self.samp_rate / f0mean
+ length = length.int()
+ time_idx = torch.arange(0, length, device=beta.device)
+ time_idx = time_idx.unsqueeze(0).unsqueeze(2)
+ time_idx = time_idx.repeat(beta.shape[0], 1, beta.shape[2])
+
+ noise = torch.randn(time_idx.shape, device=beta.device)
+
+ # due to Pytorch implementation, use f0_mean as the f0 factor
+ decay = torch.exp(-time_idx * f0mean / beta / self.samp_rate)
+ return noise * self.noise_std * decay
+
+ def forward(self, f0s, beta):
+ """Producde cyclic-noise"""
+ # pulse train
+ pulse_train, sine_wav, uv, noise = self.l_pulse(f0s)
+ pure_pulse = pulse_train - noise
+
+ # decayed_noise (length, dim=1)
+ if (uv < 1).all():
+ # all unvoiced
+ cyc_noise = torch.zeros_like(sine_wav)
+ else:
+ f0mean = f0s[uv > 0].mean()
+
+ decayed_noise = self.noise_decay(beta, f0mean)[0, :, :]
+ # convolute
+ cyc_noise = self.l_conv(pure_pulse, decayed_noise)
+
+ # add noise in invoiced segments
+ cyc_noise = cyc_noise + noise * (1.0 - uv)
+ return cyc_noise, pulse_train, sine_wav, uv, noise
+
+
+class SineGen(torch.nn.Module):
+ """Definition of sine generator
+ SineGen(samp_rate, harmonic_num = 0,
+ sine_amp = 0.1, noise_std = 0.003,
+ voiced_threshold = 0,
+ flag_for_pulse=False)
+
+ samp_rate: sampling rate in Hz
+ harmonic_num: number of harmonic overtones (default 0)
+ sine_amp: amplitude of sine-wavefrom (default 0.1)
+ noise_std: std of Gaussian noise (default 0.003)
+ voiced_thoreshold: F0 threshold for U/V classification (default 0)
+ flag_for_pulse: this SinGen is used inside PulseGen (default False)
+
+ Note: when flag_for_pulse is True, the first time step of a voiced
+ segment is always sin(np.pi) or cos(0)
+ """
+
+ def __init__(
+ self,
+ samp_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ noise_std=0.003,
+ voiced_threshold=0,
+ flag_for_pulse=False,
+ ):
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = samp_rate
+ self.voiced_threshold = voiced_threshold
+ self.flag_for_pulse = flag_for_pulse
+
+ def _f02uv(self, f0):
+ # generate uv signal
+ uv = torch.ones_like(f0)
+ uv = uv * (f0 > self.voiced_threshold)
+ return uv
+
+ def _f02sine(self, f0_values):
+ """f0_values: (batchsize, length, dim)
+ where dim indicates fundamental tone and overtones
+ """
+ # convert to F0 in rad. The interger part n can be ignored
+ # because 2 * np.pi * n doesn't affect phase
+ rad_values = (f0_values / self.sampling_rate) % 1
+
+ # initial phase noise (no noise for fundamental component)
+ rand_ini = torch.rand(
+ f0_values.shape[0], f0_values.shape[2], device=f0_values.device
+ )
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+ # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+ if not self.flag_for_pulse:
+ # for normal case
+
+ # To prevent torch.cumsum numerical overflow,
+ # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+ # Buffer tmp_over_one_idx indicates the time step to add -1.
+ # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+ tmp_over_one = torch.cumsum(rad_values, 1) % 1
+ tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+ cumsum_shift = torch.zeros_like(rad_values)
+ cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+ sines = torch.sin(
+ torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
+ )
+ else:
+ # If necessary, make sure that the first time step of every
+ # voiced segments is sin(pi) or cos(0)
+ # This is used for pulse-train generation
+
+ # identify the last time step in unvoiced segments
+ uv = self._f02uv(f0_values)
+ uv_1 = torch.roll(uv, shifts=-1, dims=1)
+ uv_1[:, -1, :] = 1
+ u_loc = (uv < 1) * (uv_1 > 0)
+
+ # get the instantanouse phase
+ tmp_cumsum = torch.cumsum(rad_values, dim=1)
+ # different batch needs to be processed differently
+ for idx in range(f0_values.shape[0]):
+ temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+ temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+ # stores the accumulation of i.phase within
+ # each voiced segments
+ tmp_cumsum[idx, :, :] = 0
+ tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+ # rad_values - tmp_cumsum: remove the accumulation of i.phase
+ # within the previous voiced segment.
+ i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+ # get the sines
+ sines = torch.cos(i_phase * 2 * np.pi)
+ return sines
+
+ def forward(self, f0):
+ """sine_tensor, uv = forward(f0)
+ input F0: tensor(batchsize=1, length, dim=1)
+ f0 for unvoiced steps should be 0
+ output sine_tensor: tensor(batchsize=1, length, dim)
+ output uv: tensor(batchsize=1, length, 1)
+ """
+ with torch.no_grad():
+ f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
+ # fundamental component
+ f0_buf[:, :, 0] = f0[:, :, 0]
+ for idx in np.arange(self.harmonic_num):
+ # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+ f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+ # generate sine waveforms
+ sine_waves = self._f02sine(f0_buf) * self.sine_amp
+
+ # generate uv signal
+ # uv = torch.ones(f0.shape)
+ # uv = uv * (f0 > self.voiced_threshold)
+ uv = self._f02uv(f0)
+
+ # noise: for unvoiced should be similar to sine_amp
+ # std = self.sine_amp/3 -> max value ~ self.sine_amp
+ # . for voiced regions is self.noise_std
+ noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+ noise = noise_amp * torch.randn_like(sine_waves)
+
+ # first: set the unvoiced part to 0 by uv
+ # then: additive noise
+ sine_waves = sine_waves * uv + noise
+ return sine_waves
+
+
+class SourceModuleCycNoise_v1(torch.nn.Module):
+ """SourceModuleCycNoise_v1
+ SourceModule(sampling_rate, noise_std=0.003, voiced_threshod=0)
+ sampling_rate: sampling_rate in Hz
+
+ noise_std: std of Gaussian noise (default: 0.003)
+ voiced_threshold: threshold to set U/V given F0 (default: 0)
+
+ cyc, noise, uv = SourceModuleCycNoise_v1(F0_upsampled, beta)
+ F0_upsampled (batchsize, length, 1)
+ beta (1)
+ cyc (batchsize, length, 1)
+ noise (batchsize, length, 1)
+ uv (batchsize, length, 1)
+ """
+
+ def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0):
+ super(SourceModuleCycNoise_v1, self).__init__()
+ self.sampling_rate = sampling_rate
+ self.noise_std = noise_std
+ self.l_cyc_gen = CyclicNoiseGen_v1(sampling_rate, noise_std, voiced_threshod)
+
+ def forward(self, f0_upsamped, beta):
+ """
+ cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta)
+ F0_upsampled (batchsize, length, 1)
+ beta (1)
+ cyc (batchsize, length, 1)
+ noise (batchsize, length, 1)
+ uv (batchsize, length, 1)
+ """
+ # source for harmonic branch
+ cyc, pulse, sine, uv, add_noi = self.l_cyc_gen(f0_upsamped, beta)
+
+ # source for noise branch, in the same shape as uv
+ noise = torch.randn_like(uv) * self.noise_std / 3
+ return cyc, noise, uv
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+ """SourceModule for hn-nsf
+ SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+ add_noise_std=0.003, voiced_threshod=0)
+ sampling_rate: sampling_rate in Hz
+ harmonic_num: number of harmonic above F0 (default: 0)
+ sine_amp: amplitude of sine source signal (default: 0.1)
+ add_noise_std: std of additive Gaussian noise (default: 0.003)
+ note that amplitude of noise in unvoiced is decided
+ by sine_amp
+ voiced_threshold: threhold to set U/V given F0 (default: 0)
+
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ uv (batchsize, length, 1)
+ """
+
+ def __init__(
+ self,
+ sampling_rate=48000,
+ harmonic_num=10,
+ sine_amp=0.1,
+ add_noise_std=0.003,
+ voiced_threshod=0,
+ ):
+ super(SourceModuleHnNSF, self).__init__()
+
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+
+ # to produce sine waveforms
+ self.l_sin_gen = SineGen(
+ sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
+ )
+
+ # to merge source harmonics into a single excitation
+ self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = torch.nn.Tanh()
+
+ def forward(self, x):
+ """
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ """
+ # source for harmonic branch
+ sine_wavs = self.l_sin_gen(x)
+ sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+ return sine_merge
diff --git a/vits_pretrain/README.md b/vits_pretrain/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5e13d08f9a9c3fbc1d21e06e760abb0dce647ef0
--- /dev/null
+++ b/vits_pretrain/README.md
@@ -0,0 +1,3 @@
+Path for:
+
+ vits_pretrain.pt
\ No newline at end of file
diff --git a/vits_pretrain/sovits5.0-48k-debug.pth b/vits_pretrain/sovits5.0-48k-debug.pth
new file mode 100644
index 0000000000000000000000000000000000000000..b00531e1557b5e6141159b04ef9294ffcf0de56a
--- /dev/null
+++ b/vits_pretrain/sovits5.0-48k-debug.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2c3e49cebb2968266659507c80a007375f49d616ee216cf084cb3f87e93083d
+size 67866609
diff --git a/whisper/LICENSE b/whisper/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d25552598bb9c5400612159ed4bab92ce12a5ce5
--- /dev/null
+++ b/whisper/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 OpenAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/whisper/README.md b/whisper/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9ea3a38e58aa56be82a79e31461849083917babb
--- /dev/null
+++ b/whisper/README.md
@@ -0,0 +1,147 @@
+# Whisper
+
+[[Blog]](https://openai.com/blog/whisper)
+[[Paper]](https://arxiv.org/abs/2212.04356)
+[[Model card]](https://github.com/openai/whisper/blob/main/model-card.md)
+[[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)
+
+Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
+
+
+## Approach
+
+
+
+A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
+
+
+## Setup
+
+We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command:
+
+ pip install -U openai-whisper
+
+Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:
+
+ pip install git+https://github.com/openai/whisper.git
+
+To update the package to the latest version of this repository, please run:
+
+ pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
+
+It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:
+
+```bash
+# on Ubuntu or Debian
+sudo apt update && sudo apt install ffmpeg
+
+# on Arch Linux
+sudo pacman -S ffmpeg
+
+# on MacOS using Homebrew (https://brew.sh/)
+brew install ffmpeg
+
+# on Windows using Chocolatey (https://chocolatey.org/)
+choco install ffmpeg
+
+# on Windows using Scoop (https://scoop.sh/)
+scoop install ffmpeg
+```
+
+You may need [`rust`](http://rust-lang.org) installed as well, in case [tokenizers](https://pypi.org/project/tokenizers/) does not provide a pre-built wheel for your platform. If you see installation errors during the `pip install` command above, please follow the [Getting started page](https://www.rust-lang.org/learn/get-started) to install Rust development environment. Additionally, you may need to configure the `PATH` environment variable, e.g. `export PATH="$HOME/.cargo/bin:$PATH"`. If the installation fails with `No module named 'setuptools_rust'`, you need to install `setuptools_rust`, e.g. by running:
+
+```bash
+pip install setuptools-rust
+```
+
+
+## Available models and languages
+
+There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.
+
+
+| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
+|:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|
+| tiny | 39 M | `tiny.en` | `tiny` | ~1 GB | ~32x |
+| base | 74 M | `base.en` | `base` | ~1 GB | ~16x |
+| small | 244 M | `small.en` | `small` | ~2 GB | ~6x |
+| medium | 769 M | `medium.en` | `medium` | ~5 GB | ~2x |
+| large | 1550 M | N/A | `large` | ~10 GB | 1x |
+
+The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
+
+Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller, the better.
+
+
+
+
+
+## Command-line usage
+
+The following command will transcribe speech in audio files, using the `medium` model:
+
+ whisper audio.flac audio.mp3 audio.wav --model medium
+
+The default setting (which selects the `small` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
+
+ whisper japanese.wav --language Japanese
+
+Adding `--task translate` will translate the speech into English:
+
+ whisper japanese.wav --language Japanese --task translate
+
+Run the following to view all available options:
+
+ whisper --help
+
+See [tokenizer.py](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.
+
+
+## Python usage
+
+Transcription can also be performed within Python:
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+result = model.transcribe("audio.mp3")
+print(result["text"])
+```
+
+Internally, the `transcribe()` method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.
+
+Below is an example usage of `whisper.detect_language()` and `whisper.decode()` which provide lower-level access to the model.
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+
+# load audio and pad/trim it to fit 30 seconds
+audio = whisper.load_audio("audio.mp3")
+audio = whisper.pad_or_trim(audio)
+
+# make log-Mel spectrogram and move to the same device as the model
+mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+# detect the spoken language
+_, probs = model.detect_language(mel)
+print(f"Detected language: {max(probs, key=probs.get)}")
+
+# decode the audio
+options = whisper.DecodingOptions()
+result = whisper.decode(model, mel, options)
+
+# print the recognized text
+print(result.text)
+```
+
+## More examples
+
+Please use the [🙌 Show and tell](https://github.com/openai/whisper/discussions/categories/show-and-tell) category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.
+
+
+## License
+
+Whisper's code and model weights are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details.
\ No newline at end of file
diff --git a/whisper/__init__.py b/whisper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/whisper/assets/gpt2/merges.txt b/whisper/assets/gpt2/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956
--- /dev/null
+++ b/whisper/assets/gpt2/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
+Ġ a
+h e
+i n
+r e
+o n
+Ġt he
+e r
+Ġ s
+a t
+Ġ w
+Ġ o
+e n
+Ġ c
+i t
+i s
+a n
+o r
+e s
+Ġ b
+e d
+Ġ f
+in g
+Ġ p
+o u
+Ġa n
+a l
+a r
+Ġt o
+Ġ m
+Ġo f
+Ġ in
+Ġ d
+Ġ h
+Ġan d
+i c
+a s
+l e
+Ġt h
+i on
+o m
+l l
+en t
+Ġ n
+Ġ l
+s t
+Ġ re
+v e
+Ġ e
+r o
+l y
+Ġb e
+Ġ g
+Ġ T
+c t
+Ġ S
+i d
+o t
+Ġ I
+u t
+e t
+Ġ A
+Ġ is
+Ġ on
+i m
+a m
+o w
+a y
+a d
+s e
+Ġth at
+Ġ C
+i g
+Ġf or
+a c
+Ġ y
+v er
+u r
+Ġ u
+l d
+Ġs t
+Ġ M
+' s
+Ġ he
+Ġ it
+at ion
+it h
+i r
+c e
+Ġy ou
+i l
+Ġ B
+Ġw h
+o l
+Ġ P
+Ġw ith
+Ġ 1
+t er
+c h
+Ġa s
+Ġw e
+Ġ (
+n d
+i ll
+Ġ D
+i f
+Ġ 2
+a g
+er s
+k e
+Ġ "
+Ġ H
+e m
+Ġc on
+Ġ W
+Ġ R
+he r
+Ġw as
+Ġ r
+o d
+Ġ F
+u l
+at e
+Ġa t
+r i
+p p
+o re
+ĠT he
+Ġs e
+u s
+Ġp ro
+Ġh a
+u m
+Ġa re
+Ġd e
+a in
+an d
+Ġo r
+ig h
+es t
+is t
+a b
+r om
+Ġ N
+t h
+Ġc om
+Ġ G
+u n
+o p
+0 0
+Ġ L
+Ġn ot
+es s
+Ġe x
+Ġ v
+re s
+Ġ E
+e w
+it y
+an t
+Ġb y
+e l
+o s
+or t
+o c
+q u
+Ġf rom
+Ġha ve
+Ġs u
+i ve
+ou ld
+Ġs h
+Ġth is
+n t
+r a
+p e
+igh t
+ar t
+m ent
+Ġa l
+u st
+en d
+- -
+al l
+Ġ O
+ac k
+Ġc h
+Ġ le
+i es
+re d
+ar d
+â Ģ
+ou t
+Ġ J
+Ġa b
+e ar
+i v
+al ly
+ou r
+o st
+g h
+p t
+Ġp l
+as t
+Ġc an
+a k
+om e
+u d
+T he
+Ġh is
+Ġd o
+Ġg o
+Ġh as
+g e
+' t
+Ġ U
+r ou
+Ġs a
+Ġ j
+Ġb ut
+Ġw or
+Ġa ll
+e ct
+Ġ k
+am e
+Ġw ill
+o k
+Ġw he
+Ġthe y
+id e
+0 1
+f f
+ic h
+p l
+t her
+Ġt r
+. .
+Ġin t
+i e
+u re
+ag e
+Ġn e
+i al
+a p
+in e
+ic e
+Ġm e
+Ġo ut
+an s
+on e
+on g
+ion s
+Ġwh o
+Ġ K
+Ġu p
+Ġthe ir
+Ġa d
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diff --git a/whisper/assets/gpt2/special_tokens_map.json b/whisper/assets/gpt2/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546
--- /dev/null
+++ b/whisper/assets/gpt2/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/whisper/assets/gpt2/tokenizer_config.json b/whisper/assets/gpt2/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c92208ce39324aadabc239d73dd9bb3ec1cd45ca
--- /dev/null
+++ b/whisper/assets/gpt2/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/whisper/assets/gpt2/vocab.json b/whisper/assets/gpt2/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..84ef7fb594b5c0979e48bdeddb60a0adef33df0b
--- /dev/null
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diff --git a/whisper/assets/multilingual/merges.txt b/whisper/assets/multilingual/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..afde9f4fa8bcf6d4762c5780977825666dc49eac
--- /dev/null
+++ b/whisper/assets/multilingual/merges.txt
@@ -0,0 +1,50000 @@
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diff --git a/whisper/assets/multilingual/special_tokens_map.json b/whisper/assets/multilingual/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546
--- /dev/null
+++ b/whisper/assets/multilingual/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/whisper/assets/multilingual/tokenizer_config.json b/whisper/assets/multilingual/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0235ccff90efbc022fbe30360220375c5bad68da
--- /dev/null
+++ b/whisper/assets/multilingual/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/whisper/assets/multilingual/vocab.json b/whisper/assets/multilingual/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..406c3f2600089389bd8bf3920c2fa3770d11789a
--- /dev/null
+++ b/whisper/assets/multilingual/vocab.json
@@ -0,0 +1 @@
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"ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 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"Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 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20630, "Ġhonour": 20631, "orig": 20632, "INA": 20633, "ĠNathan": 20634, "ва": 20635, "ĠÑģказаÑĤÑĮ": 20636, "tır": 20637, "Ġexclusively": 20638, "Ġshades": 20639, "ĠпÑĢоÑĨ": 20640, "Ġoccasions": 20641, "ija": 20642, "çļĦæĻĤåĢĻ": 20643, "åݲ": 20644, "æħ¢": 20645, "fig": 20646, "Ġtus": 20647, "Ġremem": 20648, "ĠChristopher": 20649, "Ġslime": 20650, "Ġalguna": 20651, "ĠFortunately": 20652, "Ġlors": 20653, "voll": 20654, "aver": 20655, "Ġoutlet": 20656, "ĠLinkedIn": 20657, "ĠExecutive": 20658, "Ġorgans": 20659, "ĠBegin": 20660, "ĠíĻĶ": 20661, "Ġtransplant": 20662, "ragen": 20663, "VO": 20664, "ĠFör": 20665, "ĠباÙĦ": 20666, "ĠAndre": 20667, "isine": 20668, "Ġlasts": 20669, "Ġhistória": 20670, "Ġluz": 20671, "Ġcollar": 20672, "Ġkidna": 20673, "Ġoptical": 20674, "iov": 20675, "Ġtob": 20676, "Ġexterior": 20677, "Ġmetric": 20678, "ieur": 20679, "Ġtroll": 20680, "ĠÑĢоз": 20681, "æĺŁ": 20682, "Ġtô": 20683, "ĠìĺĪìģ": 20684, "ĠGesetz": 20685, "Ġед": 20686, "Ġdenominator": 20687, "ì³": 20688, 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"Ġhomemade": 23336, "amba": 23337, "Ġpronunciation": 23338, "Ġalphabet": 23339, "анÑĮ": 23340, "owany": 23341, "iras": 23342, "idency": 23343, "OME": 23344, "ĠÑĢаÑģÑģ": 23345, "arak": 23346, "viamente": 23347, "Ġnonprofit": 23348, "ĠYouTuber": 23349, "Ġparenth": 23350, "ĠBoo": 23351, "vat": 23352, "ĠStir": 23353, "Ġprecip": 23354, "Ġants": 23355, "Ġally": 23356, "ĠMaori": 23357, "ĠëĮĢíķľ": 23358, "åı¯æĺ¯": 23359, "ogene": 23360, "ĠLabour": 23361, "arette": 23362, "Ġrecycling": 23363, "ensa": 23364, "Ġpursuit": 23365, "Ġsak": 23366, "ĠÐĹдеÑģÑĮ": 23367, "Ġtolerance": 23368, "Ġsaat": 23369, "Ġclicked": 23370, "âĻ¥": 23371, "Ġfacebook": 23372, "ĠInto": 23373, "Ġincentives": 23374, "기ëĬĶ": 23375, "ĠDennis": 23376, "ĠWik": 23377, "gesch": 23378, "à¹Ģà¸Ľ": 23379, "ĠÏĢα": 23380, "ĠWhoo": 23381, "Ġrounded": 23382, "Ġdope": 23383, "Ġcapturing": 23384, "ĠWarri": 23385, "Ġcivilian": 23386, "Ġcharming": 23387, "Ġesas": 23388, "Ġsustained": 23389, "Ġleaning": 23390, "Ġabundance": 23391, "ÃŃlia": 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23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 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"Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 24038, "Ġdrawer": 24039, "Ġnoon": 24040, "Ġìŀĺë": 24041, "Ġpermanently": 24042, "æ·±": 24043, "ĠнапÑĢимеÑĢ": 24044, "Ġpodcasts": 24045, "erapeut": 24046, "prit": 24047, "Ġstainless": 24048, "ĠÚ©ÛĴ": 24049, "Ġfamilia": 24050, "ĠÑĢазÑĢ": 24051, "unto": 24052, "ĠÑģÑĤол": 24053, "Ġhä": 24054, "ĠHai": 24055, "ĠPB": 24056, "izon": 24057, "Ġkonnte": 24058, "Ġbüyük": 24059, "Ġutilizar": 24060, "ÚĨ": 24061, "Ġaquesta": 24062, "Ġmixer": 24063, "udent": 24064, "лекÑģ": 24065, "ÅĤu": 24066, "ĠÑģиÑģÑĤем": 24067, "ĠноÑĢм": 24068, "Ġfatal": 24069, "Ġconsiderations": 24070, "Ġvalidation": 24071, "Ġoli": 24072, "ĠkardeÅŁ": 24073, "ĠGLORIA": 24074, "Ġpall": 24075, "еÑģÑĤе": 24076, "Ġrectang": 24077, "Ġmedieval": 24078, "allahi": 24079, "asti": 24080, "ĠSyrian": 24081, "Ġshear": 24082, "Ġdebug": 24083, "ĠMai": 24084, "Ġknocking": 24085, "ĠLex": 24086, "ardan": 24087, "rov": 24088, "Ġmemorial": 24089, "æ°£": 24090, "ooky": 24091, "Ġstuffed": 24092, "Ġpassé": 24093, "Ġwig": 24094, "Ĥł": 24095, "Ġpróxima": 24096, "Ġ1991": 24097, "ĠмеждÑĥ": 24098, "Ġnuestros": 24099, "ĠBeast": 24100, "Ġsmo": 24101, "atched": 24102, "ologia": 24103, "Ġмод": 24104, "Ġgee": 24105, "Ġconceptual": 24106, "Ġô": 24107, "Ġdecreases": 24108, "Ġqueries": 24109, "олÑĮÑĪ": 24110, "ĠApart": 24111, "Ġexempl": 24112, "å±±": 24113, "Ġfled": 24114, "ĠOFF": 24115, "ggak": 24116, "Ġbead": 24117, "hir": 24118, "lies": 24119, "ĠClearly": 24120, "ılar": 24121, "Ġchess": 24122, "Ġwhichever": 24123, "Ġ96": 24124, "ằ": 24125, "Ġrespects": 24126, "ĠмоÑĢ": 24127, "Ġorganism": 24128, "Ġgrandpa": 24129, "ĠVie": 24130, "è·Łä½ł": 24131, "Ġflooding": 24132, "Ġupgraded": 24133, "ÑijÑĢ": 24134, "Ġcheeks": 24135, "Ġconquer": 24136, "Ġstubborn": 24137, "Ġpuzzles": 24138, "Ġauction": 24139, "Ġrelying": 24140, "ĠPROF": 24141, "ĠEsper": 24142, "ĠÐľÐ£": 24143, "Ġhype": 24144, "Ġpossibil": 24145, "Ġimprison": 24146, "ĠErn": 24147, "ìĹĪìĬµëĭĪëĭ¤": 24148, "Ġenvie": 24149, "Ġresurrection": 24150, "ä¸įè¡Į": 24151, "Ġsper": 24152, "ĠVenezuela": 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26716, "urm": 26717, "ĠAdventure": 26718, "ãĥĬ": 26719, "cis": 26720, "emark": 26721, "Ġnego": 26722, "Ġinappropriate": 26723, "ĠпÑĢиз": 26724, "ĠÑĢол": 26725, "Ġdreamed": 26726, "Bry": 26727, "Ġshuttle": 26728, "Ġpillars": 26729, "Ġbik": 26730, "inum": 26731, "ĠÑĥÑģ": 26732, "ĠNebr": 26733, "Ġperpendicular": 26734, "Ġbooked": 26735, "bery": 26736, "Ġvikt": 26737, "bear": 26738, "esus": 26739, "Ġвозможно": 26740, "¨¹": 26741, "Ġpresumably": 26742, "ĠMemphis": 26743, "Ġambulance": 26744, "×ķ×ŀר": 26745, "Ġthumbnail": 26746, "Ġmodification": 26747, "éĩı": 26748, "Ġinterpreted": 26749, "Ġpromo": 26750, "Ġκά": 26751, "ĠεÏĢ": 26752, "Ġacoustic": 26753, "ĠDB": 26754, "åĵİ": 26755, "Ġnonetheless": 26756, "oule": 26757, "Ġpequ": 26758, "Ġknob": 26759, "ãĤ£": 26760, "ĠëıĮìķĦ": 26761, "Ġpurchases": 26762, "ĠÃĩünkü": 26763, "Ġdividing": 26764, "perform": 26765, "raction": 26766, "healthy": 26767, "ĠTitle": 26768, "Ġuk": 26769, "Ġcerca": 26770, "Ġarguably": 26771, "Ġfale": 26772, "ë³µ": 26773, 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, "èĦ": 27067, "аниз": 27068, "Ġpalav": 27069, "ĠSword": 27070, "Ġgim": 27071, "ĠCrow": 27072, "Ġpotent": 27073, "bish": 27074, "Ġabused": 27075, "ĠJed": 27076, "Ġgambling": 27077, "ĠSpect": 27078, "Ġinvestigators": 27079, "æĻļ": 27080, "Ġratt": 27081, "Ġdob": 27082, "ĠDES": 27083, "hog": 27084, "ĠоÑĤкÑĢÑĭ": 27085, "íĮħ": 27086, "ĠденÑĮги": 27087, "Ġíĺ¹": 27088, "Ġ머리": 27089, "Ġsaturation": 27090, "Ġinherited": 27091, "ĠInnovation": 27092, "ìĹĪëįĺ": 27093, "Ġtangible": 27094, "Ġdepri": 27095, "hed": 27096, "Ġпомог": 27097, "Ġsliced": 27098, "à¥į": 27099, "Ġthế": 27100, "Å¥": 27101, "68": 27102, "Ġcorona": 27103, "Ġgifted": 27104, "Ġsoir": 27105, "Ġhumility": 27106, "ĠìĿ´ê±¸": 27107, "Ġflaws": 27108, "ĠпÑĢакÑĤи": 27109, "Ġkald": 27110, "waż": 27111, "yw": 27112, "ãĤĵãģ§ãģĻ": 27113, "irteen": 27114, "Ġcrochets": 27115, "¦¬ê°Ģ": 27116, "ĠìłĦìĹIJ": 27117, "Ġdese": 27118, "æ¥Ń": 27119, "Ġмаг": 27120, "ĠdziaÅĤ": 27121, "Ġlég": 27122, "changing": 27123, "Ġllev": 27124, "ÅĦsk": 27125, "çĶ»": 27126, "Ġ1984": 27127, "orns": 27128, "ĠWelsh": 27129, "Ġpharmaceutical": 27130, "Ġpumping": 27131, "ĠShaw": 27132, "punk": 27133, "Ġvault": 27134, "Ġkinetic": 27135, "Ġhurricane": 27136, "ĠIncluding": 27137, "ức": 27138, "ĠGrandpa": 27139, "anship": 27140, "é¦Ļ港": 27141, "ĠвÑĭÑħод": 27142, "нож": 27143, "ľł": 27144, "utta": 27145, "Ġê²ģëĭĪëĭ¤": 27146, "Ġbaz": 27147, "ĠпоÑĪ": 27148, "Ġpeculiar": 27149, "zyÄĩ": 27150, "ĠEllie": 27151, "Ġlearns": 27152, "ĠKrishna": 27153, "Ġconsecut": 27154, "Ġempath": 27155, "ĠDin": 27156, "Ġtraded": 27157, "ĠBoris": 27158, "uggage": 27159, "olla": 27160, "Ġназв": 27161, "Ġeternity": 27162, "Ġвп": 27163, "èmes": 27164, "Ġgrapp": 27165, "bé": 27166, "ĠпÑĢедÑģÑĤав": 27167, "ĠFC": 27168, "įëĭĪëĭ¤": 27169, "even": 27170, "ĠNebraska": 27171, "ortune": 27172, "Ġkarena": 27173, "ĠAgent": 27174, "Ġsting": 27175, "ĠPI": 27176, "Ġmunicipal": 27177, "powered": 27178, "Ġconsegue": 27179, "ĠManchester": 27180, "Ġrainy": 27181, "Ġbli": 27182, "Ġkost": 27183, 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"ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, "Ġworthwhile": 28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 28566, "ãģ¡ãĤī": 28567, "ĠMaz": 28568, "´ìĭ¬": 28569, "Ġви": 28570, "ãĤ§": 28571, "ĠÑĤакиÑħ": 28572, "ĠSharon": 28573, "Ġenthusiastic": 28574, "irement": 28575, "Ġíŀĺëĵ¤": 28576, "Ġpotrze": 28577, "Ġinitiated": 28578, "ãĥ§": 28579, "ĠÅĽrod": 28580, "ĠìĿ´ë¦Ħ": 28581, "Ġremake": 28582, "Ġculmin": 28583, "Ġconfuse": 28584, "miyor": 28585, "urar": 28586, "CTOR": 28587, "Ġbunny": 28588, "Ġ大": 28589, "ä¸įèĥ½": 28590, "elp": 28591, "Ġvampire": 28592, "Ġillumin": 28593, "ĠHend": 28594, "ĠкаÑĩе": 28595, "ĠSalv": 28596, "Ġканал": 28597, "Ġporta": 28598, "Ġasshole": 28599, "Ġsupporter": 28600, "Ġskeptical": 28601, "Ġknead": 28602, "Ġìĺ¬": 28603, "eza": 28604, "Ġquê": 28605, "ĠDH": 28606, "Ġrodz": 28607, "owners": 28608, "Ġplots": 28609, "Ġdelays": 28610, "Ġbelonged": 28611, "Ġahh": 28612, "Ġcarved": 28613, "Ġrisen": 28614, "Ġorden": 28615, "phony": 28616, "issy": 28617, "!!!!!!!!": 28618, "ĠolduÄŁunu": 28619, "Ġroses": 28620, "Ġintrins": 28621, "ĠAngst": 28622, "Ġfinalement": 28623, "ì§Ŀ": 28624, "SOUND": 28625, "Ġindul": 28626, "°Į": 28627, "Ġ×ķ×Ķ": 28628, "chy": 28629, "акÑģим": 28630, "Ġnggak": 28631, "Ġliz": 28632, "Ġelectoral": 28633, "ĠShawn": 28634, "ricia": 28635, "Ġarsen": 28636, "ĠPep": 28637, "Ġ2030": 28638, "Ġtrophy": 28639, "Ġsmoother": 28640, "Ġerre": 28641, "Ġcrashes": 28642, "Ġschne": 28643, "Ġasi": 28644, "ĠMaÃŁ": 28645, "Ñĥли": 28646, "ÑĩеÑģки": 28647, "ieves": 28648, "REAM": 28649, "Ġstirring": 28650, "ãĥĢ": 28651, "usta": 28652, "Ġinver": 28653, "sight": 28654, "ordu": 28655, "oor": 28656, "ĠÄĥn": 28657, "Ġpermitted": 28658, "ÑĢÑĮ": 28659, "Ġchalk": 28660, "ãĤĪãģĹ": 28661, "Ġtattoos": 28662, "ĠRelations": 28663, "ĠHoy": 28664, "ksam": 28665, "Ġdentist": 28666, "Ġ미êµŃ": 28667, "Ġsofa": 28668, "ĠÑĶ": 28669, "Ġforme": 28670, "ÙĤØ©": 28671, "Ġë²ł": 28672, "Ġembraced": 28673, "mil": 28674, "Ġsunglasses": 28675, "Ġê°Ķ": 28676, "Ġseamless": 28677, "Ġbeep": 28678, "ächst": 28679, "Ġsweets": 28680, "Ġsemaine": 28681, "Ġirrelevant": 28682, "Ġdesenvol": 28683, "ÏģÏī": 28684, "ĠпÑĢоизвод": 28685, "angs": 28686, "Ġaroma": 28687, "Ġpools": 28688, "Ġgiá»Ŀ": 28689, "ĠUg": 28690, "Ġclimbed": 28691, "Ġtrending": 28692, "Ġseperti": 28693, "ĠBarr": 28694, "ĠpÅĤ": 28695, "ĠOriginally": 28696, "ĠÚ¯": 28697, "utto": 28698, "Ĭ¸ë": 28699, "ĠкоÑĤоÑĢÑĭÑħ": 28700, "ĠзаÑħ": 28701, "Ġeigenen": 28702, "Ġmurderer": 28703, "ername": 28704, "Åŀ": 28705, "Ġannouncing": 28706, "ĠPlatform": 28707, "Ġexplanations": 28708, "Ġpresente": 28709, "ĠNasıl": 28710, "Ġorphan": 28711, "ĠFortnite": 28712, "rospect": 28713, "eredith": 28714, "ĠìĹĨìĸ´": 28715, "ĠNIH": 28716, "wagen": 28717, "Ġremed": 28718, "§Ģë": 28719, "mont": 28720, "ĠJeffrey": 28721, "prom": 28722, "Ġfünf": 28723, "Ġназад": 28724, "Ġcucumber": 28725, "ĠSummit": 28726, "åĪĿ": 28727, "§¤": 28728, "ÐĿÐIJЯ": 28729, "ĠJet": 28730, "Ġcambio": 28731, "ÑĥйÑĤе": 28732, "Ġcubic": 28733, "Ġdisproportion": 28734, "erez": 28735, "Ġmadness": 28736, "çĹĽ": 28737, "Ġtint": 28738, "Ġfueron": 28739, "Ġky": 28740, "Ġbipart": 28741, "ãģ¾ãģĽ": 28742, "Sam": 28743, "Ġë½": 28744, "Ġriv": 28745, "ĠTank": 28746, "ĠëĨĵ": 28747, "Ġrendered": 28748, "ÅĽlÄĻ": 28749, "conds": 28750, "Ġdisruption": 28751, "Ġinconven": 28752, "Ġquiser": 28753, "Ġdenial": 28754, "Ġgalaxies": 28755, "Ġsovereign": 28756, "Ġpolsk": 28757, "ÏģÏİ": 28758, "Ġmex": 28759, "Ġcaracter": 28760, "ĠLego": 28761, "anden": 28762, ".'\"": 28763, "ĠíĶĮë": 28764, "Ġcompressor": 28765, "ĠMovie": 28766, "Ġapplicants": 28767, "ziehen": 28768, "Ġvegetation": 28769, "Ġbelle": 28770, "ĠGOOD": 28771, "ĠBau": 28772, "Ġresent": 28773, "sex": 28774, "amentos": 28775, "Ġ×Ķ×ĸ×Ķ": 28776, "Ġoverload": 28777, "Ġsilicone": 28778, "еÑģÑĤно": 28779, "Ġdenken": 28780, "Ġdefinit": 28781, "ĠWasn": 28782, "Ġaltered": 28783, "ĠSoo": 28784, "ĠWing": 28785, "indre": 28786, "ĠNPC": 28787, "ÏģÎŃ": 28788, "ĠTwenty": 28789, "ĠLiebe": 28790, "Ġhomelessness": 28791, "oulder": 28792, "ĠÐĺÑĤак": 28793, "ÑģкаÑı": 28794, "Ġcuatro": 28795, "ĠHarvey": 28796, "Ġphilan": 28797, "ĠBeet": 28798, "Ġpolicing": 28799, "ĠAlexand": 28800, "Ġмолод": 28801, "Ġmüs": 28802, "Ġhizo": 28803, "ë³´ëĭ¤": 28804, "Ġпозвол": 28805, "ĠпÑĭÑĤ": 28806, "оÑĩемÑĥ": 28807, "Ġíĥľ": 28808, "Ġcryptocurrency": 28809, "Ġloro": 28810, "Ġsummation": 28811, "Ġbakalım": 28812, "Ġneuros": 28813, "Ø¥": 28814, "Ġможем": 28815, "Ġüst": 28816, "Ġpreliminary": 28817, "Ġhorns": 28818, "ĠTI": 28819, "ÙĥÙĦ": 28820, "YO": 28821, "Ġhinge": 28822, "Ġrepairs": 28823, "Ġbonding": 28824, "Ġbize": 28825, "ĠÑĪÑĤ": 28826, "Ġmotive": 28827, "ĠNigeria": 28828, "120": 28829, "block": 28830, "Ġaviation": 28831, "ĠKommun": 28832, "Ġоказ": 28833, "Ġtenha": 28834, "Ġeducating": 28835, "Ġstaat": 28836, "æ¶Ī": 28837, "ĠÑģколÑĮко": 28838, "Ġfrightened": 28839, "Ġseeks": 28840, "ÑĢÑĥÑĪ": 28841, "quent": 28842, "ĠNou": 28843, "Ġprat": 28844, "ĠShot": 28845, "Work": 28846, "karang": 28847, "ĠLightning": 28848, "nolds": 28849, "rolled": 28850, "glass": 28851, "Ġcredibility": 28852, "ITY": 28853, "Ġatmospheric": 28854, "Ġhavia": 28855, "ändern": 28856, "cheers": 28857, "These": 28858, "ĠCell": 28859, "Ġmagnes": 28860, "ĠBravo": 28861, "season": 28862, "ĠÅŁeyler": 28863, "ðŁİ": 28864, "white": 28865, "ĠMB": 28866, "Ġstacked": 28867, "Ġ74": 28868, "Ġдавай": 28869, "Ġpave": 28870, "ĠоÑħ": 28871, "Ġdataset": 28872, "Ġretour": 28873, "Ġmaturity": 28874, "Ġquase": 28875, "Ġ93": 28876, "ĠSym": 28877, "Ġbriefing": 28878, "Ġculturally": 28879, "Ġì·¨": 28880, "inhas": 28881, "Ġmadam": 28882, "Ġajudar": 28883, "ĠTibet": 28884, "Ġleaks": 28885, "cile": 28886, "Ġtheaters": 28887, "ìĺ¨": 28888, "ãĥĸ": 28889, "72": 28890, "ĠWash": 28891, "ĠQuality": 28892, "ĠIvan": 28893, "ĠBent": 28894, "igator": 28895, "ĠGeschichte": 28896, "Ġreactive": 28897, "Ġ1900": 28898, "æ¡Ī": 28899, "Ġcontradict": 28900, "Ġziemlich": 28901, "Ġcohort": 28902, "ủ": 28903, "Ġpestic": 28904, "Ġoraz": 28905, "Ġtellement": 28906, "é¾": 28907, "ĠNowadays": 28908, "crew": 28909, "Steve": 28910, "Ġfictional": 28911, "Ġilk": 28912, "ãģĤãģ£": 28913, "Ġgasoline": 28914, "zam": 28915, "Ġpancake": 28916, "ència": 28917, "Ġmuitos": 28918, "Ġbury": 28919, "Ġkop": 28920, "ĠIQ": 28921, "Ġreservation": 28922, "ĠUpdate": 28923, "Ġjej": 28924, "ĠEyes": 28925, "åıij": 28926, "Ġvive": 28927, "Ġchce": 28928, "ĠIni": 28929, "respons": 28930, "Ġreflective": 28931, "ĠWan": 28932, "Ñĸз": 28933, "Ġenca": 28934, "Ġembod": 28935, "ĠBurger": 28936, "Ġacademia": 28937, "ĠCirc": 28938, "ĠпÑĢек": 28939, "Ġanlam": 28940, "Ġphilanthrop": 28941, "ĠBaÅŁ": 28942, "ĠAudi": 28943, "Ġvost": 28944, "ä½łçŁ¥éģĵ": 28945, "Ġreper": 28946, "Peter": 28947, "Ġconsoles": 28948, "Ġscrut": 28949, "ĠTurner": 28950, "ĠбÑĭв": 28951, "III": 28952, "訴": 28953, "ĠFlight": 28954, "à¸ĸ": 28955, "ĠRaven": 28956, "Ġcorros": 28957, "fern": 28958, "Ġprova": 28959, "ĠSev": 28960, "Ġrecipro": 28961, "Ġ1985": 28962, "Ġnueva": 28963, "Ġdab": 28964, "ãĢģãĢĮ": 28965, "Ġmez": 28966, "ĠStark": 28967, "ppings": 28968, "оÑģÑĤи": 28969, "ì¦Ŀ": 28970, "Ġframing": 28971, "ĠÐłÐ°Ð·": 28972, "Ġpostp": 28973, "ĠShannon": 28974, "ĠкÑĥÑĢ": 28975, "Ġjakby": 28976, "iennent": 28977, "ĠMaps": 28978, "ĠRevelation": 28979, "ĠÑģÑĤал": 28980, "ìļ´ëį°": 28981, "Ġdevant": 28982, "ĠGiving": 28983, "ĠWAS": 28984, "Ġкого": 28985, "Ġrema": 28986, "ĠRC": 28987, "nÃŃ": 28988, "Ġslipped": 28989, "ĠRams": 28990, "Ġweet": 28991, "Ġmasculine": 28992, "ĠEc": 28993, "Ġreop": 28994, "ĠPlant": 28995, "ĠMAY": 28996, "Ġspikes": 28997, "Ġnozzle": 28998, "ĠWikipedia": 28999, "ĠCoh": 29000, "ISSA": 29001, "chlossen": 29002, "ì§Ģ를": 29003, "Ġ미ë": 29004, "ĠNeder": 29005, "Josh": 29006, "ĠÐłÐ¾ÑģÑģии": 29007, "Ġ1987": 29008, "ĠTheory": 29009, "ekk": 29010, "Ġutan": 29011, "Ġдома": 29012, "chu": 29013, "ĠÑģб": 29014, "Ġaprove": 29015, "VEN": 29016, "ueprint": 29017, "Ġ84": 29018, "æ¼Ĥ亮": 29019, "Cor": 29020, "Ġricher": 29021, "Ġsandwiches": 29022, "atsu": 29023, "ÑĪиÑħ": 29024, "Ġlatt": 29025, "~~~~": 29026, "friends": 29027, "Ġdernière": 29028, "Ġstereo": 29029, "ĠÑįкÑģп": 29030, "Ġprotections": 29031, "Ġhaut": 29032, "Everyone": 29033, "Ġenterprises": 29034, "ĠMostly": 29035, "ĠSpotify": 29036, "ĠSex": 29037, "Ġung": 29038, "Į를": 29039, "Ġactivism": 29040, "ctica": 29041, "original": 29042, "ĠпÑĢогÑĢам": 29043, "Ġbroccoli": 29044, "à¦": 29045, "огÑĢаÑĦ": 29046, "Ġsekarang": 29047, "Ġcrafting": 29048, "Ġбан": 29049, "ãģ»ãģ©": 29050, "ĠRaz": 29051, "Ġnaive": 29052, "Ġscrolling": 29053, "Ġnumerical": 29054, "Ġscheduling": 29055, "Ġapartments": 29056, "çį": 29057, "Ġstretches": 29058, "acey": 29059, "ĠHER": 29060, "ãĤº": 29061, "Ġzinc": 29062, "Ġdarn": 29063, "Ġcél": 29064, "Ġwardrobe": 29065, "Ġredirect": 29066, "Ġjum": 29067, "ĠStrange": 29068, "ĠnÃło": 29069, "Ġexperimenting": 29070, "éré": 29071, "Ġvoulez": 29072, "Ġgebe": 29073, "ĠKann": 29074, "ĠÄijá»Ļ": 29075, "ĠMaxim": 29076, "ĠKön": 29077, "ĠGlas": 29078, "Ġpolished": 29079, "Ġnuma": 29080, "Ich": 29081, "Ġrituals": 29082, "ĠSI": 29083, "иÑĤели": 29084, "Ġinfilt": 29085, "Ġscarf": 29086, "ophy": 29087, "Ġyine": 29088, "Ġcivic": 29089, "ĠMeng": 29090, "änge": 29091, "Õ¥": 29092, "histoire": 29093, "ĠOke": 29094, "ĠìĺĨ": 29095, "Ġsollten": 29096, "Ġ82": 29097, "馬": 29098, "Ġprescribed": 29099, "ĠDubai": 29100, "ĠEltern": 29101, "Ġnationwide": 29102, "Ġskating": 29103, "iary": 29104, "Ġrewarded": 29105, "Ġmorality": 29106, "ĠMaggie": 29107, "ĠOhhh": 29108, "ĠFahren": 29109, "olved": 29110, "æŶåĢĻ": 29111, "Ġdeuxième": 29112, "techn": 29113, "role": 29114, "Ġleider": 29115, "ĠJAY": 29116, "ĠинÑĦоÑĢм": 29117, "Ġcaffe": 29118, "reichen": 29119, "Ġkart": 29120, "ĠCute": 29121, "ffective": 29122, "Ġbully": 29123, "agar": 29124, "Ġcommodity": 29125, "Ġobrig": 29126, "OUR": 29127, "Ġunpleasant": 29128, "nox": 29129, "Jul": 29130, "olith": 29131, "ÑĤоÑıÑī": 29132, "ĠBella": 29133, "Ġdolls": 29134, "ĠHoff": 29135, "Ġadvisors": 29136, "Ġtransfers": 29137, "ĠGoku": 29138, "Ġ1200": 29139, "inhos": 29140, "Pal": 29141, "Ġëĺij": 29142, "Ġrept": 29143, "Ġaccomplishment": 29144, "Ġweave": 29145, "Ġoversight": 29146, "Ġunhealthy": 29147, "Ġfilt": 29148, "Ġpudding": 29149, "ĠMiguel": 29150, "Ġchuckles": 29151, "åı°çģ£": 29152, "version": 29153, "Ġconfession": 29154, "value": 29155, "Ġtriumph": 29156, "Ġsair": 29157, "Ġëħ¸": 29158, "Ġarte": 29159, "ĠMaterial": 29160, "uti": 29161, "Ġliquor": 29162, "ĠBayern": 29163, "ĠMail": 29164, "Ġíĸ¥": 29165, "Ñģком": 29166, "Ġcheapest": 29167, "ĠÑĩаÑģÑĤи": 29168, "ĠJobs": 29169, "ĠCanyon": 29170, "harma": 29171, "aley": 29172, "andro": 29173, "Ġappearances": 29174, "prof": 29175, "Ġоз": 29176, "lagen": 29177, "Ġ//": 29178, "ĠлиÑĪÑĮ": 29179, "Ġrecovering": 29180, "дж": 29181, "psy": 29182, "ãĥ¢": 29183, "Ġswift": 29184, "ĠSpin": 29185, "å¸Ī": 29186, "Ġseinem": 29187, "Ġdolph": 29188, "führ": 29189, "ât": 29190, "Ġaltijd": 29191, "ĠMarty": 29192, "ĠHoch": 29193, "Ġpredators": 29194, "Ġvorher": 29195, "ĠÐĶавай": 29196, "Ġfragments": 29197, "Ġpastry": 29198, "Ġcommen": 29199, "ĠSana": 29200, "Ġê±´ëį°": 29201, "ussen": 29202, "Ġtela": 29203, "ĠNina": 29204, "lek": 29205, "Ġcries": 29206, "Ġthighs": 29207, "ĠFlex": 29208, "ĠBuzz": 29209, "ãĦ": 29210, "Us": 29211, "Ġpaso": 29212, "Ġdeclined": 29213, "ĠNy": 29214, "balance": 29215, "Ġmasa": 29216, "Ġjos": 29217, "ãģªãĤĭ": 29218, "ĠСпаÑģибо": 29219, "achu": 29220, "loud": 29221, "Ġpena": 29222, "ĠWald": 29223, "Ġelimination": 29224, "ĠвеÑģÑĮ": 29225, "orage": 29226, "Ġmisunderstanding": 29227, "Ġendorse": 29228, "Ġogóle": 29229, "Ġgreed": 29230, "Ġklein": 29231, "׾×Ķ": 29232, "REY": 29233, "ĠEating": 29234, "Ġseminar": 29235, "ĠBirthday": 29236, "Ġquelle": 29237, "ĠMulti": 29238, "Ġtirar": 29239, "Ġperch": 29240, "Ġlavor": 29241, "ĠJia": 29242, "Ġmutations": 29243, "Ġcigarettes": 29244, "ÙĪج": 29245, "Ġcousins": 29246, "Ġcapsule": 29247, "Ġhorrific": 29248, "Ġstur": 29249, "Ġzeigt": 29250, "nuts": 29251, "Ġmeanwhile": 29252, "ĠColin": 29253, "Ġgobierno": 29254, "Ġgw": 29255, "Ġuhh": 29256, "ĠJER": 29257, "specific": 29258, "Ġallegations": 29259, "Ġë©ĭ": 29260, "ĠElla": 29261, "ooked": 29262, "ĠFit": 29263, "affle": 29264, "ĠAprès": 29265, "ĠDuck": 29266, "Ġcellular": 29267, "ców": 29268, "ĠÑĩÑĥвÑģÑĤв": 29269, "genommen": 29270, "ìĬ¤íĬ¸": 29271, "Ġlain": 29272, "isol": 29273, "Ġholders": 29274, "Ġbooster": 29275, "ĠSasha": 29276, "ÑĭваеÑĤ": 29277, "ģ¼": 29278, "Ġseparating": 29279, "Ġreinforcement": 29280, "Ġодной": 29281, "ìĹĨ": 29282, "IDE": 29283, "ĠOption": 29284, "phon": 29285, "Ġplais": 29286, "ĠCamb": 29287, "ĠíĻĺ": 29288, "Ġuncommon": 29289, "\":": 29290, "miyorum": 29291, "moi": 29292, "acje": 29293, "ажÑĥ": 29294, "Õ¶": 29295, "Ġgems": 29296, "üler": 29297, "ools": 29298, "Ġenzymes": 29299, "Ġkidnapped": 29300, "Ġketchup": 29301, "talk": 29302, "Ġzach": 29303, "Ġwasher": 29304, "ãĢĤãĢĤ": 29305, "ĠArchitect": 29306, "venue": 29307, "ĠPlanning": 29308, "éĢģ": 29309, "ĠSavior": 29310, "ĠгÑĢÑĥпп": 29311, "íĬ¼": 29312, "arya": 29313, "Ġproceso": 29314, "Ġlimbs": 29315, "Ġrealizes": 29316, "iander": 29317, "FS": 29318, "aji": 29319, "Ġunite": 29320, "ĠìĿĺë": 29321, "ĠpossÃŃvel": 29322, "raits": 29323, "ĠAgre": 29324, "ÛĮÚ©": 29325, "ìĦľëıĦ": 29326, "æİī": 29327, "Ġвел": 29328, "ĠмеÑģÑı": 29329, "anor": 29330, "Pat": 29331, "Ġdernier": 29332, "ÏĥÏĦε": 29333, "ĠкакаÑı": 29334, "Ġlässt": 29335, "æİ°": 29336, "ĠMeh": 29337, "Ġngh": 29338, "Ġamateur": 29339, "è«ĸ": 29340, "Fe": 29341, "Ġê¶ģ": 29342, "Ġsituación": 29343, "Ġsedan": 29344, "Ġcleansing": 29345, "lasting": 29346, "Ġcommunist": 29347, "ANE": 29348, "Ġirregular": 29349, "Ġsout": 29350, "ĠCarney": 29351, "Ġallemaal": 29352, "ĠmuchÃŃs": 29353, "Ġlibro": 29354, "ÐŃÑĤо": 29355, "Ġап": 29356, "Ġcontinuation": 29357, "ĠLor": 29358, "?\",": 29359, "quin": 29360, "Ġcharacterized": 29361, "ajes": 29362, "Ġsights": 29363, "ĠÑıзÑĭ": 29364, "ĠUhh": 29365, "è·³": 29366, "birth": 29367, "dong": 29368, "Ġhablando": 29369, "Ġsymptom": 29370, "çµĤ": 29371, "Ġcapacitor": 29372, "Ġtransported": 29373, "Ġignorant": 29374, "Ġникогда": 29375, "Ġdrip": 29376, "ĠEva": 29377, "Ġadject": 29378, "Ġmassively": 29379, "ĠEthi": 29380, "ĠCircle": 29381, "Ġrainfall": 29382, "ĠMouse": 29383, "Ġrefund": 29384, "ĠZw": 29385, "assemb": 29386, "Ġ220": 29387, "ĠOrd": 29388, "è§Ĵ": 29389, "Ġveins": 29390, "ĠGiant": 29391, "Ġmãe": 29392, "Ġvap": 29393, "Ġmisses": 29394, "οÏħÏĤ": 29395, "Mo": 29396, "ĠEntwick": 29397, "INT": 29398, "ÙĨت": 29399, "Ġtheoretically": 29400, "Ġtearing": 29401, "Ġtroubled": 29402, "prem": 29403, "Ġrepetitive": 29404, "Ġâĸ": 29405, "Ġheavenly": 29406, "ĠAmber": 29407, "Ġполож": 29408, "Ġíķ´ì¤": 29409, "Ġvowel": 29410, "anking": 29411, "ĠWirtschaft": 29412, "Ġirr": 29413, "Ġcozy": 29414, "Ġunfamiliar": 29415, "ĠPors": 29416, "Ġë§ŀìķĦ": 29417, "ĠTimothy": 29418, "ÑģолÑİÑĤ": 29419, "pex": 29420, "ĠVIS": 29421, ")(": 29422, "Ġsuperst": 29423, "Ġimprov": 29424, "ĠBeng": 29425, "Ġdisconnected": 29426, "Ġapt": 29427, "ÑĢен": 29428, "ĠExtra": 29429, "Ġбел": 29430, "shop": 29431, "dings": 29432, "ĠConnecticut": 29433, "ì°¬": 29434, "ĠGC": 29435, "åıĸ": 29436, "beh": 29437, "Jeremy": 29438, "ĠBatt": 29439, "ãģ¸": 29440, "atha": 29441, "ĠZusammen": 29442, "screams": 29443, "Ġgras": 29444, "afft": 29445, "ĠInitially": 29446, "ĠBrett": 29447, "Ġspecifications": 29448, "Ġseaweed": 29449, "Ġoath": 29450, "Ġfountain": 29451, "ĠкоÑĤоÑĢой": 29452, "ĠStein": 29453, "èģ²": 29454, "ĠCorinth": 29455, "Ġconjug": 29456, "å·¦åı³": 29457, "Ġcompensate": 29458, "ĠëĬIJëĤĮìĿ´": 29459, "Ġonze": 29460, "Ġskincare": 29461, "Brian": 29462, "Ġservir": 29463, "}}": 29464, "ĠVik": 29465, "Ġunint": 29466, "Ġsuppliers": 29467, "Ġbalcony": 29468, "Ġenergia": 29469, "ometric": 29470, "зÑı": 29471, "Ġsigh": 29472, "ĠTOM": 29473, "ĠPure": 29474, "ytt": 29475, "ÑĭÑģ": 29476, "ĠRainbow": 29477, "ĠPitts": 29478, "×Ļ×ŀ": 29479, "Ġstatues": 29480, "heads": 29481, "Ġcoupled": 29482, "éĮ¢": 29483, "Ġherd": 29484, "ä½ĵ": 29485, "Ġexcluded": 29486, "Ġgilt": 29487, "ĠÑİ": 29488, "Ġswoje": 29489, "ĠSver": 29490, "63": 29491, "issant": 29492, "Ġdürfen": 29493, "łĪë": 29494, "Ġkissing": 29495, "oof": 29496, "以ä¸Ĭ": 29497, "Ġcursed": 29498, "Ġshowers": 29499, "Ġswinging": 29500, "Ġreproduce": 29501, "ãģ¨ãģĦãģĨãģĵãģ¨": 29502, "Ġsätt": 29503, "elcome": 29504, "Ġfundamentals": 29505, "Ġalmond": 29506, "Ġpé": 29507, "Ġwellbeing": 29508, "Ġhunters": 29509, "å¾Ģ": 29510, "Sec": 29511, "ĵľë¦´": 29512, "Ġemission": 29513, "Ġpsychologist": 29514, "Ġbetrayed": 29515, "ĠReynolds": 29516, "LES": 29517, "Ġpolling": 29518, "Ġnegatively": 29519, "Ġcombines": 29520, "׾×IJ": 29521, "аÑĢа": 29522, "λλά": 29523, "ĠTurns": 29524, "OTT": 29525, "Ġ×Ķ×Ļ": 29526, "aison": 29527, "Ġairline": 29528, "Ġrestriction": 29529, "wal": 29530, "Ġaurait": 29531, "ĠLebanon": 29532, "ĠMOR": 29533, "Ġmonkeys": 29534, "éner": 29535, "ÑĸÑĹ": 29536, "Ġmotherf": 29537, "ĠÙĩØ°Ùĩ": 29538, "Ġfeu": 29539, "ühren": 29540, "Ġhygiene": 29541, "enteen": 29542, "Des": 29543, "Ġdissip": 29544, "Est": 29545, "Ġsaints": 29546, "Ġpotassium": 29547, "Ġreckon": 29548, "Clintus": 29549, "Ġmanifestation": 29550, "ĠAppro": 29551, "ĠInspect": 29552, "Ġventilation": 29553, "Ġhelm": 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"Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, "culos": 32397, "Ġcrise": 32398, "裡éĿ¢": 32399, "Ġclasse": 32400, "beitet": 32401, "iÄŁi": 32402, "Ġwhales": 32403, "Ġperimeter": 32404, "Ġtying": 32405, "Ġstrony": 32406, "Ġlikewise": 32407, "ĠPunch": 32408, "Da": 32409, "ĠBaptist": 32410, "Ġsorting": 32411, "Ġiv": 32412, "Ġíķ©": 32413, "Ġrehab": 32414, "Ġeta": 32415, "river": 32416, "Ġsai": 32417, "ãģĦãģŁãģł": 32418, "odus": 32419, "ãģĬé¡ĺãģĦãģĹãģ¾ãģĻ": 32420, "Ġessayer": 32421, "Ġturtles": 32422, "ĠHazrat": 32423, "Ġfabrics": 32424, "Ġcavity": 32425, "Ġponieważ": 32426, "Ġschlecht": 32427, "Ġsalsa": 32428, "ÅŁekkür": 32429, "Ġseating": 32430, "Ġeconomists": 32431, "Ġmang": 32432, "Ġseguinte": 32433, "Ġrang": 32434, "Ġratios": 32435, "Ġconstell": 32436, "Ġlongtemps": 32437, "uating": 32438, "Ġspoiled": 32439, "Ġrecipients": 32440, "Ġsniper": 32441, "ä¹ĭåīį": 32442, "ìĬµëĭĪê¹Į": 32443, "Ġwp": 32444, "ĠLINKE": 32445, "Ġflare": 32446, "ĠAdri": 32447, "ñas": 32448, "Ġbackl": 32449, "mÃ¤ÃŁ": 32450, "ĠBend": 32451, "Ġworkloads": 32452, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, 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"Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 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"Ġsalaries": 35057, "Ġinsecurity": 35058, "ÅĤoÅĽci": 35059, "ĠабÑģолÑİÑĤно": 35060, "inken": 35061, "ĠEddy": 35062, "udos": 35063, "Ġdummy": 35064, "Ðļак": 35065, "six": 35066, "Ġinbox": 35067, "ẩ": 35068, "People": 35069, "á»ĵng": 35070, "Ġorganizers": 35071, "find": 35072, "Ġül": 35073, "ĠCOM": 35074, "ża": 35075, "weile": 35076, "Commentary": 35077, "íĬ¸ë¥¼": 35078, "ĠMittel": 35079, "kus": 35080, "èĽĭ": 35081, "न": 35082, "iral": 35083, "Ġgarment": 35084, "ικά": 35085, "Ġstool": 35086, "payers": 35087, "Ġshimmer": 35088, "ĠOllie": 35089, "ĠJeżeli": 35090, "è¿ĺæľī": 35091, "Ġ1977": 35092, "Ġjeux": 35093, "Ġextinct": 35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 35384, "ıģımız": 35385, "Ġtrig": 35386, "Ġaxes": 35387, "inters": 35388, "Ġpenalties": 35389, "ĠOsman": 35390, "ĠJenna": 35391, "Ġflakes": 35392, "Ġtrainers": 35393, "Ġstunned": 35394, "ĠScroll": 35395, "ĠPip": 35396, "ĠнаÑģÑĤ": 35397, "ĠnhÃł": 35398, "ĠSmack": 35399, "ẫn": 35400, "ratos": 35401, "ĠÑĢабоÑĤÑĭ": 35402, "Ġucz": 35403, "ĠLemon": 35404, "ĠSind": 35405, "Ġpsychic": 35406, "ĠAbg": 35407, "Ġmammals": 35408, "Ġimmersive": 35409, "Ġbots": 35410, "Ġverschiedene": 35411, "Ġgeral": 35412, "Ġfollower": 35413, "Ġä»ĸ": 35414, "Ġseguridad": 35415, "Ġimmersed": 35416, "feito": 35417, "cross": 35418, "Ġöld": 35419, "íĥĦ": 35420, "Ġãģĵãģ®": 35421, "Ġ×Ķ×Ļ×IJ": 35422, "ĠJian": 35423, "Ġbiliyor": 35424, "area": 35425, "Ġkaf": 35426, "Ġgodt": 35427, "çĽ¸ä¿¡": 35428, "Ġë°©ìĨ¡": 35429, "Ġdetriment": 35430, "æ¥ļ": 35431, "Ñĸл": 35432, "ĠÄijâu": 35433, "Ġchloride": 35434, "øre": 35435, "lei": 35436, "Ġmonte": 35437, "Ġdifférentes": 35438, "à¯ģ.": 35439, "Ġcaregivers": 35440, "Ġinadequ": 35441, "Ġfarewell": 35442, "ĠÑĤипа": 35443, "ontec": 35444, "ĠEph": 35445, "HHH": 35446, "ĠTodos": 35447, "ĠСШÐIJ": 35448, "Ġtrov": 35449, "Ġlige": 35450, "Ġcông": 35451, "ĠCiv": 35452, "Ġcapaz": 35453, "ĠVallahi": 35454, "Ġqueste": 35455, "Ġreplica": 35456, "سب": 35457, "zna": 35458, "ĠÑģлÑĥж": 35459, 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36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, 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36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, 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36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, 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"Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, "Ġuphill": 39132, "Ġéén": 39133, "âĢ¦)": 39134, "ĠÑģоп": 39135, "ĠCorey": 39136, "Ġsiebie": 39137, "Ġmuse": 39138, "Ġcloves": 39139, "Ġpous": 39140, "ĠFinanz": 39141, "ĠRoute": 39142, "amat": 39143, "Ġmutually": 39144, "ĠвнÑĥÑĤÑĢи": 39145, "ĠSelena": 39146, "ëĶ": 39147, "ĠGaussian": 39148, "ë¶ĢíĦ°": 39149, "Ġ×ij׼": 39150, "Ġejerc": 39151, "å¾®": 39152, "kea": 39153, "ĠGerry": 39154, "ĠSic": 39155, "大çļĦ": 39156, "Ġ1966": 39157, "iese": 39158, "Ġfossils": 39159, "Ġestad": 39160, "ĠKane": 39161, "ciÄĩ": 39162, "ĠìľłíĬľë": 39163, "Ġпам": 39164, "ĠCruise": 39165, "intérieur": 39166, "Ġbekannt": 39167, "ĠPode": 39168, "Ġdemander": 39169, "Rem": 39170, "Ġinvade": 39171, "Ġdecorating": 39172, "ropic": 39173, "Ġcowboy": 39174, "ĠPhoto": 39175, "opolit": 39176, "Ġì»¬ëŁ¬ë": 39177, "Ġreap": 39178, "Ġhandwriting": 39179, "à¹Ħร": 39180, "Ġëļ": 39181, "Ġبعد": 39182, "ĠMt": 39183, "ÙĢ": 39184, "Ġspaceship": 39185, "Ġnationalism": 39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, 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39249, "Ġundertaking": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 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"Ġê·¸ëĮĢë¡ľ": 41711, "ìĭ¤í": 41712, "Ġsimulated": 41713, "ĠëĦ¤ê°Ģ": 41714, "Ġbö": 41715, "ĠOtto": 41716, "LING": 41717, "Ġillustrates": 41718, "ĠDestroy": 41719, "Ġ1961": 41720, "ĠTagen": 41721, "Ġmelon": 41722, "ĠPascal": 41723, "QUE": 41724, "ĠполÑĥÑĩиÑĤÑĮ": 41725, "Ġincidence": 41726, "ĠStevens": 41727, "ĠGins": 41728, "rue": 41729, "Ġunreasonable": 41730, "ĠJie": 41731, "ysics": 41732, "Ġ몰ëĿ¼": 41733, "Ġfishes": 41734, "©´ì": 41735, "Ġprecurs": 41736, "ĠmogÄĻ": 41737, "tight": 41738, "eté": 41739, "Ġmundial": 41740, "ìĹĪëĭ¤": 41741, "âĢ¦!": 41742, "BU": 41743, "Ġsociology": 41744, "Ġbrutality": 41745, "Ġpersonaje": 41746, "ĠnÃŃvel": 41747, "Ġfazem": 41748, "Ġessen": 41749, "Ġdwelling": 41750, "Ġcommercially": 41751, "Ġedits": 41752, "Ġdues": 41753, "ĠGSA": 41754, "ìĿ¸ê°Ģ": 41755, "ĠíĹĪíĮĿ": 41756, "ĠYahoo": 41757, "енеÑĢ": 41758, "ìľ¨": 41759, "ÑĥÑĪки": 41760, "left": 41761, "Ġcaptive": 41762, "cipher": 41763, "Ġ×ŀ×ŀ×": 41764, "ĠгÑĢом": 41765, "Ġinnate": 41766, "Ġimpul": 41767, "ĠìŬìŀIJ": 41768, "Ġswallowed": 41769, "ĠTabii": 41770, "ìĿ´ìĭ": 41771, "ĠÑģоÑģÑĤав": 41772, "Ġoyun": 41773, "Ġobrigado": 41774, "ĠAph": 41775, "Katie": 41776, "Ġcena": 41777, "ĠAllÄģh": 41778, "ÙĪس": 41779, "Ġprzyp": 41780, "Ġpept": 41781, "Ġvoluntarily": 41782, "ĠOÄŁlum": 41783, "ĠElo": 41784, "oue": 41785, "Bir": 41786, "burger": 41787, "ĠSBS": 41788, "Ġ6000": 41789, "Ġpromotional": 41790, "ĠHerrn": 41791, "Ġstamping": 41792, "Ġqualifying": 41793, "Ġcosmos": 41794, "Ġafar": 41795, "æ±Ł": 41796, "abus": 41797, "Ġdads": 41798, "ãģŃãģĩ": 41799, "ĠÑįконом": 41800, "incarn": 41801, "Ġìĸ´ëĶ": 41802, "Ġлеж": 41803, "ĠBET": 41804, "Ġнайд": 41805, "onter": 41806, "Ġreusable": 41807, "Ġkomma": 41808, "ĠBij": 41809, "ĠTeraz": 41810, "ĠOlá": 41811, "ĠìķĦ침": 41812, "ĠÑĢазмеÑĢ": 41813, "awan": 41814, "Ġcarta": 41815, "æIJŀ": 41816, "iceless": 41817, "Ġsme": 41818, "ĠTutaj": 41819, "ĠÈĺi": 41820, "Ġprobation": 41821, "Ġadequately": 41822, "ĠPresidential": 41823, "indruck": 41824, "blade": 41825, "Ġveulent": 41826, "Ġcioè": 41827, "åĮħæĭ¬": 41828, "Ġreverb": 41829, "Ġgegenüber": 41830, "ĠEspero": 41831, "Ġbege": 41832, "ĠSTUDENT": 41833, "sound": 41834, "ĠDü": 41835, "Ġoffend": 41836, "Ġ\"..": 41837, "kennt": 41838, "ĠÑģлÑĥÑĪ": 41839, "Ġpurposely": 41840, "ĠLit": 41841, "ĠíĽ¨": 41842, "ucher": 41843, "Ġhina": 41844, "ých": 41845, "ignon": 41846, "THE": 41847, "Ġglide": 41848, "ourcing": 41849, "ĠØ£ÙĨا": 41850, "Ġollut": 41851, "Ġarchety": 41852, "Ġshady": 41853, "Ġsomm": 41854, "Ġepile": 41855, "Keep": 41856, "Ġnajbardziej": 41857, "à¤ķ": 41858, "itutional": 41859, "Ġмай": 41860, "Ġsinful": 41861, "ĠBronx": 41862, "ĠглÑĥб": 41863, "Ġvam": 41864, "Ġpresets": 41865, "ĠDag": 41866, "ĠìĻĦìĦ±": 41867, "Ġcreek": 41868, "itures": 41869, "ĠLords": 41870, "ött": 41871, "UNT": 41872, "Ra": 41873, "Ġinequalities": 41874, "Ġcollateral": 41875, "Ġwrists": 41876, "Ġgrouped": 41877, "ĠобÑĭÑĩно": 41878, "Ġarmored": 41879, "Ġtung": 41880, "Ġconverge": 41881, "Ġbok": 41882, "ĠDodge": 41883, "нÑıÑı": 41884, "Ġfleeing": 41885, "ĠMartinez": 41886, "ĠDreams": 41887, "kek": 41888, "Ġsociale": 41889, "ĠPlaza": 41890, "دة": 41891, "Ġkell": 41892, "ĠStellen": 41893, "felt": 41894, "ĠÑģпаÑģ": 41895, "ĠPv": 41896, "Ġcanción": 41897, "ĠHert": 41898, "ĠBalance": 41899, "Ġselves": 41900, "Ġvandaag": 41901, "Ġpry": 41902, "Ġnajle": 41903, "ĠвидиÑĤе": 41904, "Ġvelvet": 41905, "Ġgroot": 41906, "Ġfout": 41907, "模": 41908, "ĠSchulen": 41909, "ĠMohammed": 41910, "ĠCenters": 41911, "Ġhaver": 41912, "Ġfreuen": 41913, "¤íĬ¸": 41914, "лан": 41915, "POS": 41916, "inki": 41917, "Ġëĭµ": 41918, "Ġparalyzed": 41919, "GLISH": 41920, "Ġcasts": 41921, "ĠVC": 41922, "ìĿ´ìħĺ": 41923, "Ġتھ": 41924, "票": 41925, "Ġì¤ĺ": 41926, "Ġר×ķצ": 41927, "Ġsuced": 41928, "Ġprogresses": 41929, "ĠEÄŁer": 41930, "°ëıĦ": 41931, "Ġinstallations": 41932, "pedo": 41933, "еÑĢб": 41934, "interpret": 41935, "Ġê³łë¯¼": 41936, "ĠAzerbai": 41937, "ividades": 41938, "Ġì£ĦìĨ¡": 41939, "Ġentfer": 41940, "Ġchwil": 41941, "ĠHerbert": 41942, "ĠAlexandria": 41943, "yty": 41944, "Ġsechs": 41945, "Ġcaliber": 41946, "ĠWeise": 41947, "ĠHeck": 41948, "ĠYug": 41949, "ĠاÙĦØ·": 41950, "Ġpesar": 41951, "Ġcigar": 41952, "Ġmél": 41953, "Ġhaird": 41954, "Ġprzypadku": 41955, "Ġconfidently": 41956, "Ġanarch": 41957, "ĠGian": 41958, "Ġdobre": 41959, "cjÄĻ": 41960, "awy": 41961, "ĠRece": 41962, "ĠGobierno": 41963, "Ġcarga": 41964, "umsy": 41965, "Ġnorte": 41966, "Ġhandler": 41967, "Ġrespecting": 41968, "Ġallied": 41969, "ĠPiet": 41970, "ichtlich": 41971, "Ġolds": 41972, "Ġdusty": 41973, "Ġgry": 41974, "Ġ-...": 41975, "GHT": 41976, "Ġneo": 41977, "Ñĩики": 41978, "ежд": 41979, "aide": 41980, "ĠбÑĥло": 41981, "íį¼": 41982, "Ġtemporada": 41983, "Ġdoute": 41984, "âĺĨ": 41985, "ĠìĪł": 41986, "ĠJUSTIN": 41987, "auto": 41988, "Ġrationale": 41989, "prob": 41990, "Ġfishy": 41991, "Ġdoorway": 41992, "Ġemptiness": 41993, "еннаÑı": 41994, "Ġbrag": 41995, "ĠÐĵде": 41996, "çĪ¾": 41997, "Ġtransient": 41998, "Ġmittlerweile": 41999, "ĠBret": 42000, "Ġfij": 42001, "Ġdeposited": 42002, "NS": 42003, "ĠìķŀìĹIJ": 42004, "Ġkimse": 42005, "Ġcharities": 42006, "ĠMillenn": 42007, "dogs": 42008, "Ġmoyen": 42009, "Ġnuevos": 42010, "ĠCookie": 42011, "parable": 42012, "doing": 42013, "ĠSail": 42014, "Ġicy": 42015, "haba": 42016, "Ġqueens": 42017, "Ġchocolates": 42018, "ĠNay": 42019, "ĠÑĦин": 42020, "Ġvec": 42021, "Ġhelmets": 42022, "TM": 42023, "ĠArmed": 42024, "Ġimpairment": 42025, "ĠTus": 42026, "ĠMême": 42027, "omez": 42028, "ĠRequ": 42029, "ĠInvestig": 42030, "íİĺ": 42031, "Ġgolpe": 42032, "ĠRac": 42033, "igraph": 42034, "Ġkwest": 42035, "Ġsailors": 42036, "Ġstatutory": 42037, "Ġmilestones": 42038, "ĠMash": 42039, "ĠGesetzentwurf": 42040, "éĬ": 42041, "Ġcoloured": 42042, "huma": 42043, "Ġyere": 42044, "Ġsubtitles": 42045, "Ġembodied": 42046, "Ġmisschien": 42047, "ĠiPh": 42048, "ützen": 42049, "Ġdetached": 42050, "Ġdescrição": 42051, "ciamo": 42052, "Ġrecoil": 42053, "ĠÐŃÑĤоÑĤ": 42054, "Ġexported": 42055, "ĠAlone": 42056, "antry": 42057, "Ġestan": 42058, "ĠSod": 42059, "Ġlavoro": 42060, "æĬĬå®ĥ": 42061, "ר×ij": 42062, "ĠÄijá»ĭ": 42063, "Ġswag": 42064, "ĠPCB": 42065, "ĠKaiser": 42066, "ĠModer": 42067, "jug": 42068, "Ġtextile": 42069, "Tw": 42070, "Ġnac": 42071, "frei": 42072, "Ġretard": 42073, "iscern": 42074, "Ġtallest": 42075, "ĠLuca": 42076, "Rah": 42077, "Ġpreacher": 42078, "Ġjut": 42079, "ĠRica": 42080, "iciency": 42081, "ĠÄijiá»ģu": 42082, "Ġkaufen": 42083, "Ġnett": 42084, "Ġdiscut": 42085, "Ġdeprived": 42086, "¡Ń": 42087, "Ġspricht": 42088, "Ġenclosed": 42089, "ĠSubst": 42090, "ç§ij": 42091, "ĠRabbit": 42092, "prised": 42093, "Ġbitches": 42094, "ìŁģ": 42095, "çīĪ": 42096, "Ġtapa": 42097, "ĠEssen": 42098, "ĠBao": 42099, "Ġdevient": 42100, "ĠWuhan": 42101, "ĠTipp": 42102, "Ġdisast": 42103, "ÑģÑĤвÑĥ": 42104, "ublique": 42105, "Ġqualité": 42106, "Ġinadequate": 42107, "Ġbargaining": 42108, "ĠGotcha": 42109, "евиÑĩ": 42110, "ievous": 42111, "erton": 42112, "blue": 42113, "ĠìĽĢì§ģ": 42114, "Ġsandbox": 42115, "ĠRein": 42116, "親": 42117, "ĠìĿ´ê²ĥëıĦ": 42118, "Ġsax": 42119, "zogen": 42120, "unächst": 42121, "Ġherkes": 42122, "Ġ-,": 42123, "zeni": 42124, "rising": 42125, "Ġresposta": 42126, "Ġpromotions": 42127, "ĠUnterstüt": 42128, "ĠMAS": 42129, "Nothing": 42130, "otics": 42131, "ĠвÑĭй": 42132, "Ġrotates": 42133, "kien": 42134, "Ġhabla": 42135, "ĠDani": 42136, "union": 42137, "Ġwack": 42138, "Ġarchaeological": 42139, "ĠCurtis": 42140, "ĠHoriz": 42141, "Ġ골ë": 42142, "Ġwaiver": 42143, "åĺ¿": 42144, "Bon": 42145, "Ġrotated": 42146, "Ġpitcher": 42147, "Ġinad": 42148, "Ġhugs": 42149, "ĠNortheast": 42150, "×Ļת×Ļ": 42151, "Ġplea": 42152, "Ġcupcake": 42153, "ĠLY": 42154, "Ġfamili": 42155, "Ġgroo": 42156, "ĠBlair": 42157, "Ġlij": 42158, "Ġhabitats": 42159, "Ġcommunism": 42160, "osium": 42161, "bars": 42162, "ĠFreeman": 42163, "neo": 42164, "Ġdiffuse": 42165, "Ġcylinders": 42166, "ĠDebat": 42167, "íĸĪëĬĶëį°": 42168, "еÑĪе": 42169, "Ġfingerprints": 42170, "Ġamar": 42171, "вид": 42172, "ĠìłķëıĦë¡ľ": 42173, 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43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 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43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, 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"Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, "ĠAllahu": 44351, "ÏİÏģα": 44352, "ìķĺìĬµëĭĪëĭ¤": 44353, "ĠVote": 44354, "ĠSmells": 44355, "одно": 44356, "Ġhindsight": 44357, "VR": 44358, "ĠPatch": 44359, "ĠJahres": 44360, "Ġsouvenir": 44361, "Ġneutron": 44362, "Ġlongtime": 44363, "Ġsayin": 44364, "ä¹IJ": 44365, "asaki": 44366, "ĠоÑģÑĤанов": 44367, "Ġexpelled": 44368, "Ġcryptocurrencies": 44369, "ĠMurder": 44370, "ĠCitizen": 44371, "WAY": 44372, "Ġplu": 44373, "Ġlemonade": 44374, "Ġconveniently": 44375, "ĠHI": 44376, "Ġ2023": 44377, "ש×ķת": 44378, "аÑĨион": 44379, "Ġ뼰": 44380, "ĠÙĦÙĥÙĨ": 44381, "Ġнемножко": 44382, "Ġunused": 44383, "Ġmaioria": 44384, "Ġastrology": 44385, "ĠDownt": 44386, "Nick": 44387, "Ġpreoccup": 44388, "Ġdemain": 44389, "×ŀ×¢": 44390, "ĠводÑĭ": 44391, "ĠSanskrit": 44392, "Ġprêt": 44393, "Ġstranded": 44394, "Ġrefin": 44395, "ĠпÑĢиним": 44396, "ĠповеÑĢÑħ": 44397, "à¯į?": 44398, "Ġzrob": 44399, "Ġintertw": 44400, "ĠDavidson": 44401, "лена": 44402, "ĠпонÑıÑĤÑĮ": 44403, "ĠReno": 44404, "ĠполÑĥÑĩилоÑģÑĮ": 44405, 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45032, "funding": 45033, "Ġzust": 45034, "ĠPens": 45035, "sed": 45036, "ĠíŤ": 45037, "Ġgereki": 45038, "Ġalarms": 45039, "ĠWha": 45040, "ĠMarkus": 45041, "aksi": 45042, "ĠÐIJле": 45043, "klore": 45044, "Ġéner": 45045, "Ġtilde": 45046, "boxing": 45047, "ĠìĦŀ": 45048, "Ġencontramos": 45049, "ĠPhar": 45050, "наком": 45051, "óst": 45052, "ĠÄ°s": 45053, "Ġëĭĺ": 45054, "Ġsquats": 45055, "Ġpretended": 45056, "Ġdez": 45057, "Ġê´ľì°®ìķĦ": 45058, "jach": 45059, "ëĿ¼ê³ł": 45060, "ĠíĻķì§Ħ": 45061, "ĠAnsch": 45062, "imerk": 45063, "Ġconjugate": 45064, "Ġpeninsula": 45065, "Ġgorilla": 45066, "Ġphotographed": 45067, "ĠAunque": 45068, "Ġentren": 45069, "ĠDeutschen": 45070, "ĠAladdin": 45071, "Ġ무ìĦľ": 45072, "ĠStella": 45073, "ĠElection": 45074, "outine": 45075, "Grand": 45076, "ĠWak": 45077, "ĠSergio": 45078, "horse": 45079, "ahon": 45080, "ĠFamilies": 45081, "Ġhating": 45082, "ĠBett": 45083, "à¸Ļะà¸Ħะ": 45084, "Ġcurling": 45085, "ĠIsraelis": 45086, "Ġ׾×IJ×": 45087, "ĠMyers": 45088, "Ġscanned": 45089, "ĠBEC": 45090, "ileri": 45091, "Ġcalle": 45092, "ĠMinh": 45093, "Ġmicron": 45094, "Ġconduc": 45095, "ÃŃv": 45096, "ĠвозÑĮ": 45097, "Ġactionable": 45098, "ĠTrustees": 45099, "Ġtief": 45100, "Ġheaders": 45101, "Ġanimales": 45102, "ìĽĢ": 45103, "лоÑħ": 45104, "unity": 45105, "lya": 45106, "Ġjangan": 45107, "Ġhani": 45108, "Ġcasing": 45109, "Ġjóvenes": 45110, "ĠSplit": 45111, "ĠCarlo": 45112, "ĠBeim": 45113, "å°įä¸įèµ·": 45114, "Ġnuanced": 45115, "Ġteddy": 45116, "ĠClan": 45117, "ächen": 45118, "pier": 45119, "Ġдополн": 45120, "Ġdiaper": 45121, "effective": 45122, "ĠNiagara": 45123, "Ġwart": 45124, "Ġcorro": 45125, "ĠKampf": 45126, "zte": 45127, "Ġdéveloppement": 45128, "Ġattackers": 45129, "ĠSherman": 45130, "Ġ1914": 45131, "Ġmeow": 45132, "ĠPÃ¥": 45133, "ìº": 45134, "cit": 45135, "Ġcoupe": 45136, "Ġê·¸ëĭ¤ìĿĮìĹIJ": 45137, "Ġhumour": 45138, "Ġcole": 45139, "ĠWarning": 45140, "ĠTil": 45141, "calm": 45142, "buat": 45143, "Ġcine": 45144, "kiej": 45145, "Kevin": 45146, "Ġmilligrams": 45147, 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"ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, "ĠJacqu": 49770, "tak": 49771, "iembre": 49772, "Ġxen": 49773, "Ġnominees": 49774, "Ġbiomedical": 49775, "ésus": 49776, "Ġestuv": 49777, "ÏĦÏĮ": 49778, "ATHAN": 49779, "Ġíķľëį°": 49780, "Ġheed": 49781, "crosstalk": 49782, "Bill": 49783, "Ġspouses": 49784, "ĠÑģÑİж": 49785, "Ġverso": 49786, "ĠSven": 49787, "ĠCau": 49788, "cuz": 49789, "Ġë³´ìĦ¸ìļĶ": 49790, "ĠÑħозÑı": 49791, "Ġmocking": 49792, "ĠOna": 49793, "ĠDá": 49794, "Ġfruitful": 49795, "Ġbanquet": 49796, "udding": 49797, "inctions": 49798, "dert": 49799, "sud": 49800, "Ġdescon": 49801, "ĠJC": 49802, "Ġ§": 49803, "Ġpubli": 49804, "ëĪĪ": 49805, "éģķãģĨ": 49806, "Ġentschieden": 49807, "ĠROI": 49808, "ãģįãģŁ": 49809, "ĠìĥĿê²¼": 49810, "Ġkäytt": 49811, "yani": 49812, "shaw": 49813, "Ġunleash": 49814, "Ġmanne": 49815, "Ġhistogram": 49816, "æĬ¥": 49817, "à¸Ńะà¹Ħร": 49818, "Ġgn": 49819, "Ġfella": 49820, "Ġeinges": 49821, "ĠBuilt": 49822, "Ġrepresenta": 49823, "Ġpunishing": 49824, "Ġoutsiders": 49825, "нÑĥÑĤÑĮÑģÑı": 49826, "current": 49827, "Ġfamiliarity": 49828, "Ġдив": 49829, "Ġprojets": 49830, "Ġaqueles": 49831, "ĠGlue": 49832, "those": 49833, "Ġinception": 49834, "Ġaquellos": 49835, "Ġillusions": 49836, "Ġattends": 49837, "rese": 49838, "Ġswarm": 49839, "Ġswab": 49840, "Ġregardez": 49841, "Ġposição": 49842, "Ġakhir": 49843, "Ġextracting": 49844, "Ġanecdote": 49845, "ĠTale": 49846, "Ġвин": 49847, "Ġabges": 49848, "ĠoluÅŁ": 49849, "Ġcomplicado": 49850, "Ġcovari": 49851, "ÑĸÑĤÑĮ": 49852, "Der": 49853, "Ġ×Ļ×Ķ": 49854, "Form": 49855, "Ġìĸ´ì¨Įëĵł": 49856, "Ġreadable": 49857, "Ġinhibit": 49858, "Ġdecipher": 49859, "ĠAngry": 49860, "pg": 49861, "வத": 49862, "ĠÑģобÑģÑĤвенно": 49863, "Ġsamh": 49864, "Ġescr": 49865, "Ġencompasses": 49866, "Ġauster": 49867, "Ġconfisc": 49868, "ĠMandal": 49869, "Ġ}": 49870, "atcher": 49871, "=#": 49872, "çļĦæŶåĢĻ": 49873, "Ġкино": 49874, "Ġstal": 49875, "lungs": 49876, "Ġvole": 49877, "Ġrequis": 49878, "ĠãĤĪ": 49879, "Ġpén": 49880, "Ġlecturer": 49881, "Ġinscription": 49882, "Ġcervical": 49883, "ĠTreasure": 49884, "ĠJW": 49885, "comings": 49886, "Ġeyesight": 49887, "ĠTails": 49888, "ÃŃsimo": 49889, "Ġworksheet": 49890, "Ġswiftly": 49891, "Ġconos": 49892, "Ġeliminates": 49893, "ĠBlaze": 49894, "алог": 49895, "Ġpictured": 49896, "Ġgiraffe": 49897, "ĠLogic": 49898, "åĺī": 49899, "Ġenrichment": 49900, "Fit": 49901, "Ġunintended": 49902, "Ġpersecuted": 49903, "akap": 49904, "ë°ĺ": 49905, "Ġbarber": 49906, "Ġarbeitet": 49907, "ĠSurprisingly": 49908, "ĠAutob": 49909, "unku": 49910, "prov": 49911, "ĠLoch": 49912, "obyl": 49913, "ĠподгоÑĤов": 49914, "Ġéconomique": 49915, "Ġpatt": 49916, "Ġceased": 49917, "ĠÑģпиÑģ": 49918, "Ġnuclei": 49919, "Ġiste": 49920, "ĠWag": 49921, "ĠzupeÅĤnie": 49922, "Ġproverb": 49923, "ĠAhÃŃ": 49924, "åĽŀåİ»": 49925, "liamo": 49926, "Ġreliably": 49927, "Ġpik": 49928, "ĠTrading": 49929, "ĠColeman": 49930, "Ġανα": 49931, "Ġmagari": 49932, "ĠPHIL": 49933, "Ġshedding": 49934, "ohner": 49935, "Ġpornography": 49936, "Ġbeneficiaries": 49937, "âĢ¢": 49938, "enin": 49939, "Ġresolving": 49940, "ĠÑģпоÑĢÑĤ": 49941, "Ġбег": 49942, "Ġnectar": 49943, "ultura": 49944, "imsical": 49945, "ĮĢ를": 49946, "å¹´åīį": 49947, "ãģĹãĤĥ": 49948, "Ġvisão": 49949, "éģİä¾Ĩ": 49950, "ÿÿÿÿÿÿÿÿ": 49951, "attform": 49952, "Ġë§ŀëĬĶ": 49953, "Ġpilgrimage": 49954, "Ġmating": 49955, "ĠReaper": 49956, "ĠBref": 49957, "çĶŁæ´»": 49958, "Ġ×ij×ĵ": 49959, "Ġnovamente": 49960, "Ġgrilling": 49961, "ĠWireless": 49962, "ĠRomanian": 49963, "ÒĽ": 49964, "ìľłë": 49965, "hait": 49966, "ĠBora": 49967, "ARRY": 49968, "Ġhypotheses": 49969, "马": 49970, "ikut": 49971, "ĠìķĦë²Ħ": 49972, "ĠÑĸз": 49973, "Ġnationale": 49974, "تÙī": 49975, "üllt": 49976, "Ġéléments": 49977, "ĠWare": 49978, "Ġ(-": 49979, "алÑĮном": 49980, "Ġindict": 49981, "ĠStones": 49982, "ãģŁãĤģ": 49983, "explosion": 49984, "ĠëĥĦìĥĪ": 49985, "Ġfelic": 49986, "Ġjudiciary": 49987, "Ġincarnation": 49988, "Ġinning": 49989, "Ġformul": 49990, "Ġshipment": 49991, "Ġreindeer": 49992, "æĴŃ": 49993, "ĠознаÑĩ": 49994, "Ġenvol": 49995, "undy": 49996, "ĠзнаÑĤÑĮ": 49997, "Ġвидели": 49998, "Ġexcluding": 49999, "death": 50000, "Ġberm": 50001, "Ġsoprattutto": 50002, "Ġdebido": 50003, "ĠZig": 50004, "ĠOv": 50005, "ĠKEVIN": 50006, "ĠPale": 50007, "ĠMire": 50008, "Ġandar": 50009, "including": 50010, "Ġswapped": 50011, "Ġmisconceptions": 50012, "Ġspong": 50013, "réal": 50014, "Ġorbitals": 50015, "Ġhashtags": 50016, "orit": 50017, "Ġmauvais": 50018, "иÑģа": 50019, "Ġlivres": 50020, "ĠIPS": 50021, "Ġ04": 50022, "ög": 50023, "instr": 50024, "ĠвнеÑĪ": 50025, "Ġhice": 50026, "isée": 50027, "Ġowes": 50028, "Ġesimerk": 50029, "ĠUH": 50030, "Ġirritation": 50031, "Ġgiggles": 50032, "Ġcolonialism": 50033, "ĠBliss": 50034, "strings": 50035, "Ġreunited": 50036, "ĠPsaki": 50037, "wach": 50038, "Ġcliffs": 50039, "ĠFalse": 50040, "äg": 50041, "pipe": 50042, "Ġwhopping": 50043, "Ġmeringue": 50044, "Ġbung": 50045, "industrie": 50046, "Ġleche": 50047, "ĠLoy": 50048, "Ġdrie": 50049, "Ġpassat": 50050, "Ġoleh": 50051, "Ġcéu": 50052, "ĠGabrie": 50053, 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50228, "Ġmalfunction": 50229, "Ġasteroids": 50230, "irez": 50231, "amorph": 50232, "ĠÑģоÑĤÑĢÑĥд": 50233, "Ġfreshwater": 50234, "Ġarran": 50235, "ĠпÑĢÑĭ": 50236, "ног": 50237, "Ġdiabetic": 50238, "ĠÙĤاÙĦ": 50239, "Ġoppress": 50240, "Ġcapacitance": 50241, "performance": 50242, "crates": 50243, "Ġapostle": 50244, "ĠJEN": 50245, "OULD": 50246, "Intro": 50247, "Ġstalls": 50248, "ĠABOUT": 50249, "cticamente": 50250, "Ġdiligent": 50251, "Ġmanifests": 50252, "ĠPakistani": 50253, "Ġ('": 50254, "åľº": 50255, "": 50256}
\ No newline at end of file
diff --git a/whisper/audio.py b/whisper/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..de8a1951408b8bf08639f17f01b3fa764a82d83b
--- /dev/null
+++ b/whisper/audio.py
@@ -0,0 +1,124 @@
+import os
+from functools import lru_cache
+from typing import Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+ """
+ Open an audio file and read as mono waveform, resampling as necessary
+
+ Parameters
+ ----------
+ file: str
+ The audio file to open
+
+ sr: int
+ The sample rate to resample the audio if necessary
+
+ Returns
+ -------
+ A NumPy array containing the audio waveform, in float32 dtype.
+ """
+ try:
+ # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+ # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+ out, _ = (
+ ffmpeg.input(file, threads=0)
+ .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+ .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+ )
+ except ffmpeg.Error as e:
+ raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+ return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+ """
+ Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+ """
+ if torch.is_tensor(array):
+ if array.shape[axis] > length:
+ array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
+
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+ else:
+ if array.shape[axis] > length:
+ array = array.take(indices=range(length), axis=axis)
+
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = np.pad(array, pad_widths)
+
+ return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+ """
+ load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+ Allows decoupling librosa dependency; saved using:
+
+ np.savez_compressed(
+ "mel_filters.npz",
+ mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+ )
+ """
+ assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+ with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
+ return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
+ """
+ Compute the log-Mel spectrogram of
+
+ Parameters
+ ----------
+ audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+ The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+ n_mels: int
+ The number of Mel-frequency filters, only 80 is supported
+
+ Returns
+ -------
+ torch.Tensor, shape = (80, n_frames)
+ A Tensor that contains the Mel spectrogram
+ """
+ if not torch.is_tensor(audio):
+ if isinstance(audio, str):
+ audio = load_audio(audio)
+ audio = torch.from_numpy(audio)
+
+ window = torch.hann_window(N_FFT).to(audio.device)
+ stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+ magnitudes = stft[..., :-1].abs() ** 2
+
+ filters = mel_filters(audio.device, n_mels)
+ mel_spec = filters @ magnitudes
+
+ log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+ log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+ log_spec = (log_spec + 4.0) / 4.0
+ return log_spec
diff --git a/whisper/decoding.py b/whisper/decoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..603546d4c9ff67514d2567576935b974fe373bef
--- /dev/null
+++ b/whisper/decoding.py
@@ -0,0 +1,712 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributions import Categorical
+
+from .audio import CHUNK_LENGTH
+from .tokenizer import Tokenizer, get_tokenizer
+from .utils import compression_ratio
+
+if TYPE_CHECKING:
+ from .model import Whisper
+
+
+@torch.no_grad()
+def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
+ """
+ Detect the spoken language in the audio, and return them as list of strings, along with the ids
+ of the most probable language tokens and the probability distribution over all language tokens.
+ This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+ Returns
+ -------
+ language_tokens : Tensor, shape = (n_audio,)
+ ids of the most probable language tokens, which appears after the startoftranscript token.
+ language_probs : List[Dict[str, float]], length = n_audio
+ list of dictionaries containing the probability distribution over all languages.
+ """
+ if tokenizer is None:
+ tokenizer = get_tokenizer(model.is_multilingual)
+ if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
+ raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
+
+ single = mel.ndim == 2
+ if single:
+ mel = mel.unsqueeze(0)
+
+ # skip encoder forward pass if already-encoded audio features were given
+ if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+ mel = model.encoder(mel)
+
+ # forward pass using a single token, startoftranscript
+ n_audio = mel.shape[0]
+ x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]
+ logits = model.logits(x, mel)[:, 0]
+
+ # collect detected languages; suppress all non-language tokens
+ mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+ mask[list(tokenizer.all_language_tokens)] = False
+ logits[:, mask] = -np.inf
+ language_tokens = logits.argmax(dim=-1)
+ language_token_probs = logits.softmax(dim=-1).cpu()
+ language_probs = [
+ {
+ c: language_token_probs[i, j].item()
+ for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+ }
+ for i in range(n_audio)
+ ]
+
+ if single:
+ language_tokens = language_tokens[0]
+ language_probs = language_probs[0]
+
+ return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+ task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate"
+ language: Optional[str] = None # language that the audio is in; uses detected language if None
+
+ # sampling-related options
+ temperature: float = 0.0
+ sample_len: Optional[int] = None # maximum number of tokens to sample
+ best_of: Optional[int] = None # number of independent samples to collect, when t > 0
+ beam_size: Optional[int] = None # number of beams in beam search, when t == 0
+ patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424)
+
+ # options for ranking generations (either beams or best-of-N samples)
+ length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm
+
+ # prompt, prefix, and token suppression
+ prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context
+ prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context
+ suppress_blank: bool = True # this will suppress blank outputs
+
+ # list of tokens ids (or comma-separated token ids) to suppress
+ # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+ suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+
+ # timestamp sampling options
+ without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only
+ max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this
+
+ # implementation details
+ fp16: bool = True # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+ audio_features: Tensor
+ language: str
+ language_probs: Optional[Dict[str, float]] = None
+ tokens: List[int] = field(default_factory=list)
+ text: str = ""
+ avg_logprob: float = np.nan
+ no_speech_prob: float = np.nan
+ temperature: float = np.nan
+ compression_ratio: float = np.nan
+
+
+class Inference:
+ def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+ """Perform a forward pass on the decoder and return per-token logits"""
+ raise NotImplementedError
+
+ def rearrange_kv_cache(self, source_indices) -> None:
+ """Update the key-value cache according to the updated beams"""
+ raise NotImplementedError
+
+ def cleanup_caching(self) -> None:
+ """Clean up any resources or hooks after decoding is finished"""
+ pass
+
+
+class PyTorchInference(Inference):
+ def __init__(self, model: "Whisper", initial_token_length: int):
+ self.model: "Whisper" = model
+ self.initial_token_length = initial_token_length
+ self.kv_cache = {}
+ self.hooks = []
+
+ def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+ if not self.kv_cache:
+ self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+
+ if tokens.shape[-1] > self.initial_token_length:
+ # only need to use the last token except in the first forward pass
+ tokens = tokens[:, -1:]
+
+ return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
+
+ def cleanup_caching(self):
+ for hook in self.hooks:
+ hook.remove()
+
+ self.kv_cache = {}
+ self.hooks = []
+
+ def rearrange_kv_cache(self, source_indices):
+ for module, tensor in self.kv_cache.items():
+ # update the key/value cache to contain the selected sequences
+ self.kv_cache[module] = tensor[source_indices].detach()
+
+
+class SequenceRanker:
+ def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
+ """
+ Given a list of groups of samples and their cumulative log probabilities,
+ return the indices of the samples in each group to select as the final result
+ """
+ raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+ """
+ Select the sample with the highest log probabilities, penalized using either
+ a simple length normalization or Google NMT paper's length penalty
+ """
+
+ def __init__(self, length_penalty: Optional[float]):
+ self.length_penalty = length_penalty
+
+ def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
+ def scores(logprobs, lengths):
+ result = []
+ for logprob, length in zip(logprobs, lengths):
+ if self.length_penalty is None:
+ penalty = length
+ else:
+ # from the Google NMT paper
+ penalty = ((5 + length) / 6) ** self.length_penalty
+ result.append(logprob / penalty)
+ return result
+
+ # get the sequence with the highest score
+ lengths = [[len(t) for t in s] for s in tokens]
+ return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+ def reset(self):
+ """Initialize any stateful variables for decoding a new sequence"""
+
+ def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+ """Specify how to select the next token, based on the current trace and logits
+
+ Parameters
+ ----------
+ tokens : Tensor, shape = (n_batch, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence tokens
+
+ logits : Tensor, shape = (n_batch, vocab_size)
+ per-token logits of the probability distribution at the current step
+
+ sum_logprobs : Tensor, shape = (n_batch)
+ cumulative log probabilities for each sequence
+
+ Returns
+ -------
+ tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
+ the tokens, appended with the selected next token
+
+ completed : bool
+ True if all sequences has reached the end of text
+
+ """
+ raise NotImplementedError
+
+ def finalize(
+ self, tokens: Tensor, sum_logprobs: Tensor
+ ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
+ """Finalize search and return the final candidate sequences
+
+ Parameters
+ ----------
+ tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence
+
+ sum_logprobs : Tensor, shape = (n_audio, n_group)
+ cumulative log probabilities for each sequence
+
+ Returns
+ -------
+ tokens : Sequence[Sequence[Tensor]], length = n_audio
+ sequence of Tensors containing candidate token sequences, for each audio input
+
+ sum_logprobs : List[List[float]], length = n_audio
+ sequence of cumulative log probabilities corresponding to the above
+
+ """
+ raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+ def __init__(self, temperature: float, eot: int):
+ self.temperature = temperature
+ self.eot = eot
+
+ def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+ temperature = self.temperature
+ if temperature == 0:
+ next_tokens = logits.argmax(dim=-1)
+ else:
+ next_tokens = Categorical(logits=logits / temperature).sample()
+
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
+ sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+ next_tokens[tokens[:, -1] == self.eot] = self.eot
+ tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+ completed = (tokens[:, -1] == self.eot).all()
+ return tokens, completed
+
+ def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+ # make sure each sequence has at least one EOT token at the end
+ tokens = F.pad(tokens, (0, 1), value=self.eot)
+ return tokens, sum_logprobs.tolist()
+
+
+class BeamSearchDecoder(TokenDecoder):
+ def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
+ self.beam_size = beam_size
+ self.eot = eot
+ self.inference = inference
+ self.patience = patience or 1.0
+ self.max_candidates: int = round(beam_size * self.patience)
+ self.finished_sequences = None
+
+ assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+ def reset(self):
+ self.finished_sequences = None
+
+ def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+ if tokens.shape[0] % self.beam_size != 0:
+ raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+ n_audio = tokens.shape[0] // self.beam_size
+ if self.finished_sequences is None: # for the first update
+ self.finished_sequences = [{} for _ in range(n_audio)]
+
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ next_tokens, source_indices, finished_sequences = [], [], []
+ for i in range(n_audio):
+ scores, sources, finished = {}, {}, {}
+
+ # STEP 1: calculate the cumulative log probabilities for possible candidates
+ for j in range(self.beam_size):
+ idx = i * self.beam_size + j
+ prefix = tokens[idx].tolist()
+ for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+ new_logprob = (sum_logprobs[idx] + logprob).item()
+ sequence = tuple(prefix + [token.item()])
+ scores[sequence] = new_logprob
+ sources[sequence] = idx
+
+ # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
+ saved = 0
+ for sequence in sorted(scores, key=scores.get, reverse=True):
+ if sequence[-1] == self.eot:
+ finished[sequence] = scores[sequence]
+ else:
+ sum_logprobs[len(next_tokens)] = scores[sequence]
+ next_tokens.append(sequence)
+ source_indices.append(sources[sequence])
+
+ saved += 1
+ if saved == self.beam_size:
+ break
+
+ finished_sequences.append(finished)
+
+ tokens = torch.tensor(next_tokens, device=tokens.device)
+ self.inference.rearrange_kv_cache(source_indices)
+
+ # add newly finished sequences to self.finished_sequences
+ assert len(self.finished_sequences) == len(finished_sequences)
+ for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
+ for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+ if len(previously_finished) >= self.max_candidates:
+ break # the candidate list is full
+ previously_finished[seq] = newly_finished[seq]
+
+ # mark as completed if all audio has enough number of samples
+ completed = all(
+ len(sequences) >= self.max_candidates for sequences in self.finished_sequences
+ )
+ return tokens, completed
+
+ def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+ # collect all finished sequences, including patience, and add unfinished ones if not enough
+ sum_logprobs = sum_logprobs.cpu()
+ for i, sequences in enumerate(self.finished_sequences):
+ if len(sequences) < self.beam_size: # when not enough sequences are finished
+ for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+ sequence = preceding_tokens[i, j].tolist() + [self.eot]
+ sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+ if len(sequences) >= self.beam_size:
+ break
+
+ tokens: List[List[Tensor]] = [
+ [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
+ ]
+ sum_logprobs: List[List[float]] = [
+ list(sequences.values()) for sequences in self.finished_sequences
+ ]
+ return tokens, sum_logprobs
+
+
+class LogitFilter:
+ def apply(self, logits: Tensor, tokens: Tensor) -> None:
+ """Apply any filtering or masking to logits in-place
+
+ Parameters
+ ----------
+ logits : Tensor, shape = (n_batch, vocab_size)
+ per-token logits of the probability distribution at the current step
+
+ tokens : Tensor, shape = (n_batch, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence tokens
+
+ """
+ raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+ def __init__(self, tokenizer: Tokenizer, sample_begin: int):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ if tokens.shape[1] == self.sample_begin:
+ logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+
+class SuppressTokens(LogitFilter):
+ def __init__(self, suppress_tokens: Sequence[int]):
+ self.suppress_tokens = list(suppress_tokens)
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ logits[:, self.suppress_tokens] = -np.inf
+
+
+class ApplyTimestampRules(LogitFilter):
+ def __init__(
+ self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
+ ):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+ self.max_initial_timestamp_index = max_initial_timestamp_index
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ # suppress <|notimestamps|> which is handled by without_timestamps
+ if self.tokenizer.no_timestamps is not None:
+ logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+ # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+ for k in range(tokens.shape[0]):
+ seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+ last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+ penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+
+ if last_was_timestamp:
+ if penultimate_was_timestamp: # has to be non-timestamp
+ logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+ else: # cannot be normal text tokens
+ logits[k, : self.tokenizer.eot] = -np.inf
+
+ if tokens.shape[1] == self.sample_begin:
+ # suppress generating non-timestamp tokens at the beginning
+ logits[:, : self.tokenizer.timestamp_begin] = -np.inf
+
+ # apply the `max_initial_timestamp` option
+ if self.max_initial_timestamp_index is not None:
+ last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+ logits[:, last_allowed + 1 :] = -np.inf
+
+ # if sum of probability over timestamps is above any other token, sample timestamp
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ for k in range(tokens.shape[0]):
+ timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
+ max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+ if timestamp_logprob > max_text_token_logprob:
+ logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+
+class DecodingTask:
+ inference: Inference
+ sequence_ranker: SequenceRanker
+ decoder: TokenDecoder
+ logit_filters: List[LogitFilter]
+
+ def __init__(self, model: "Whisper", options: DecodingOptions):
+ self.model = model
+
+ language = options.language or "en"
+ tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
+ self.tokenizer: Tokenizer = tokenizer
+ self.options: DecodingOptions = self._verify_options(options)
+
+ self.n_group: int = options.beam_size or options.best_of or 1
+ self.n_ctx: int = model.dims.n_text_ctx
+ self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+ self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+ if self.options.without_timestamps:
+ self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+ self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+ self.sample_begin: int = len(self.initial_tokens)
+ self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+ # inference: implements the forward pass through the decoder, including kv caching
+ self.inference = PyTorchInference(model, len(self.initial_tokens))
+
+ # sequence ranker: implements how to rank a group of sampled sequences
+ self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+ # decoder: implements how to select the next tokens, given the autoregressive distribution
+ if options.beam_size is not None:
+ self.decoder = BeamSearchDecoder(
+ options.beam_size, tokenizer.eot, self.inference, options.patience
+ )
+ else:
+ self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+ # logit filters: applies various rules to suppress or penalize certain tokens
+ self.logit_filters = []
+ if self.options.suppress_blank:
+ self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
+ if self.options.suppress_tokens:
+ self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
+ if not options.without_timestamps:
+ precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds
+ max_initial_timestamp_index = None
+ if options.max_initial_timestamp:
+ max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
+ self.logit_filters.append(
+ ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
+ )
+
+ def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+ if options.beam_size is not None and options.best_of is not None:
+ raise ValueError("beam_size and best_of can't be given together")
+ if options.temperature == 0:
+ if options.best_of is not None:
+ raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+ if options.patience is not None and options.beam_size is None:
+ raise ValueError("patience requires beam_size to be given")
+ if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
+ raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+ return options
+
+ def _get_initial_tokens(self) -> Tuple[int]:
+ tokens = list(self.sot_sequence)
+ prefix = self.options.prefix
+ prompt = self.options.prompt
+
+ if prefix:
+ prefix_tokens = (
+ self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
+ )
+ if self.sample_len is not None:
+ max_prefix_len = self.n_ctx // 2 - self.sample_len
+ prefix_tokens = prefix_tokens[-max_prefix_len:]
+ tokens = tokens + prefix_tokens
+
+ if prompt:
+ prompt_tokens = (
+ self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
+ )
+ tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
+
+ return tuple(tokens)
+
+ def _get_suppress_tokens(self) -> Tuple[int]:
+ suppress_tokens = self.options.suppress_tokens
+
+ if isinstance(suppress_tokens, str):
+ suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+ if -1 in suppress_tokens:
+ suppress_tokens = [t for t in suppress_tokens if t >= 0]
+ suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+ elif suppress_tokens is None or len(suppress_tokens) == 0:
+ suppress_tokens = [] # interpret empty string as an empty list
+ else:
+ assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+ suppress_tokens.extend(
+ [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
+ )
+ if self.tokenizer.no_speech is not None:
+ # no-speech probability is collected separately
+ suppress_tokens.append(self.tokenizer.no_speech)
+
+ return tuple(sorted(set(suppress_tokens)))
+
+ def _get_audio_features(self, mel: Tensor):
+ if self.options.fp16:
+ mel = mel.half()
+
+ if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
+ # encoded audio features are given; skip audio encoding
+ print("encoded audio features are given; skip audio encoding")
+ audio_features = mel
+ else:
+ print(mel.shape)
+ print("===============================")
+ audio_features = self.model.encoder(mel)
+
+ if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
+ return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
+
+ return audio_features
+
+ def _detect_language(self, audio_features: Tensor, tokens: Tensor):
+ languages = [self.options.language] * audio_features.shape[0]
+ lang_probs = None
+
+ if self.options.language is None or self.options.task == "lang_id":
+ lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
+ languages = [max(probs, key=probs.get) for probs in lang_probs]
+ if self.options.language is None:
+ tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
+
+ return languages, lang_probs
+
+ def _main_loop(self, audio_features: Tensor, tokens: Tensor):
+ assert audio_features.shape[0] == tokens.shape[0]
+ n_batch = tokens.shape[0]
+ sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
+ no_speech_probs = [np.nan] * n_batch
+
+ try:
+ for i in range(self.sample_len):
+ logits = self.inference.logits(tokens, audio_features)
+
+ if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs
+ probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+ no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+ # now we need to consider the logits at the last token only
+ logits = logits[:, -1]
+
+ # apply the logit filters, e.g. for suppressing or applying penalty to
+ for logit_filter in self.logit_filters:
+ logit_filter.apply(logits, tokens)
+
+ # expand the tokens tensor with the selected next tokens
+ tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+
+ if completed or tokens.shape[-1] > self.n_ctx:
+ break
+ finally:
+ self.inference.cleanup_caching()
+
+ return tokens, sum_logprobs, no_speech_probs
+
+ @torch.no_grad()
+ def run(self, mel: Tensor) -> List[DecodingResult]:
+ self.decoder.reset()
+ tokenizer: Tokenizer = self.tokenizer
+ n_audio: int = mel.shape[0]
+
+ audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass
+ tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+ # detect language if requested, overwriting the language token
+ languages, language_probs = self._detect_language(audio_features, tokens)
+ if self.options.task == "lang_id":
+ return [
+ DecodingResult(audio_features=features, language=language, language_probs=probs)
+ for features, language, probs in zip(audio_features, languages, language_probs)
+ ]
+
+ # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
+ audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
+ tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+
+ # call the main sampling loop
+ tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
+
+ # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+ audio_features = audio_features[:: self.n_group]
+ no_speech_probs = no_speech_probs[:: self.n_group]
+ assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+ tokens = tokens.reshape(n_audio, self.n_group, -1)
+ sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+ # get the final candidates for each group, and slice between the first sampled token and EOT
+ tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+ tokens: List[List[Tensor]] = [
+ [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
+ ]
+
+ # select the top-ranked sample in each group
+ selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+ tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
+ texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+ sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+ avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
+
+ fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
+ if len(set(map(len, fields))) != 1:
+ raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+ return [
+ DecodingResult(
+ audio_features=features,
+ language=language,
+ tokens=tokens,
+ text=text,
+ avg_logprob=avg_logprob,
+ no_speech_prob=no_speech_prob,
+ temperature=self.options.temperature,
+ compression_ratio=compression_ratio(text),
+ )
+ for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
+ ]
+
+
+@torch.no_grad()
+def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
+ """
+ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+ Parameters
+ ----------
+ model: Whisper
+ the Whisper model instance
+
+ mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
+ A tensor containing the Mel spectrogram(s)
+
+ options: DecodingOptions
+ A dataclass that contains all necessary options for decoding 30-second segments
+
+ Returns
+ -------
+ result: Union[DecodingResult, List[DecodingResult]]
+ The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+ """
+ single = mel.ndim == 2
+ if single:
+ mel = mel.unsqueeze(0)
+ result = DecodingTask(model, options).run(mel)
+
+ if single:
+ result = result[0]
+
+ return result
diff --git a/whisper/inference.py b/whisper/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..9de5f82a28a9278ec0c4ef31dda32eb65852cc4b
--- /dev/null
+++ b/whisper/inference.py
@@ -0,0 +1,59 @@
+import os
+import numpy as np
+import argparse
+import torch
+
+from whisper.model import Whisper, ModelDimensions
+from whisper.audio import load_audio, pad_or_trim, log_mel_spectrogram
+
+
+def load_model(path) -> Whisper:
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ checkpoint = torch.load(path, map_location=device)
+ dims = ModelDimensions(**checkpoint["dims"])
+ model = Whisper(dims)
+ model.load_state_dict(checkpoint["model_state_dict"])
+ return model.to(device)
+
+
+def pred_ppg(whisper: Whisper, wavPath, ppgPath):
+ audio = load_audio(wavPath)
+ audln = audio.shape[0]
+ ppg_a = []
+ idx_s = 0
+ while (idx_s + 25 * 16000 < audln):
+ short = audio[idx_s:idx_s + 25 * 16000]
+ idx_s = idx_s + 25 * 16000
+ ppgln = 25 * 16000 // 320
+ # short = pad_or_trim(short)
+ mel = log_mel_spectrogram(short).to(whisper.device)
+ with torch.no_grad():
+ ppg = whisper.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy()
+ ppg = ppg[:ppgln,] # [length, dim=1024]
+ ppg_a.extend(ppg)
+ if (idx_s < audln):
+ short = audio[idx_s:audln]
+ ppgln = (audln - idx_s) // 320
+ # short = pad_or_trim(short)
+ mel = log_mel_spectrogram(short).to(whisper.device)
+ with torch.no_grad():
+ ppg = whisper.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy()
+ ppg = ppg[:ppgln,] # [length, dim=1024]
+ ppg_a.extend(ppg)
+ np.save(ppgPath, ppg_a, allow_pickle=False)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.description = 'please enter embed parameter ...'
+ parser.add_argument("-w", "--wav", help="wav", dest="wav")
+ parser.add_argument("-p", "--ppg", help="ppg", dest="ppg")
+ args = parser.parse_args()
+ print(args.wav)
+ print(args.ppg)
+
+ wavPath = args.wav
+ ppgPath = args.ppg
+
+ whisper = load_model(os.path.join("whisper_pretrain", "medium.pt"))
+ pred_ppg(whisper, wavPath, ppgPath)
diff --git a/whisper/model.py b/whisper/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb3781c17a1e78a33bf62246e5134e8512206d0d
--- /dev/null
+++ b/whisper/model.py
@@ -0,0 +1,269 @@
+from dataclasses import dataclass
+from typing import Dict
+from typing import Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch import nn
+
+from .decoding import detect_language as detect_language_function, decode as decode_function
+
+
+@dataclass
+class ModelDimensions:
+ n_mels: int
+ n_audio_ctx: int
+ n_audio_state: int
+ n_audio_head: int
+ n_audio_layer: int
+ n_vocab: int
+ n_text_ctx: int
+ n_text_state: int
+ n_text_head: int
+ n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+ def forward(self, x: Tensor) -> Tensor:
+ return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+ def forward(self, x: Tensor) -> Tensor:
+ return F.linear(
+ x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
+ )
+
+
+class Conv1d(nn.Conv1d):
+ def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
+ return super()._conv_forward(
+ x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+ )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+ """Returns sinusoids for positional embedding"""
+ assert channels % 2 == 0
+ log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+ scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+ def __init__(self, n_state: int, n_head: int):
+ super().__init__()
+ self.n_head = n_head
+ self.query = Linear(n_state, n_state)
+ self.key = Linear(n_state, n_state, bias=False)
+ self.value = Linear(n_state, n_state)
+ self.out = Linear(n_state, n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ ):
+ q = self.query(x)
+
+ if kv_cache is None or xa is None or self.key not in kv_cache:
+ # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+ # otherwise, perform key/value projections for self- or cross-attention as usual.
+ k = self.key(x if xa is None else xa)
+ v = self.value(x if xa is None else xa)
+ else:
+ # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+ k = kv_cache[self.key]
+ v = kv_cache[self.value]
+
+ wv, qk = self.qkv_attention(q, k, v, mask)
+ return self.out(wv), qk
+
+ def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
+ n_batch, n_ctx, n_state = q.shape
+ scale = (n_state // self.n_head) ** -0.25
+ q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+ k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+ v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+ qk = q @ k
+ if mask is not None:
+ qk = qk + mask[:n_ctx, :n_ctx]
+ qk = qk.float()
+
+ w = F.softmax(qk, dim=-1).to(q.dtype)
+ return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+
+
+class ResidualAttentionBlock(nn.Module):
+ def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+ super().__init__()
+
+ self.attn = MultiHeadAttention(n_state, n_head)
+ self.attn_ln = LayerNorm(n_state)
+
+ self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+ self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+ n_mlp = n_state * 4
+ self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+ self.mlp_ln = LayerNorm(n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ ):
+ x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
+ if self.cross_attn:
+ x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
+ x = x + self.mlp(self.mlp_ln(x))
+ return x
+
+
+class AudioEncoder(nn.Module):
+ def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+ super().__init__()
+ self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+ self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+ self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+ self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+ [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+ )
+ self.ln_post = LayerNorm(n_state)
+
+ def forward(self, x: Tensor):
+ """
+ x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+ the mel spectrogram of the audio
+ """
+ x = F.gelu(self.conv1(x))
+ x = F.gelu(self.conv2(x))
+ x = x.permute(0, 2, 1)
+
+ len_x = x.shape[1]
+ len_e = self.positional_embedding.shape[0]
+ assert len_x <= len_e, "incorrect audio shape"
+ pos_e = self.positional_embedding[:len_x, :]
+ x = (x + pos_e).to(x.dtype)
+
+ for block in self.blocks:
+ x = block(x)
+
+ x = self.ln_post(x)
+ return x
+
+
+class TextDecoder(nn.Module):
+ def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+ super().__init__()
+
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+ self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+ [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
+ )
+ self.ln = LayerNorm(n_state)
+
+ mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+ self.register_buffer("mask", mask, persistent=False)
+
+ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
+ """
+ x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+ the text tokens
+ xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
+ the encoded audio features to be attended on
+ """
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
+ x = x.to(xa.dtype)
+
+ for block in self.blocks:
+ x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+
+ x = self.ln(x)
+ logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+ return logits
+
+
+class Whisper(nn.Module):
+ def __init__(self, dims: ModelDimensions):
+ super().__init__()
+ self.dims = dims
+ self.encoder = AudioEncoder(
+ self.dims.n_mels,
+ self.dims.n_audio_ctx,
+ self.dims.n_audio_state,
+ self.dims.n_audio_head,
+ self.dims.n_audio_layer,
+ )
+ self.decoder = TextDecoder(
+ self.dims.n_vocab,
+ self.dims.n_text_ctx,
+ self.dims.n_text_state,
+ self.dims.n_text_head,
+ self.dims.n_text_layer,
+ )
+
+ def embed_audio(self, mel: torch.Tensor):
+ return self.encoder(mel)
+
+ def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+ return self.decoder(tokens, audio_features)
+
+ def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
+ return self.decoder(tokens, self.encoder(mel))
+
+ @property
+ def device(self):
+ return next(self.parameters()).device
+
+ @property
+ def is_multilingual(self):
+ return self.dims.n_vocab == 51865
+
+ def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+ """
+ The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+ tensors calculated for the previous positions. This method returns a dictionary that stores
+ all caches, and the necessary hooks for the key and value projection modules that save the
+ intermediate tensors to be reused during later calculations.
+
+ Returns
+ -------
+ cache : Dict[nn.Module, torch.Tensor]
+ A dictionary object mapping the key/value projection modules to its cache
+ hooks : List[RemovableHandle]
+ List of PyTorch RemovableHandle objects to stop the hooks to be called
+ """
+ cache = {**cache} if cache is not None else {}
+ hooks = []
+
+ def save_to_cache(module, _, output):
+ if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
+ cache[module] = output # save as-is, for the first token or cross attention
+ else:
+ cache[module] = torch.cat([cache[module], output], dim=1).detach()
+ return cache[module]
+
+ def install_hooks(layer: nn.Module):
+ if isinstance(layer, MultiHeadAttention):
+ hooks.append(layer.key.register_forward_hook(save_to_cache))
+ hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+ self.decoder.apply(install_hooks)
+ return cache, hooks
+
+ detect_language = detect_language_function
+ decode = decode_function
diff --git a/whisper/normalizers/__init__.py b/whisper/normalizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b10d5a9d6389368fe7f3bac38e3b3cd9cc1470f
--- /dev/null
+++ b/whisper/normalizers/__init__.py
@@ -0,0 +1,2 @@
+from .basic import BasicTextNormalizer
+from .english import EnglishTextNormalizer
diff --git a/whisper/normalizers/basic.py b/whisper/normalizers/basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef8d249bfca2cd10bccdd56950504581e2598560
--- /dev/null
+++ b/whisper/normalizers/basic.py
@@ -0,0 +1,71 @@
+import re
+import unicodedata
+
+import regex
+
+# non-ASCII letters that are not separated by "NFKD" normalization
+ADDITIONAL_DIACRITICS = {
+ "œ": "oe",
+ "Œ": "OE",
+ "ø": "o",
+ "Ø": "O",
+ "æ": "ae",
+ "Æ": "AE",
+ "ß": "ss",
+ "ẞ": "SS",
+ "đ": "d",
+ "Đ": "D",
+ "ð": "d",
+ "Ð": "D",
+ "þ": "th",
+ "Þ": "th",
+ "ł": "l",
+ "Ł": "L",
+}
+
+
+def remove_symbols_and_diacritics(s: str, keep=""):
+ """
+ Replace any other markers, symbols, and punctuations with a space,
+ and drop any diacritics (category 'Mn' and some manual mappings)
+ """
+ return "".join(
+ c
+ if c in keep
+ else ADDITIONAL_DIACRITICS[c]
+ if c in ADDITIONAL_DIACRITICS
+ else ""
+ if unicodedata.category(c) == "Mn"
+ else " "
+ if unicodedata.category(c)[0] in "MSP"
+ else c
+ for c in unicodedata.normalize("NFKD", s)
+ )
+
+
+def remove_symbols(s: str):
+ """
+ Replace any other markers, symbols, punctuations with a space, keeping diacritics
+ """
+ return "".join(
+ " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
+ )
+
+
+class BasicTextNormalizer:
+ def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
+ self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
+ self.split_letters = split_letters
+
+ def __call__(self, s: str):
+ s = s.lower()
+ s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets
+ s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis
+ s = self.clean(s).lower()
+
+ if self.split_letters:
+ s = " ".join(regex.findall(r"\X", s, regex.U))
+
+ s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space
+
+ return s
diff --git a/whisper/normalizers/english.json b/whisper/normalizers/english.json
new file mode 100644
index 0000000000000000000000000000000000000000..74a1c3521d9af4a84a0aa65d95b0859138640799
--- /dev/null
+++ b/whisper/normalizers/english.json
@@ -0,0 +1,1741 @@
+{
+ "accessorise": "accessorize",
+ "accessorised": "accessorized",
+ "accessorises": "accessorizes",
+ "accessorising": "accessorizing",
+ "acclimatisation": "acclimatization",
+ "acclimatise": "acclimatize",
+ "acclimatised": "acclimatized",
+ "acclimatises": "acclimatizes",
+ "acclimatising": "acclimatizing",
+ "accoutrements": "accouterments",
+ "aeon": "eon",
+ "aeons": "eons",
+ "aerogramme": "aerogram",
+ "aerogrammes": "aerograms",
+ "aeroplane": "airplane",
+ "aeroplanes": "airplanes",
+ "aesthete": "esthete",
+ "aesthetes": "esthetes",
+ "aesthetic": "esthetic",
+ "aesthetically": "esthetically",
+ "aesthetics": "esthetics",
+ "aetiology": "etiology",
+ "ageing": "aging",
+ "aggrandisement": "aggrandizement",
+ "agonise": "agonize",
+ "agonised": "agonized",
+ "agonises": "agonizes",
+ "agonising": "agonizing",
+ "agonisingly": "agonizingly",
+ "almanack": "almanac",
+ "almanacks": "almanacs",
+ "aluminium": "aluminum",
+ "amortisable": "amortizable",
+ "amortisation": "amortization",
+ "amortisations": "amortizations",
+ "amortise": "amortize",
+ "amortised": "amortized",
+ "amortises": "amortizes",
+ "amortising": "amortizing",
+ "amphitheatre": "amphitheater",
+ "amphitheatres": "amphitheaters",
+ "anaemia": "anemia",
+ "anaemic": "anemic",
+ "anaesthesia": "anesthesia",
+ "anaesthetic": "anesthetic",
+ "anaesthetics": "anesthetics",
+ "anaesthetise": "anesthetize",
+ "anaesthetised": "anesthetized",
+ "anaesthetises": "anesthetizes",
+ "anaesthetising": "anesthetizing",
+ "anaesthetist": "anesthetist",
+ "anaesthetists": "anesthetists",
+ "anaesthetize": "anesthetize",
+ "anaesthetized": "anesthetized",
+ "anaesthetizes": "anesthetizes",
+ "anaesthetizing": "anesthetizing",
+ "analogue": "analog",
+ "analogues": "analogs",
+ "analyse": "analyze",
+ "analysed": "analyzed",
+ "analyses": "analyzes",
+ "analysing": "analyzing",
+ "anglicise": "anglicize",
+ "anglicised": "anglicized",
+ "anglicises": "anglicizes",
+ "anglicising": "anglicizing",
+ "annualised": "annualized",
+ "antagonise": "antagonize",
+ "antagonised": "antagonized",
+ "antagonises": "antagonizes",
+ "antagonising": "antagonizing",
+ "apologise": "apologize",
+ "apologised": "apologized",
+ "apologises": "apologizes",
+ "apologising": "apologizing",
+ "appal": "appall",
+ "appals": "appalls",
+ "appetiser": "appetizer",
+ "appetisers": "appetizers",
+ "appetising": "appetizing",
+ "appetisingly": "appetizingly",
+ "arbour": "arbor",
+ "arbours": "arbors",
+ "archeological": "archaeological",
+ "archaeologically": "archeologically",
+ "archaeologist": "archeologist",
+ "archaeologists": "archeologists",
+ "archaeology": "archeology</span>",
+ "ardour": "ardor",
+ "armour": "armor",
+ "armoured": "armored",
+ "armourer": "armorer",
+ "armourers": "armorers",
+ "armouries": "armories",
+ "armoury": "armory",
+ "artefact": "artifact",
+ "artefacts": "artifacts",
+ "authorise": "authorize",
+ "authorised": "authorized",
+ "authorises": "authorizes",
+ "authorising": "authorizing",
+ "axe": "ax",
+ "backpedalled": "backpedaled",
+ "backpedalling": "backpedaling",
+ "bannister": "banister",
+ "bannisters": "banisters",
+ "baptise": "baptize",
+ "baptised": "baptized",
+ "baptises": "baptizes",
+ "baptising": "baptizing",
+ "bastardise": "bastardize",
+ "bastardised": "bastardized",
+ "bastardises": "bastardizes",
+ "bastardising": "bastardizing",
+ "battleax": "battleaxe",
+ "baulk": "balk",
+ "baulked": "balked",
+ "baulking": "balking",
+ "baulks": "balks",
+ "bedevilled": "bedeviled",
+ "bedevilling": "bedeviling",
+ "behaviour": "behavior",
+ "behavioural": "behavioral",
+ "behaviourism": "behaviorism",
+ "behaviourist": "behaviorist",
+ "behaviourists": "behaviorists",
+ "behaviours": "behaviors",
+ "behove": "behoove",
+ "behoved": "behooved",
+ "behoves": "behooves",
+ "bejewelled": "bejeweled",
+ "belabour": "belabor",
+ "belaboured": "belabored",
+ "belabouring": "belaboring",
+ "belabours": "belabors",
+ "bevelled": "beveled",
+ "bevvies": "bevies",
+ "bevvy": "bevy",
+ "biassed": "biased",
+ "biassing": "biasing",
+ "bingeing": "binging",
+ "bougainvillaea": "bougainvillea",
+ "bougainvillaeas": "bougainvilleas",
+ "bowdlerise": "bowdlerize",
+ "bowdlerised": "bowdlerized",
+ "bowdlerises": "bowdlerizes",
+ "bowdlerising": "bowdlerizing",
+ "breathalyse": "breathalyze",
+ "breathalysed": "breathalyzed",
+ "breathalyser": "breathalyzer",
+ "breathalysers": "breathalyzers",
+ "breathalyses": "breathalyzes",
+ "breathalysing": "breathalyzing",
+ "brutalise": "brutalize",
+ "brutalised": "brutalized",
+ "brutalises": "brutalizes",
+ "brutalising": "brutalizing",
+ "busses": "buses",
+ "bussing": "busing",
+ "caesarean": "cesarean",
+ "caesareans": "cesareans",
+ "calibre": "caliber",
+ "calibres": "calibers",
+ "calliper": "caliper",
+ "callipers": "calipers",
+ "callisthenics": "calisthenics",
+ "canalise": "canalize",
+ "canalised": "canalized",
+ "canalises": "canalizes",
+ "canalising": "canalizing",
+ "cancelation": "cancellation",
+ "cancelations": "cancellations",
+ "cancelled": "canceled",
+ "cancelling": "canceling",
+ "candour": "candor",
+ "cannibalise": "cannibalize",
+ "cannibalised": "cannibalized",
+ "cannibalises": "cannibalizes",
+ "cannibalising": "cannibalizing",
+ "canonise": "canonize",
+ "canonised": "canonized",
+ "canonises": "canonizes",
+ "canonising": "canonizing",
+ "capitalise": "capitalize",
+ "capitalised": "capitalized",
+ "capitalises": "capitalizes",
+ "capitalising": "capitalizing",
+ "caramelise": "caramelize",
+ "caramelised": "caramelized",
+ "caramelises": "caramelizes",
+ "caramelising": "caramelizing",
+ "carbonise": "carbonize",
+ "carbonised": "carbonized",
+ "carbonises": "carbonizes",
+ "carbonising": "carbonizing",
+ "carolled": "caroled",
+ "carolling": "caroling",
+ "catalogue": "catalog",
+ "catalogued": "cataloged",
+ "catalogues": "catalogs",
+ "cataloguing": "cataloging",
+ "catalyse": "catalyze",
+ "catalysed": "catalyzed",
+ "catalyses": "catalyzes",
+ "catalysing": "catalyzing",
+ "categorise": "categorize",
+ "categorised": "categorized",
+ "categorises": "categorizes",
+ "categorising": "categorizing",
+ "cauterise": "cauterize",
+ "cauterised": "cauterized",
+ "cauterises": "cauterizes",
+ "cauterising": "cauterizing",
+ "cavilled": "caviled",
+ "cavilling": "caviling",
+ "centigramme": "centigram",
+ "centigrammes": "centigrams",
+ "centilitre": "centiliter",
+ "centilitres": "centiliters",
+ "centimetre": "centimeter",
+ "centimetres": "centimeters",
+ "centralise": "centralize",
+ "centralised": "centralized",
+ "centralises": "centralizes",
+ "centralising": "centralizing",
+ "centre": "center",
+ "centred": "centered",
+ "centrefold": "centerfold",
+ "centrefolds": "centerfolds",
+ "centrepiece": "centerpiece",
+ "centrepieces": "centerpieces",
+ "centres": "centers",
+ "channelled": "channeled",
+ "channelling": "channeling",
+ "characterise": "characterize",
+ "characterised": "characterized",
+ "characterises": "characterizes",
+ "characterising": "characterizing",
+ "cheque": "check",
+ "chequebook": "checkbook",
+ "chequebooks": "checkbooks",
+ "chequered": "checkered",
+ "cheques": "checks",
+ "chilli": "chili",
+ "chimaera": "chimera",
+ "chimaeras": "chimeras",
+ "chiselled": "chiseled",
+ "chiselling": "chiseling",
+ "circularise": "circularize",
+ "circularised": "circularized",
+ "circularises": "circularizes",
+ "circularising": "circularizing",
+ "civilise": "civilize",
+ "civilised": "civilized",
+ "civilises": "civilizes",
+ "civilising": "civilizing",
+ "clamour": "clamor",
+ "clamoured": "clamored",
+ "clamouring": "clamoring",
+ "clamours": "clamors",
+ "clangour": "clangor",
+ "clarinettist": "clarinetist",
+ "clarinettists": "clarinetists",
+ "collectivise": "collectivize",
+ "collectivised": "collectivized",
+ "collectivises": "collectivizes",
+ "collectivising": "collectivizing",
+ "colonisation": "colonization",
+ "colonise": "colonize",
+ "colonised": "colonized",
+ "coloniser": "colonizer",
+ "colonisers": "colonizers",
+ "colonises": "colonizes",
+ "colonising": "colonizing",
+ "colour": "color",
+ "colourant": "colorant",
+ "colourants": "colorants",
+ "coloured": "colored",
+ "coloureds": "coloreds",
+ "colourful": "colorful",
+ "colourfully": "colorfully",
+ "colouring": "coloring",
+ "colourize": "colorize",
+ "colourized": "colorized",
+ "colourizes": "colorizes",
+ "colourizing": "colorizing",
+ "colourless": "colorless",
+ "colours": "colors",
+ "commercialise": "commercialize",
+ "commercialised": "commercialized",
+ "commercialises": "commercializes",
+ "commercialising": "commercializing",
+ "compartmentalise": "compartmentalize",
+ "compartmentalised": "compartmentalized",
+ "compartmentalises": "compartmentalizes",
+ "compartmentalising": "compartmentalizing",
+ "computerise": "computerize",
+ "computerised": "computerized",
+ "computerises": "computerizes",
+ "computerising": "computerizing",
+ "conceptualise": "conceptualize",
+ "conceptualised": "conceptualized",
+ "conceptualises": "conceptualizes",
+ "conceptualising": "conceptualizing",
+ "connexion": "connection",
+ "connexions": "connections",
+ "contextualise": "contextualize",
+ "contextualised": "contextualized",
+ "contextualises": "contextualizes",
+ "contextualising": "contextualizing",
+ "cosier": "cozier",
+ "cosies": "cozies",
+ "cosiest": "coziest",
+ "cosily": "cozily",
+ "cosiness": "coziness",
+ "cosy": "cozy",
+ "councillor": "councilor",
+ "councillors": "councilors",
+ "counselled": "counseled",
+ "counselling": "counseling",
+ "counsellor": "counselor",
+ "counsellors": "counselors",
+ "crenelated": "crenellated",
+ "criminalise": "criminalize",
+ "criminalised": "criminalized",
+ "criminalises": "criminalizes",
+ "criminalising": "criminalizing",
+ "criticise": "criticize",
+ "criticised": "criticized",
+ "criticises": "criticizes",
+ "criticising": "criticizing",
+ "crueller": "crueler",
+ "cruellest": "cruelest",
+ "crystallisation": "crystallization",
+ "crystallise": "crystallize",
+ "crystallised": "crystallized",
+ "crystallises": "crystallizes",
+ "crystallising": "crystallizing",
+ "cudgelled": "cudgeled",
+ "cudgelling": "cudgeling",
+ "customise": "customize",
+ "customised": "customized",
+ "customises": "customizes",
+ "customising": "customizing",
+ "cypher": "cipher",
+ "cyphers": "ciphers",
+ "decentralisation": "decentralization",
+ "decentralise": "decentralize",
+ "decentralised": "decentralized",
+ "decentralises": "decentralizes",
+ "decentralising": "decentralizing",
+ "decriminalisation": "decriminalization",
+ "decriminalise": "decriminalize",
+ "decriminalised": "decriminalized",
+ "decriminalises": "decriminalizes",
+ "decriminalising": "decriminalizing",
+ "defence": "defense",
+ "defenceless": "defenseless",
+ "defences": "defenses",
+ "dehumanisation": "dehumanization",
+ "dehumanise": "dehumanize",
+ "dehumanised": "dehumanized",
+ "dehumanises": "dehumanizes",
+ "dehumanising": "dehumanizing",
+ "demeanour": "demeanor",
+ "demilitarisation": "demilitarization",
+ "demilitarise": "demilitarize",
+ "demilitarised": "demilitarized",
+ "demilitarises": "demilitarizes",
+ "demilitarising": "demilitarizing",
+ "demobilisation": "demobilization",
+ "demobilise": "demobilize",
+ "demobilised": "demobilized",
+ "demobilises": "demobilizes",
+ "demobilising": "demobilizing",
+ "democratisation": "democratization",
+ "democratise": "democratize",
+ "democratised": "democratized",
+ "democratises": "democratizes",
+ "democratising": "democratizing",
+ "demonise": "demonize",
+ "demonised": "demonized",
+ "demonises": "demonizes",
+ "demonising": "demonizing",
+ "demoralisation": "demoralization",
+ "demoralise": "demoralize",
+ "demoralised": "demoralized",
+ "demoralises": "demoralizes",
+ "demoralising": "demoralizing",
+ "denationalisation": "denationalization",
+ "denationalise": "denationalize",
+ "denationalised": "denationalized",
+ "denationalises": "denationalizes",
+ "denationalising": "denationalizing",
+ "deodorise": "deodorize",
+ "deodorised": "deodorized",
+ "deodorises": "deodorizes",
+ "deodorising": "deodorizing",
+ "depersonalise": "depersonalize",
+ "depersonalised": "depersonalized",
+ "depersonalises": "depersonalizes",
+ "depersonalising": "depersonalizing",
+ "deputise": "deputize",
+ "deputised": "deputized",
+ "deputises": "deputizes",
+ "deputising": "deputizing",
+ "desensitisation": "desensitization",
+ "desensitise": "desensitize",
+ "desensitised": "desensitized",
+ "desensitises": "desensitizes",
+ "desensitising": "desensitizing",
+ "destabilisation": "destabilization",
+ "destabilise": "destabilize",
+ "destabilised": "destabilized",
+ "destabilises": "destabilizes",
+ "destabilising": "destabilizing",
+ "dialled": "dialed",
+ "dialling": "dialing",
+ "dialogue": "dialog",
+ "dialogues": "dialogs",
+ "diarrhoea": "diarrhea",
+ "digitise": "digitize",
+ "digitised": "digitized",
+ "digitises": "digitizes",
+ "digitising": "digitizing",
+ "disc": "disk",
+ "discolour": "discolor",
+ "discoloured": "discolored",
+ "discolouring": "discoloring",
+ "discolours": "discolors",
+ "discs": "disks",
+ "disembowelled": "disemboweled",
+ "disembowelling": "disemboweling",
+ "disfavour": "disfavor",
+ "dishevelled": "disheveled",
+ "dishonour": "dishonor",
+ "dishonourable": "dishonorable",
+ "dishonourably": "dishonorably",
+ "dishonoured": "dishonored",
+ "dishonouring": "dishonoring",
+ "dishonours": "dishonors",
+ "disorganisation": "disorganization",
+ "disorganised": "disorganized",
+ "distil": "distill",
+ "distils": "distills",
+ "dramatisation": "dramatization",
+ "dramatisations": "dramatizations",
+ "dramatise": "dramatize",
+ "dramatised": "dramatized",
+ "dramatises": "dramatizes",
+ "dramatising": "dramatizing",
+ "draught": "draft",
+ "draughtboard": "draftboard",
+ "draughtboards": "draftboards",
+ "draughtier": "draftier",
+ "draughtiest": "draftiest",
+ "draughts": "drafts",
+ "draughtsman": "draftsman",
+ "draughtsmanship": "draftsmanship",
+ "draughtsmen": "draftsmen",
+ "draughtswoman": "draftswoman",
+ "draughtswomen": "draftswomen",
+ "draughty": "drafty",
+ "drivelled": "driveled",
+ "drivelling": "driveling",
+ "duelled": "dueled",
+ "duelling": "dueling",
+ "economise": "economize",
+ "economised": "economized",
+ "economises": "economizes",
+ "economising": "economizing",
+ "edoema": "edema",
+ "editorialise": "editorialize",
+ "editorialised": "editorialized",
+ "editorialises": "editorializes",
+ "editorialising": "editorializing",
+ "empathise": "empathize",
+ "empathised": "empathized",
+ "empathises": "empathizes",
+ "empathising": "empathizing",
+ "emphasise": "emphasize",
+ "emphasised": "emphasized",
+ "emphasises": "emphasizes",
+ "emphasising": "emphasizing",
+ "enamelled": "enameled",
+ "enamelling": "enameling",
+ "enamoured": "enamored",
+ "encyclopaedia": "encyclopedia",
+ "encyclopaedias": "encyclopedias",
+ "encyclopaedic": "encyclopedic",
+ "endeavour": "endeavor",
+ "endeavoured": "endeavored",
+ "endeavouring": "endeavoring",
+ "endeavours": "endeavors",
+ "energise": "energize",
+ "energised": "energized",
+ "energises": "energizes",
+ "energising": "energizing",
+ "enrol": "enroll",
+ "enrols": "enrolls",
+ "enthral": "enthrall",
+ "enthrals": "enthralls",
+ "epaulette": "epaulet",
+ "epaulettes": "epaulets",
+ "epicentre": "epicenter",
+ "epicentres": "epicenters",
+ "epilogue": "epilog",
+ "epilogues": "epilogs",
+ "epitomise": "epitomize",
+ "epitomised": "epitomized",
+ "epitomises": "epitomizes",
+ "epitomising": "epitomizing",
+ "equalisation": "equalization",
+ "equalise": "equalize",
+ "equalised": "equalized",
+ "equaliser": "equalizer",
+ "equalisers": "equalizers",
+ "equalises": "equalizes",
+ "equalising": "equalizing",
+ "eulogise": "eulogize",
+ "eulogised": "eulogized",
+ "eulogises": "eulogizes",
+ "eulogising": "eulogizing",
+ "evangelise": "evangelize",
+ "evangelised": "evangelized",
+ "evangelises": "evangelizes",
+ "evangelising": "evangelizing",
+ "exorcise": "exorcize",
+ "exorcised": "exorcized",
+ "exorcises": "exorcizes",
+ "exorcising": "exorcizing",
+ "extemporisation": "extemporization",
+ "extemporise": "extemporize",
+ "extemporised": "extemporized",
+ "extemporises": "extemporizes",
+ "extemporising": "extemporizing",
+ "externalisation": "externalization",
+ "externalisations": "externalizations",
+ "externalise": "externalize",
+ "externalised": "externalized",
+ "externalises": "externalizes",
+ "externalising": "externalizing",
+ "factorise": "factorize",
+ "factorised": "factorized",
+ "factorises": "factorizes",
+ "factorising": "factorizing",
+ "faecal": "fecal",
+ "faeces": "feces",
+ "familiarisation": "familiarization",
+ "familiarise": "familiarize",
+ "familiarised": "familiarized",
+ "familiarises": "familiarizes",
+ "familiarising": "familiarizing",
+ "fantasise": "fantasize",
+ "fantasised": "fantasized",
+ "fantasises": "fantasizes",
+ "fantasising": "fantasizing",
+ "favour": "favor",
+ "favourable": "favorable",
+ "favourably": "favorably",
+ "favoured": "favored",
+ "favouring": "favoring",
+ "favourite": "favorite",
+ "favourites": "favorites",
+ "favouritism": "favoritism",
+ "favours": "favors",
+ "feminise": "feminize",
+ "feminised": "feminized",
+ "feminises": "feminizes",
+ "feminising": "feminizing",
+ "fertilisation": "fertilization",
+ "fertilise": "fertilize",
+ "fertilised": "fertilized",
+ "fertiliser": "fertilizer",
+ "fertilisers": "fertilizers",
+ "fertilises": "fertilizes",
+ "fertilising": "fertilizing",
+ "fervour": "fervor",
+ "fibre": "fiber",
+ "fibreglass": "fiberglass",
+ "fibres": "fibers",
+ "fictionalisation": "fictionalization",
+ "fictionalisations": "fictionalizations",
+ "fictionalise": "fictionalize",
+ "fictionalised": "fictionalized",
+ "fictionalises": "fictionalizes",
+ "fictionalising": "fictionalizing",
+ "fillet": "filet",
+ "filleted": "fileted",
+ "filleting": "fileting",
+ "fillets": "filets",
+ "finalisation": "finalization",
+ "finalise": "finalize",
+ "finalised": "finalized",
+ "finalises": "finalizes",
+ "finalising": "finalizing",
+ "flautist": "flutist",
+ "flautists": "flutists",
+ "flavour": "flavor",
+ "flavoured": "flavored",
+ "flavouring": "flavoring",
+ "flavourings": "flavorings",
+ "flavourless": "flavorless",
+ "flavours": "flavors",
+ "flavoursome": "flavorsome",
+ "flyer / flier": "flier / flyer",
+ "foetal": "fetal",
+ "foetid": "fetid",
+ "foetus": "fetus",
+ "foetuses": "fetuses",
+ "formalisation": "formalization",
+ "formalise": "formalize",
+ "formalised": "formalized",
+ "formalises": "formalizes",
+ "formalising": "formalizing",
+ "fossilisation": "fossilization",
+ "fossilise": "fossilize",
+ "fossilised": "fossilized",
+ "fossilises": "fossilizes",
+ "fossilising": "fossilizing",
+ "fraternisation": "fraternization",
+ "fraternise": "fraternize",
+ "fraternised": "fraternized",
+ "fraternises": "fraternizes",
+ "fraternising": "fraternizing",
+ "fulfil": "fulfill",
+ "fulfilment": "fulfillment",
+ "fulfils": "fulfills",
+ "funnelled": "funneled",
+ "funnelling": "funneling",
+ "galvanise": "galvanize",
+ "galvanised": "galvanized",
+ "galvanises": "galvanizes",
+ "galvanising": "galvanizing",
+ "gambolled": "gamboled",
+ "gambolling": "gamboling",
+ "gaol": "jail",
+ "gaolbird": "jailbird",
+ "gaolbirds": "jailbirds",
+ "gaolbreak": "jailbreak",
+ "gaolbreaks": "jailbreaks",
+ "gaoled": "jailed",
+ "gaoler": "jailer",
+ "gaolers": "jailers",
+ "gaoling": "jailing",
+ "gaols": "jails",
+ "gasses": "gases",
+ "gage": "gauge",
+ "gaged": "gauged",
+ "gages": "gauges",
+ "gaging": "gauging",
+ "generalisation": "generalization",
+ "generalisations": "generalizations",
+ "generalise": "generalize",
+ "generalised": "generalized",
+ "generalises": "generalizes",
+ "generalising": "generalizing",
+ "ghettoise": "ghettoize",
+ "ghettoised": "ghettoized",
+ "ghettoises": "ghettoizes",
+ "ghettoising": "ghettoizing",
+ "gipsies": "gypsies",
+ "glamorise": "glamorize",
+ "glamorised": "glamorized",
+ "glamorises": "glamorizes",
+ "glamorising": "glamorizing",
+ "glamor": "glamour",
+ "globalisation": "globalization",
+ "globalise": "globalize",
+ "globalised": "globalized",
+ "globalises": "globalizes",
+ "globalising": "globalizing",
+ "glueing": "gluing",
+ "goitre": "goiter",
+ "goitres": "goiters",
+ "gonorrhoea": "gonorrhea",
+ "gramme": "gram",
+ "grammes": "grams",
+ "gravelled": "graveled",
+ "grey": "gray",
+ "greyed": "grayed",
+ "greying": "graying",
+ "greyish": "grayish",
+ "greyness": "grayness",
+ "greys": "grays",
+ "grovelled": "groveled",
+ "grovelling": "groveling",
+ "groyne": "groin",
+ "groynes": "groins",
+ "gruelling": "grueling",
+ "gruellingly": "gruelingly",
+ "gryphon": "griffin",
+ "gryphons": "griffins",
+ "gynaecological": "gynecological",
+ "gynaecologist": "gynecologist",
+ "gynaecologists": "gynecologists",
+ "gynaecology": "gynecology",
+ "haematological": "hematological",
+ "haematologist": "hematologist",
+ "haematologists": "hematologists",
+ "haematology": "hematology",
+ "haemoglobin": "hemoglobin",
+ "haemophilia": "hemophilia",
+ "haemophiliac": "hemophiliac",
+ "haemophiliacs": "hemophiliacs",
+ "haemorrhage": "hemorrhage",
+ "haemorrhaged": "hemorrhaged",
+ "haemorrhages": "hemorrhages",
+ "haemorrhaging": "hemorrhaging",
+ "haemorrhoids": "hemorrhoids",
+ "harbour": "harbor",
+ "harboured": "harbored",
+ "harbouring": "harboring",
+ "harbours": "harbors",
+ "harmonisation": "harmonization",
+ "harmonise": "harmonize",
+ "harmonised": "harmonized",
+ "harmonises": "harmonizes",
+ "harmonising": "harmonizing",
+ "homoeopath": "homeopath",
+ "homoeopathic": "homeopathic",
+ "homoeopaths": "homeopaths",
+ "homoeopathy": "homeopathy",
+ "homogenise": "homogenize",
+ "homogenised": "homogenized",
+ "homogenises": "homogenizes",
+ "homogenising": "homogenizing",
+ "honour": "honor",
+ "honourable": "honorable",
+ "honourably": "honorably",
+ "honoured": "honored",
+ "honouring": "honoring",
+ "honours": "honors",
+ "hospitalisation": "hospitalization",
+ "hospitalise": "hospitalize",
+ "hospitalised": "hospitalized",
+ "hospitalises": "hospitalizes",
+ "hospitalising": "hospitalizing",
+ "humanise": "humanize",
+ "humanised": "humanized",
+ "humanises": "humanizes",
+ "humanising": "humanizing",
+ "humour": "humor",
+ "humoured": "humored",
+ "humouring": "humoring",
+ "humourless": "humorless",
+ "humours": "humors",
+ "hybridise": "hybridize",
+ "hybridised": "hybridized",
+ "hybridises": "hybridizes",
+ "hybridising": "hybridizing",
+ "hypnotise": "hypnotize",
+ "hypnotised": "hypnotized",
+ "hypnotises": "hypnotizes",
+ "hypnotising": "hypnotizing",
+ "hypothesise": "hypothesize",
+ "hypothesised": "hypothesized",
+ "hypothesises": "hypothesizes",
+ "hypothesising": "hypothesizing",
+ "idealisation": "idealization",
+ "idealise": "idealize",
+ "idealised": "idealized",
+ "idealises": "idealizes",
+ "idealising": "idealizing",
+ "idolise": "idolize",
+ "idolised": "idolized",
+ "idolises": "idolizes",
+ "idolising": "idolizing",
+ "immobilisation": "immobilization",
+ "immobilise": "immobilize",
+ "immobilised": "immobilized",
+ "immobiliser": "immobilizer",
+ "immobilisers": "immobilizers",
+ "immobilises": "immobilizes",
+ "immobilising": "immobilizing",
+ "immortalise": "immortalize",
+ "immortalised": "immortalized",
+ "immortalises": "immortalizes",
+ "immortalising": "immortalizing",
+ "immunisation": "immunization",
+ "immunise": "immunize",
+ "immunised": "immunized",
+ "immunises": "immunizes",
+ "immunising": "immunizing",
+ "impanelled": "impaneled",
+ "impanelling": "impaneling",
+ "imperilled": "imperiled",
+ "imperilling": "imperiling",
+ "individualise": "individualize",
+ "individualised": "individualized",
+ "individualises": "individualizes",
+ "individualising": "individualizing",
+ "industrialise": "industrialize",
+ "industrialised": "industrialized",
+ "industrialises": "industrializes",
+ "industrialising": "industrializing",
+ "inflexion": "inflection",
+ "inflexions": "inflections",
+ "initialise": "initialize",
+ "initialised": "initialized",
+ "initialises": "initializes",
+ "initialising": "initializing",
+ "initialled": "initialed",
+ "initialling": "initialing",
+ "instal": "install",
+ "instalment": "installment",
+ "instalments": "installments",
+ "instals": "installs",
+ "instil": "instill",
+ "instils": "instills",
+ "institutionalisation": "institutionalization",
+ "institutionalise": "institutionalize",
+ "institutionalised": "institutionalized",
+ "institutionalises": "institutionalizes",
+ "institutionalising": "institutionalizing",
+ "intellectualise": "intellectualize",
+ "intellectualised": "intellectualized",
+ "intellectualises": "intellectualizes",
+ "intellectualising": "intellectualizing",
+ "internalisation": "internalization",
+ "internalise": "internalize",
+ "internalised": "internalized",
+ "internalises": "internalizes",
+ "internalising": "internalizing",
+ "internationalisation": "internationalization",
+ "internationalise": "internationalize",
+ "internationalised": "internationalized",
+ "internationalises": "internationalizes",
+ "internationalising": "internationalizing",
+ "ionisation": "ionization",
+ "ionise": "ionize",
+ "ionised": "ionized",
+ "ioniser": "ionizer",
+ "ionisers": "ionizers",
+ "ionises": "ionizes",
+ "ionising": "ionizing",
+ "italicise": "italicize",
+ "italicised": "italicized",
+ "italicises": "italicizes",
+ "italicising": "italicizing",
+ "itemise": "itemize",
+ "itemised": "itemized",
+ "itemises": "itemizes",
+ "itemising": "itemizing",
+ "jeopardise": "jeopardize",
+ "jeopardised": "jeopardized",
+ "jeopardises": "jeopardizes",
+ "jeopardising": "jeopardizing",
+ "jewelled": "jeweled",
+ "jeweller": "jeweler",
+ "jewellers": "jewelers",
+ "jewellery": "jewelry",
+ "judgement": "judgment",
+ "kilogramme": "kilogram",
+ "kilogrammes": "kilograms",
+ "kilometre": "kilometer",
+ "kilometres": "kilometers",
+ "labelled": "labeled",
+ "labelling": "labeling",
+ "labour": "labor",
+ "laboured": "labored",
+ "labourer": "laborer",
+ "labourers": "laborers",
+ "labouring": "laboring",
+ "labours": "labors",
+ "lacklustre": "lackluster",
+ "legalisation": "legalization",
+ "legalise": "legalize",
+ "legalised": "legalized",
+ "legalises": "legalizes",
+ "legalising": "legalizing",
+ "legitimise": "legitimize",
+ "legitimised": "legitimized",
+ "legitimises": "legitimizes",
+ "legitimising": "legitimizing",
+ "leukaemia": "leukemia",
+ "levelled": "leveled",
+ "leveller": "leveler",
+ "levellers": "levelers",
+ "levelling": "leveling",
+ "libelled": "libeled",
+ "libelling": "libeling",
+ "libellous": "libelous",
+ "liberalisation": "liberalization",
+ "liberalise": "liberalize",
+ "liberalised": "liberalized",
+ "liberalises": "liberalizes",
+ "liberalising": "liberalizing",
+ "licence": "license",
+ "licenced": "licensed",
+ "licences": "licenses",
+ "licencing": "licensing",
+ "likeable": "likable",
+ "lionisation": "lionization",
+ "lionise": "lionize",
+ "lionised": "lionized",
+ "lionises": "lionizes",
+ "lionising": "lionizing",
+ "liquidise": "liquidize",
+ "liquidised": "liquidized",
+ "liquidiser": "liquidizer",
+ "liquidisers": "liquidizers",
+ "liquidises": "liquidizes",
+ "liquidising": "liquidizing",
+ "litre": "liter",
+ "litres": "liters",
+ "localise": "localize",
+ "localised": "localized",
+ "localises": "localizes",
+ "localising": "localizing",
+ "louvre": "louver",
+ "louvred": "louvered",
+ "louvres": "louvers",
+ "lustre": "luster",
+ "magnetise": "magnetize",
+ "magnetised": "magnetized",
+ "magnetises": "magnetizes",
+ "magnetising": "magnetizing",
+ "manoeuvrability": "maneuverability",
+ "manoeuvrable": "maneuverable",
+ "manoeuvre": "maneuver",
+ "manoeuvred": "maneuvered",
+ "manoeuvres": "maneuvers",
+ "manoeuvring": "maneuvering",
+ "manoeuvrings": "maneuverings",
+ "marginalisation": "marginalization",
+ "marginalise": "marginalize",
+ "marginalised": "marginalized",
+ "marginalises": "marginalizes",
+ "marginalising": "marginalizing",
+ "marshalled": "marshaled",
+ "marshalling": "marshaling",
+ "marvelled": "marveled",
+ "marvelling": "marveling",
+ "marvellous": "marvelous",
+ "marvellously": "marvelously",
+ "materialisation": "materialization",
+ "materialise": "materialize",
+ "materialised": "materialized",
+ "materialises": "materializes",
+ "materialising": "materializing",
+ "maximisation": "maximization",
+ "maximise": "maximize",
+ "maximised": "maximized",
+ "maximises": "maximizes",
+ "maximising": "maximizing",
+ "meagre": "meager",
+ "mechanisation": "mechanization",
+ "mechanise": "mechanize",
+ "mechanised": "mechanized",
+ "mechanises": "mechanizes",
+ "mechanising": "mechanizing",
+ "mediaeval": "medieval",
+ "memorialise": "memorialize",
+ "memorialised": "memorialized",
+ "memorialises": "memorializes",
+ "memorialising": "memorializing",
+ "memorise": "memorize",
+ "memorised": "memorized",
+ "memorises": "memorizes",
+ "memorising": "memorizing",
+ "mesmerise": "mesmerize",
+ "mesmerised": "mesmerized",
+ "mesmerises": "mesmerizes",
+ "mesmerising": "mesmerizing",
+ "metabolise": "metabolize",
+ "metabolised": "metabolized",
+ "metabolises": "metabolizes",
+ "metabolising": "metabolizing",
+ "metre": "meter",
+ "metres": "meters",
+ "micrometre": "micrometer",
+ "micrometres": "micrometers",
+ "militarise": "militarize",
+ "militarised": "militarized",
+ "militarises": "militarizes",
+ "militarising": "militarizing",
+ "milligramme": "milligram",
+ "milligrammes": "milligrams",
+ "millilitre": "milliliter",
+ "millilitres": "milliliters",
+ "millimetre": "millimeter",
+ "millimetres": "millimeters",
+ "miniaturisation": "miniaturization",
+ "miniaturise": "miniaturize",
+ "miniaturised": "miniaturized",
+ "miniaturises": "miniaturizes",
+ "miniaturising": "miniaturizing",
+ "minibusses": "minibuses",
+ "minimise": "minimize",
+ "minimised": "minimized",
+ "minimises": "minimizes",
+ "minimising": "minimizing",
+ "misbehaviour": "misbehavior",
+ "misdemeanour": "misdemeanor",
+ "misdemeanours": "misdemeanors",
+ "misspelt": "misspelled",
+ "mitre": "miter",
+ "mitres": "miters",
+ "mobilisation": "mobilization",
+ "mobilise": "mobilize",
+ "mobilised": "mobilized",
+ "mobilises": "mobilizes",
+ "mobilising": "mobilizing",
+ "modelled": "modeled",
+ "modeller": "modeler",
+ "modellers": "modelers",
+ "modelling": "modeling",
+ "modernise": "modernize",
+ "modernised": "modernized",
+ "modernises": "modernizes",
+ "modernising": "modernizing",
+ "moisturise": "moisturize",
+ "moisturised": "moisturized",
+ "moisturiser": "moisturizer",
+ "moisturisers": "moisturizers",
+ "moisturises": "moisturizes",
+ "moisturising": "moisturizing",
+ "monologue": "monolog",
+ "monologues": "monologs",
+ "monopolisation": "monopolization",
+ "monopolise": "monopolize",
+ "monopolised": "monopolized",
+ "monopolises": "monopolizes",
+ "monopolising": "monopolizing",
+ "moralise": "moralize",
+ "moralised": "moralized",
+ "moralises": "moralizes",
+ "moralising": "moralizing",
+ "motorised": "motorized",
+ "mould": "mold",
+ "moulded": "molded",
+ "moulder": "molder",
+ "mouldered": "moldered",
+ "mouldering": "moldering",
+ "moulders": "molders",
+ "mouldier": "moldier",
+ "mouldiest": "moldiest",
+ "moulding": "molding",
+ "mouldings": "moldings",
+ "moulds": "molds",
+ "mouldy": "moldy",
+ "moult": "molt",
+ "moulted": "molted",
+ "moulting": "molting",
+ "moults": "molts",
+ "moustache": "mustache",
+ "moustached": "mustached",
+ "moustaches": "mustaches",
+ "moustachioed": "mustachioed",
+ "multicoloured": "multicolored",
+ "nationalisation": "nationalization",
+ "nationalisations": "nationalizations",
+ "nationalise": "nationalize",
+ "nationalised": "nationalized",
+ "nationalises": "nationalizes",
+ "nationalising": "nationalizing",
+ "naturalisation": "naturalization",
+ "naturalise": "naturalize",
+ "naturalised": "naturalized",
+ "naturalises": "naturalizes",
+ "naturalising": "naturalizing",
+ "neighbour": "neighbor",
+ "neighbourhood": "neighborhood",
+ "neighbourhoods": "neighborhoods",
+ "neighbouring": "neighboring",
+ "neighbourliness": "neighborliness",
+ "neighbourly": "neighborly",
+ "neighbours": "neighbors",
+ "neutralisation": "neutralization",
+ "neutralise": "neutralize",
+ "neutralised": "neutralized",
+ "neutralises": "neutralizes",
+ "neutralising": "neutralizing",
+ "normalisation": "normalization",
+ "normalise": "normalize",
+ "normalised": "normalized",
+ "normalises": "normalizes",
+ "normalising": "normalizing",
+ "odour": "odor",
+ "odourless": "odorless",
+ "odours": "odors",
+ "oesophagus": "esophagus",
+ "oesophaguses": "esophaguses",
+ "oestrogen": "estrogen",
+ "offence": "offense",
+ "offences": "offenses",
+ "omelette": "omelet",
+ "omelettes": "omelets",
+ "optimise": "optimize",
+ "optimised": "optimized",
+ "optimises": "optimizes",
+ "optimising": "optimizing",
+ "organisation": "organization",
+ "organisational": "organizational",
+ "organisations": "organizations",
+ "organise": "organize",
+ "organised": "organized",
+ "organiser": "organizer",
+ "organisers": "organizers",
+ "organises": "organizes",
+ "organising": "organizing",
+ "orthopaedic": "orthopedic",
+ "orthopaedics": "orthopedics",
+ "ostracise": "ostracize",
+ "ostracised": "ostracized",
+ "ostracises": "ostracizes",
+ "ostracising": "ostracizing",
+ "outmanoeuvre": "outmaneuver",
+ "outmanoeuvred": "outmaneuvered",
+ "outmanoeuvres": "outmaneuvers",
+ "outmanoeuvring": "outmaneuvering",
+ "overemphasise": "overemphasize",
+ "overemphasised": "overemphasized",
+ "overemphasises": "overemphasizes",
+ "overemphasising": "overemphasizing",
+ "oxidisation": "oxidization",
+ "oxidise": "oxidize",
+ "oxidised": "oxidized",
+ "oxidises": "oxidizes",
+ "oxidising": "oxidizing",
+ "paederast": "pederast",
+ "paederasts": "pederasts",
+ "paediatric": "pediatric",
+ "paediatrician": "pediatrician",
+ "paediatricians": "pediatricians",
+ "paediatrics": "pediatrics",
+ "paedophile": "pedophile",
+ "paedophiles": "pedophiles",
+ "paedophilia": "pedophilia",
+ "palaeolithic": "paleolithic",
+ "palaeontologist": "paleontologist",
+ "palaeontologists": "paleontologists",
+ "palaeontology": "paleontology",
+ "panelled": "paneled",
+ "panelling": "paneling",
+ "panellist": "panelist",
+ "panellists": "panelists",
+ "paralyse": "paralyze",
+ "paralysed": "paralyzed",
+ "paralyses": "paralyzes",
+ "paralysing": "paralyzing",
+ "parcelled": "parceled",
+ "parcelling": "parceling",
+ "parlour": "parlor",
+ "parlours": "parlors",
+ "particularise": "particularize",
+ "particularised": "particularized",
+ "particularises": "particularizes",
+ "particularising": "particularizing",
+ "passivisation": "passivization",
+ "passivise": "passivize",
+ "passivised": "passivized",
+ "passivises": "passivizes",
+ "passivising": "passivizing",
+ "pasteurisation": "pasteurization",
+ "pasteurise": "pasteurize",
+ "pasteurised": "pasteurized",
+ "pasteurises": "pasteurizes",
+ "pasteurising": "pasteurizing",
+ "patronise": "patronize",
+ "patronised": "patronized",
+ "patronises": "patronizes",
+ "patronising": "patronizing",
+ "patronisingly": "patronizingly",
+ "pedalled": "pedaled",
+ "pedalling": "pedaling",
+ "pedestrianisation": "pedestrianization",
+ "pedestrianise": "pedestrianize",
+ "pedestrianised": "pedestrianized",
+ "pedestrianises": "pedestrianizes",
+ "pedestrianising": "pedestrianizing",
+ "penalise": "penalize",
+ "penalised": "penalized",
+ "penalises": "penalizes",
+ "penalising": "penalizing",
+ "pencilled": "penciled",
+ "pencilling": "penciling",
+ "personalise": "personalize",
+ "personalised": "personalized",
+ "personalises": "personalizes",
+ "personalising": "personalizing",
+ "pharmacopoeia": "pharmacopeia",
+ "pharmacopoeias": "pharmacopeias",
+ "philosophise": "philosophize",
+ "philosophised": "philosophized",
+ "philosophises": "philosophizes",
+ "philosophising": "philosophizing",
+ "philtre": "filter",
+ "philtres": "filters",
+ "phoney": "phony",
+ "plagiarise": "plagiarize",
+ "plagiarised": "plagiarized",
+ "plagiarises": "plagiarizes",
+ "plagiarising": "plagiarizing",
+ "plough": "plow",
+ "ploughed": "plowed",
+ "ploughing": "plowing",
+ "ploughman": "plowman",
+ "ploughmen": "plowmen",
+ "ploughs": "plows",
+ "ploughshare": "plowshare",
+ "ploughshares": "plowshares",
+ "polarisation": "polarization",
+ "polarise": "polarize",
+ "polarised": "polarized",
+ "polarises": "polarizes",
+ "polarising": "polarizing",
+ "politicisation": "politicization",
+ "politicise": "politicize",
+ "politicised": "politicized",
+ "politicises": "politicizes",
+ "politicising": "politicizing",
+ "popularisation": "popularization",
+ "popularise": "popularize",
+ "popularised": "popularized",
+ "popularises": "popularizes",
+ "popularising": "popularizing",
+ "pouffe": "pouf",
+ "pouffes": "poufs",
+ "practise": "practice",
+ "practised": "practiced",
+ "practises": "practices",
+ "practising": "practicing",
+ "praesidium": "presidium",
+ "praesidiums": "presidiums",
+ "pressurisation": "pressurization",
+ "pressurise": "pressurize",
+ "pressurised": "pressurized",
+ "pressurises": "pressurizes",
+ "pressurising": "pressurizing",
+ "pretence": "pretense",
+ "pretences": "pretenses",
+ "primaeval": "primeval",
+ "prioritisation": "prioritization",
+ "prioritise": "prioritize",
+ "prioritised": "prioritized",
+ "prioritises": "prioritizes",
+ "prioritising": "prioritizing",
+ "privatisation": "privatization",
+ "privatisations": "privatizations",
+ "privatise": "privatize",
+ "privatised": "privatized",
+ "privatises": "privatizes",
+ "privatising": "privatizing",
+ "professionalisation": "professionalization",
+ "professionalise": "professionalize",
+ "professionalised": "professionalized",
+ "professionalises": "professionalizes",
+ "professionalising": "professionalizing",
+ "programme": "program",
+ "programmes": "programs",
+ "prologue": "prolog",
+ "prologues": "prologs",
+ "propagandise": "propagandize",
+ "propagandised": "propagandized",
+ "propagandises": "propagandizes",
+ "propagandising": "propagandizing",
+ "proselytise": "proselytize",
+ "proselytised": "proselytized",
+ "proselytiser": "proselytizer",
+ "proselytisers": "proselytizers",
+ "proselytises": "proselytizes",
+ "proselytising": "proselytizing",
+ "psychoanalyse": "psychoanalyze",
+ "psychoanalysed": "psychoanalyzed",
+ "psychoanalyses": "psychoanalyzes",
+ "psychoanalysing": "psychoanalyzing",
+ "publicise": "publicize",
+ "publicised": "publicized",
+ "publicises": "publicizes",
+ "publicising": "publicizing",
+ "pulverisation": "pulverization",
+ "pulverise": "pulverize",
+ "pulverised": "pulverized",
+ "pulverises": "pulverizes",
+ "pulverising": "pulverizing",
+ "pummelled": "pummel",
+ "pummelling": "pummeled",
+ "pyjama": "pajama",
+ "pyjamas": "pajamas",
+ "pzazz": "pizzazz",
+ "quarrelled": "quarreled",
+ "quarrelling": "quarreling",
+ "radicalise": "radicalize",
+ "radicalised": "radicalized",
+ "radicalises": "radicalizes",
+ "radicalising": "radicalizing",
+ "rancour": "rancor",
+ "randomise": "randomize",
+ "randomised": "randomized",
+ "randomises": "randomizes",
+ "randomising": "randomizing",
+ "rationalisation": "rationalization",
+ "rationalisations": "rationalizations",
+ "rationalise": "rationalize",
+ "rationalised": "rationalized",
+ "rationalises": "rationalizes",
+ "rationalising": "rationalizing",
+ "ravelled": "raveled",
+ "ravelling": "raveling",
+ "realisable": "realizable",
+ "realisation": "realization",
+ "realisations": "realizations",
+ "realise": "realize",
+ "realised": "realized",
+ "realises": "realizes",
+ "realising": "realizing",
+ "recognisable": "recognizable",
+ "recognisably": "recognizably",
+ "recognisance": "recognizance",
+ "recognise": "recognize",
+ "recognised": "recognized",
+ "recognises": "recognizes",
+ "recognising": "recognizing",
+ "reconnoitre": "reconnoiter",
+ "reconnoitred": "reconnoitered",
+ "reconnoitres": "reconnoiters",
+ "reconnoitring": "reconnoitering",
+ "refuelled": "refueled",
+ "refuelling": "refueling",
+ "regularisation": "regularization",
+ "regularise": "regularize",
+ "regularised": "regularized",
+ "regularises": "regularizes",
+ "regularising": "regularizing",
+ "remodelled": "remodeled",
+ "remodelling": "remodeling",
+ "remould": "remold",
+ "remoulded": "remolded",
+ "remoulding": "remolding",
+ "remoulds": "remolds",
+ "reorganisation": "reorganization",
+ "reorganisations": "reorganizations",
+ "reorganise": "reorganize",
+ "reorganised": "reorganized",
+ "reorganises": "reorganizes",
+ "reorganising": "reorganizing",
+ "revelled": "reveled",
+ "reveller": "reveler",
+ "revellers": "revelers",
+ "revelling": "reveling",
+ "revitalise": "revitalize",
+ "revitalised": "revitalized",
+ "revitalises": "revitalizes",
+ "revitalising": "revitalizing",
+ "revolutionise": "revolutionize",
+ "revolutionised": "revolutionized",
+ "revolutionises": "revolutionizes",
+ "revolutionising": "revolutionizing",
+ "rhapsodise": "rhapsodize",
+ "rhapsodised": "rhapsodized",
+ "rhapsodises": "rhapsodizes",
+ "rhapsodising": "rhapsodizing",
+ "rigour": "rigor",
+ "rigours": "rigors",
+ "ritualised": "ritualized",
+ "rivalled": "rivaled",
+ "rivalling": "rivaling",
+ "romanticise": "romanticize",
+ "romanticised": "romanticized",
+ "romanticises": "romanticizes",
+ "romanticising": "romanticizing",
+ "rumour": "rumor",
+ "rumoured": "rumored",
+ "rumours": "rumors",
+ "sabre": "saber",
+ "sabres": "sabers",
+ "saltpetre": "saltpeter",
+ "sanitise": "sanitize",
+ "sanitised": "sanitized",
+ "sanitises": "sanitizes",
+ "sanitising": "sanitizing",
+ "satirise": "satirize",
+ "satirised": "satirized",
+ "satirises": "satirizes",
+ "satirising": "satirizing",
+ "saviour": "savior",
+ "saviours": "saviors",
+ "savour": "savor",
+ "savoured": "savored",
+ "savouries": "savories",
+ "savouring": "savoring",
+ "savours": "savors",
+ "savoury": "savory",
+ "scandalise": "scandalize",
+ "scandalised": "scandalized",
+ "scandalises": "scandalizes",
+ "scandalising": "scandalizing",
+ "sceptic": "skeptic",
+ "sceptical": "skeptical",
+ "sceptically": "skeptically",
+ "scepticism": "skepticism",
+ "sceptics": "skeptics",
+ "sceptre": "scepter",
+ "sceptres": "scepters",
+ "scrutinise": "scrutinize",
+ "scrutinised": "scrutinized",
+ "scrutinises": "scrutinizes",
+ "scrutinising": "scrutinizing",
+ "secularisation": "secularization",
+ "secularise": "secularize",
+ "secularised": "secularized",
+ "secularises": "secularizes",
+ "secularising": "secularizing",
+ "sensationalise": "sensationalize",
+ "sensationalised": "sensationalized",
+ "sensationalises": "sensationalizes",
+ "sensationalising": "sensationalizing",
+ "sensitise": "sensitize",
+ "sensitised": "sensitized",
+ "sensitises": "sensitizes",
+ "sensitising": "sensitizing",
+ "sentimentalise": "sentimentalize",
+ "sentimentalised": "sentimentalized",
+ "sentimentalises": "sentimentalizes",
+ "sentimentalising": "sentimentalizing",
+ "sepulchre": "sepulcher",
+ "sepulchres": "sepulchers",
+ "serialisation": "serialization",
+ "serialisations": "serializations",
+ "serialise": "serialize",
+ "serialised": "serialized",
+ "serialises": "serializes",
+ "serialising": "serializing",
+ "sermonise": "sermonize",
+ "sermonised": "sermonized",
+ "sermonises": "sermonizes",
+ "sermonising": "sermonizing",
+ "sheikh": "sheik",
+ "shovelled": "shoveled",
+ "shovelling": "shoveling",
+ "shrivelled": "shriveled",
+ "shrivelling": "shriveling",
+ "signalise": "signalize",
+ "signalised": "signalized",
+ "signalises": "signalizes",
+ "signalising": "signalizing",
+ "signalled": "signaled",
+ "signalling": "signaling",
+ "smoulder": "smolder",
+ "smouldered": "smoldered",
+ "smouldering": "smoldering",
+ "smoulders": "smolders",
+ "snivelled": "sniveled",
+ "snivelling": "sniveling",
+ "snorkelled": "snorkeled",
+ "snorkelling": "snorkeling",
+ "snowplough": "snowplow",
+ "snowploughs": "snowplow",
+ "socialisation": "socialization",
+ "socialise": "socialize",
+ "socialised": "socialized",
+ "socialises": "socializes",
+ "socialising": "socializing",
+ "sodomise": "sodomize",
+ "sodomised": "sodomized",
+ "sodomises": "sodomizes",
+ "sodomising": "sodomizing",
+ "solemnise": "solemnize",
+ "solemnised": "solemnized",
+ "solemnises": "solemnizes",
+ "solemnising": "solemnizing",
+ "sombre": "somber",
+ "specialisation": "specialization",
+ "specialisations": "specializations",
+ "specialise": "specialize",
+ "specialised": "specialized",
+ "specialises": "specializes",
+ "specialising": "specializing",
+ "spectre": "specter",
+ "spectres": "specters",
+ "spiralled": "spiraled",
+ "spiralling": "spiraling",
+ "splendour": "splendor",
+ "splendours": "splendors",
+ "squirrelled": "squirreled",
+ "squirrelling": "squirreling",
+ "stabilisation": "stabilization",
+ "stabilise": "stabilize",
+ "stabilised": "stabilized",
+ "stabiliser": "stabilizer",
+ "stabilisers": "stabilizers",
+ "stabilises": "stabilizes",
+ "stabilising": "stabilizing",
+ "standardisation": "standardization",
+ "standardise": "standardize",
+ "standardised": "standardized",
+ "standardises": "standardizes",
+ "standardising": "standardizing",
+ "stencilled": "stenciled",
+ "stencilling": "stenciling",
+ "sterilisation": "sterilization",
+ "sterilisations": "sterilizations",
+ "sterilise": "sterilize",
+ "sterilised": "sterilized",
+ "steriliser": "sterilizer",
+ "sterilisers": "sterilizers",
+ "sterilises": "sterilizes",
+ "sterilising": "sterilizing",
+ "stigmatisation": "stigmatization",
+ "stigmatise": "stigmatize",
+ "stigmatised": "stigmatized",
+ "stigmatises": "stigmatizes",
+ "stigmatising": "stigmatizing",
+ "storey": "story",
+ "storeys": "stories",
+ "subsidisation": "subsidization",
+ "subsidise": "subsidize",
+ "subsidised": "subsidized",
+ "subsidiser": "subsidizer",
+ "subsidisers": "subsidizers",
+ "subsidises": "subsidizes",
+ "subsidising": "subsidizing",
+ "succour": "succor",
+ "succoured": "succored",
+ "succouring": "succoring",
+ "succours": "succors",
+ "sulphate": "sulfate",
+ "sulphates": "sulfates",
+ "sulphide": "sulfide",
+ "sulphides": "sulfides",
+ "sulphur": "sulfur",
+ "sulphurous": "sulfurous",
+ "summarise": "summarize",
+ "summarised": "summarized",
+ "summarises": "summarizes",
+ "summarising": "summarizing",
+ "swivelled": "swiveled",
+ "swivelling": "swiveling",
+ "symbolise": "symbolize",
+ "symbolised": "symbolized",
+ "symbolises": "symbolizes",
+ "symbolising": "symbolizing",
+ "sympathise": "sympathize",
+ "sympathised": "sympathized",
+ "sympathiser": "sympathizer",
+ "sympathisers": "sympathizers",
+ "sympathises": "sympathizes",
+ "sympathising": "sympathizing",
+ "synchronisation": "synchronization",
+ "synchronise": "synchronize",
+ "synchronised": "synchronized",
+ "synchronises": "synchronizes",
+ "synchronising": "synchronizing",
+ "synthesise": "synthesize",
+ "synthesised": "synthesized",
+ "synthesiser": "synthesizer",
+ "synthesisers": "synthesizers",
+ "synthesises": "synthesizes",
+ "synthesising": "synthesizing",
+ "syphon": "siphon",
+ "syphoned": "siphoned",
+ "syphoning": "siphoning",
+ "syphons": "siphons",
+ "systematisation": "systematization",
+ "systematise": "systematize",
+ "systematised": "systematized",
+ "systematises": "systematizes",
+ "systematising": "systematizing",
+ "tantalise": "tantalize",
+ "tantalised": "tantalized",
+ "tantalises": "tantalizes",
+ "tantalising": "tantalizing",
+ "tantalisingly": "tantalizingly",
+ "tasselled": "tasseled",
+ "technicolour": "technicolor",
+ "temporise": "temporize",
+ "temporised": "temporized",
+ "temporises": "temporizes",
+ "temporising": "temporizing",
+ "tenderise": "tenderize",
+ "tenderised": "tenderized",
+ "tenderises": "tenderizes",
+ "tenderising": "tenderizing",
+ "terrorise": "terrorize",
+ "terrorised": "terrorized",
+ "terrorises": "terrorizes",
+ "terrorising": "terrorizing",
+ "theatre": "theater",
+ "theatregoer": "theatergoer",
+ "theatregoers": "theatergoers",
+ "theatres": "theaters",
+ "theorise": "theorize",
+ "theorised": "theorized",
+ "theorises": "theorizes",
+ "theorising": "theorizing",
+ "tonne": "ton",
+ "tonnes": "tons",
+ "towelled": "toweled",
+ "towelling": "toweling",
+ "toxaemia": "toxemia",
+ "tranquillise": "tranquilize",
+ "tranquillised": "tranquilized",
+ "tranquilliser": "tranquilizer",
+ "tranquillisers": "tranquilizers",
+ "tranquillises": "tranquilizes",
+ "tranquillising": "tranquilizing",
+ "tranquillity": "tranquility",
+ "tranquillize": "tranquilize",
+ "tranquillized": "tranquilized",
+ "tranquillizer": "tranquilizer",
+ "tranquillizers": "tranquilizers",
+ "tranquillizes": "tranquilizes",
+ "tranquillizing": "tranquilizing",
+ "tranquilly": "tranquility",
+ "transistorised": "transistorized",
+ "traumatise": "traumatize",
+ "traumatised": "traumatized",
+ "traumatises": "traumatizes",
+ "traumatising": "traumatizing",
+ "travelled": "traveled",
+ "traveller": "traveler",
+ "travellers": "travelers",
+ "travelling": "traveling",
+ "travelog": "travelogue",
+ "travelogs": "travelogues",
+ "trialled": "trialed",
+ "trialling": "trialing",
+ "tricolour": "tricolor",
+ "tricolours": "tricolors",
+ "trivialise": "trivialize",
+ "trivialised": "trivialized",
+ "trivialises": "trivializes",
+ "trivialising": "trivializing",
+ "tumour": "tumor",
+ "tumours": "tumors",
+ "tunnelled": "tunneled",
+ "tunnelling": "tunneling",
+ "tyrannise": "tyrannize",
+ "tyrannised": "tyrannized",
+ "tyrannises": "tyrannizes",
+ "tyrannising": "tyrannizing",
+ "tyre": "tire",
+ "tyres": "tires",
+ "unauthorised": "unauthorized",
+ "uncivilised": "uncivilized",
+ "underutilised": "underutilized",
+ "unequalled": "unequaled",
+ "unfavourable": "unfavorable",
+ "unfavourably": "unfavorably",
+ "unionisation": "unionization",
+ "unionise": "unionize",
+ "unionised": "unionized",
+ "unionises": "unionizes",
+ "unionising": "unionizing",
+ "unorganised": "unorganized",
+ "unravelled": "unraveled",
+ "unravelling": "unraveling",
+ "unrecognisable": "unrecognizable",
+ "unrecognised": "unrecognized",
+ "unrivalled": "unrivaled",
+ "unsavoury": "unsavory",
+ "untrammelled": "untrammeled",
+ "urbanisation": "urbanization",
+ "urbanise": "urbanize",
+ "urbanised": "urbanized",
+ "urbanises": "urbanizes",
+ "urbanising": "urbanizing",
+ "utilisable": "utilizable",
+ "utilisation": "utilization",
+ "utilise": "utilize",
+ "utilised": "utilized",
+ "utilises": "utilizes",
+ "utilising": "utilizing",
+ "valour": "valor",
+ "vandalise": "vandalize",
+ "vandalised": "vandalized",
+ "vandalises": "vandalizes",
+ "vandalising": "vandalizing",
+ "vaporisation": "vaporization",
+ "vaporise": "vaporize",
+ "vaporised": "vaporized",
+ "vaporises": "vaporizes",
+ "vaporising": "vaporizing",
+ "vapour": "vapor",
+ "vapours": "vapors",
+ "verbalise": "verbalize",
+ "verbalised": "verbalized",
+ "verbalises": "verbalizes",
+ "verbalising": "verbalizing",
+ "victimisation": "victimization",
+ "victimise": "victimize",
+ "victimised": "victimized",
+ "victimises": "victimizes",
+ "victimising": "victimizing",
+ "videodisc": "videodisk",
+ "videodiscs": "videodisks",
+ "vigour": "vigor",
+ "visualisation": "visualization",
+ "visualisations": "visualizations",
+ "visualise": "visualize",
+ "visualised": "visualized",
+ "visualises": "visualizes",
+ "visualising": "visualizing",
+ "vocalisation": "vocalization",
+ "vocalisations": "vocalizations",
+ "vocalise": "vocalize",
+ "vocalised": "vocalized",
+ "vocalises": "vocalizes",
+ "vocalising": "vocalizing",
+ "vulcanised": "vulcanized",
+ "vulgarisation": "vulgarization",
+ "vulgarise": "vulgarize",
+ "vulgarised": "vulgarized",
+ "vulgarises": "vulgarizes",
+ "vulgarising": "vulgarizing",
+ "waggon": "wagon",
+ "waggons": "wagons",
+ "watercolour": "watercolor",
+ "watercolours": "watercolors",
+ "weaselled": "weaseled",
+ "weaselling": "weaseling",
+ "westernisation": "westernization",
+ "westernise": "westernize",
+ "westernised": "westernized",
+ "westernises": "westernizes",
+ "westernising": "westernizing",
+ "womanise": "womanize",
+ "womanised": "womanized",
+ "womaniser": "womanizer",
+ "womanisers": "womanizers",
+ "womanises": "womanizes",
+ "womanising": "womanizing",
+ "woollen": "woolen",
+ "woollens": "woolens",
+ "woollies": "woolies",
+ "woolly": "wooly",
+ "worshipped": "worshiped",
+ "worshipping": "worshiping",
+ "worshipper": "worshiper",
+ "yodelled": "yodeled",
+ "yodelling": "yodeling",
+ "yoghourt": "yogurt",
+ "yoghourts": "yogurts",
+ "yoghurt": "yogurt",
+ "yoghurts": "yogurts",
+ "mhm": "hmm",
+ "mmm": "hmm"
+}
\ No newline at end of file
diff --git a/whisper/normalizers/english.py b/whisper/normalizers/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5c2bb4ebef1a04b9ab14970c10c3fb0d4e948d4
--- /dev/null
+++ b/whisper/normalizers/english.py
@@ -0,0 +1,543 @@
+import json
+import os
+import re
+from fractions import Fraction
+from typing import Iterator, List, Match, Optional, Union
+
+from more_itertools import windowed
+
+from .basic import remove_symbols_and_diacritics
+
+
+class EnglishNumberNormalizer:
+ """
+ Convert any spelled-out numbers into arabic numbers, while handling:
+
+ - remove any commas
+ - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
+ - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
+ - spell out `one` and `ones`
+ - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ self.zeros = {"o", "oh", "zero"}
+ self.ones = {
+ name: i
+ for i, name in enumerate(
+ [
+ "one",
+ "two",
+ "three",
+ "four",
+ "five",
+ "six",
+ "seven",
+ "eight",
+ "nine",
+ "ten",
+ "eleven",
+ "twelve",
+ "thirteen",
+ "fourteen",
+ "fifteen",
+ "sixteen",
+ "seventeen",
+ "eighteen",
+ "nineteen",
+ ],
+ start=1,
+ )
+ }
+ self.ones_plural = {
+ "sixes" if name == "six" else name + "s": (value, "s")
+ for name, value in self.ones.items()
+ }
+ self.ones_ordinal = {
+ "zeroth": (0, "th"),
+ "first": (1, "st"),
+ "second": (2, "nd"),
+ "third": (3, "rd"),
+ "fifth": (5, "th"),
+ "twelfth": (12, "th"),
+ **{
+ name + ("h" if name.endswith("t") else "th"): (value, "th")
+ for name, value in self.ones.items()
+ if value > 3 and value != 5 and value != 12
+ },
+ }
+ self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
+
+ self.tens = {
+ "twenty": 20,
+ "thirty": 30,
+ "forty": 40,
+ "fifty": 50,
+ "sixty": 60,
+ "seventy": 70,
+ "eighty": 80,
+ "ninety": 90,
+ }
+ self.tens_plural = {
+ name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
+ }
+ self.tens_ordinal = {
+ name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
+ }
+ self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
+
+ self.multipliers = {
+ "hundred": 100,
+ "thousand": 1_000,
+ "million": 1_000_000,
+ "billion": 1_000_000_000,
+ "trillion": 1_000_000_000_000,
+ "quadrillion": 1_000_000_000_000_000,
+ "quintillion": 1_000_000_000_000_000_000,
+ "sextillion": 1_000_000_000_000_000_000_000,
+ "septillion": 1_000_000_000_000_000_000_000_000,
+ "octillion": 1_000_000_000_000_000_000_000_000_000,
+ "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
+ "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
+ }
+ self.multipliers_plural = {
+ name + "s": (value, "s") for name, value in self.multipliers.items()
+ }
+ self.multipliers_ordinal = {
+ name + "th": (value, "th") for name, value in self.multipliers.items()
+ }
+ self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
+ self.decimals = {*self.ones, *self.tens, *self.zeros}
+
+ self.preceding_prefixers = {
+ "minus": "-",
+ "negative": "-",
+ "plus": "+",
+ "positive": "+",
+ }
+ self.following_prefixers = {
+ "pound": "£",
+ "pounds": "£",
+ "euro": "€",
+ "euros": "€",
+ "dollar": "$",
+ "dollars": "$",
+ "cent": "¢",
+ "cents": "¢",
+ }
+ self.prefixes = set(
+ list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
+ )
+ self.suffixers = {
+ "per": {"cent": "%"},
+ "percent": "%",
+ }
+ self.specials = {"and", "double", "triple", "point"}
+
+ self.words = set(
+ [
+ key
+ for mapping in [
+ self.zeros,
+ self.ones,
+ self.ones_suffixed,
+ self.tens,
+ self.tens_suffixed,
+ self.multipliers,
+ self.multipliers_suffixed,
+ self.preceding_prefixers,
+ self.following_prefixers,
+ self.suffixers,
+ self.specials,
+ ]
+ for key in mapping
+ ]
+ )
+ self.literal_words = {"one", "ones"}
+
+ def process_words(self, words: List[str]) -> Iterator[str]:
+ prefix: Optional[str] = None
+ value: Optional[Union[str, int]] = None
+ skip = False
+
+ def to_fraction(s: str):
+ try:
+ return Fraction(s)
+ except ValueError:
+ return None
+
+ def output(result: Union[str, int]):
+ nonlocal prefix, value
+ result = str(result)
+ if prefix is not None:
+ result = prefix + result
+ value = None
+ prefix = None
+ return result
+
+ if len(words) == 0:
+ return
+
+ for prev, current, next in windowed([None] + words + [None], 3):
+ if skip:
+ skip = False
+ continue
+
+ next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
+ has_prefix = current[0] in self.prefixes
+ current_without_prefix = current[1:] if has_prefix else current
+ if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
+ # arabic numbers (potentially with signs and fractions)
+ f = to_fraction(current_without_prefix)
+ assert f is not None
+ if value is not None:
+ if isinstance(value, str) and value.endswith("."):
+ # concatenate decimals / ip address components
+ value = str(value) + str(current)
+ continue
+ else:
+ yield output(value)
+
+ prefix = current[0] if has_prefix else prefix
+ if f.denominator == 1:
+ value = f.numerator # store integers as int
+ else:
+ value = current_without_prefix
+ elif current not in self.words:
+ # non-numeric words
+ if value is not None:
+ yield output(value)
+ yield output(current)
+ elif current in self.zeros:
+ value = str(value or "") + "0"
+ elif current in self.ones:
+ ones = self.ones[current]
+
+ if value is None:
+ value = ones
+ elif isinstance(value, str) or prev in self.ones:
+ if prev in self.tens and ones < 10: # replace the last zero with the digit
+ assert value[-1] == "0"
+ value = value[:-1] + str(ones)
+ else:
+ value = str(value) + str(ones)
+ elif ones < 10:
+ if value % 10 == 0:
+ value += ones
+ else:
+ value = str(value) + str(ones)
+ else: # eleven to nineteen
+ if value % 100 == 0:
+ value += ones
+ else:
+ value = str(value) + str(ones)
+ elif current in self.ones_suffixed:
+ # ordinal or cardinal; yield the number right away
+ ones, suffix = self.ones_suffixed[current]
+ if value is None:
+ yield output(str(ones) + suffix)
+ elif isinstance(value, str) or prev in self.ones:
+ if prev in self.tens and ones < 10:
+ assert value[-1] == "0"
+ yield output(value[:-1] + str(ones) + suffix)
+ else:
+ yield output(str(value) + str(ones) + suffix)
+ elif ones < 10:
+ if value % 10 == 0:
+ yield output(str(value + ones) + suffix)
+ else:
+ yield output(str(value) + str(ones) + suffix)
+ else: # eleven to nineteen
+ if value % 100 == 0:
+ yield output(str(value + ones) + suffix)
+ else:
+ yield output(str(value) + str(ones) + suffix)
+ value = None
+ elif current in self.tens:
+ tens = self.tens[current]
+ if value is None:
+ value = tens
+ elif isinstance(value, str):
+ value = str(value) + str(tens)
+ else:
+ if value % 100 == 0:
+ value += tens
+ else:
+ value = str(value) + str(tens)
+ elif current in self.tens_suffixed:
+ # ordinal or cardinal; yield the number right away
+ tens, suffix = self.tens_suffixed[current]
+ if value is None:
+ yield output(str(tens) + suffix)
+ elif isinstance(value, str):
+ yield output(str(value) + str(tens) + suffix)
+ else:
+ if value % 100 == 0:
+ yield output(str(value + tens) + suffix)
+ else:
+ yield output(str(value) + str(tens) + suffix)
+ elif current in self.multipliers:
+ multiplier = self.multipliers[current]
+ if value is None:
+ value = multiplier
+ elif isinstance(value, str) or value == 0:
+ f = to_fraction(value)
+ p = f * multiplier if f is not None else None
+ if f is not None and p.denominator == 1:
+ value = p.numerator
+ else:
+ yield output(value)
+ value = multiplier
+ else:
+ before = value // 1000 * 1000
+ residual = value % 1000
+ value = before + residual * multiplier
+ elif current in self.multipliers_suffixed:
+ multiplier, suffix = self.multipliers_suffixed[current]
+ if value is None:
+ yield output(str(multiplier) + suffix)
+ elif isinstance(value, str):
+ f = to_fraction(value)
+ p = f * multiplier if f is not None else None
+ if f is not None and p.denominator == 1:
+ yield output(str(p.numerator) + suffix)
+ else:
+ yield output(value)
+ yield output(str(multiplier) + suffix)
+ else: # int
+ before = value // 1000 * 1000
+ residual = value % 1000
+ value = before + residual * multiplier
+ yield output(str(value) + suffix)
+ value = None
+ elif current in self.preceding_prefixers:
+ # apply prefix (positive, minus, etc.) if it precedes a number
+ if value is not None:
+ yield output(value)
+
+ if next in self.words or next_is_numeric:
+ prefix = self.preceding_prefixers[current]
+ else:
+ yield output(current)
+ elif current in self.following_prefixers:
+ # apply prefix (dollars, cents, etc.) only after a number
+ if value is not None:
+ prefix = self.following_prefixers[current]
+ yield output(value)
+ else:
+ yield output(current)
+ elif current in self.suffixers:
+ # apply suffix symbols (percent -> '%')
+ if value is not None:
+ suffix = self.suffixers[current]
+ if isinstance(suffix, dict):
+ if next in suffix:
+ yield output(str(value) + suffix[next])
+ skip = True
+ else:
+ yield output(value)
+ yield output(current)
+ else:
+ yield output(str(value) + suffix)
+ else:
+ yield output(current)
+ elif current in self.specials:
+ if next not in self.words and not next_is_numeric:
+ # apply special handling only if the next word can be numeric
+ if value is not None:
+ yield output(value)
+ yield output(current)
+ elif current == "and":
+ # ignore "and" after hundreds, thousands, etc.
+ if prev not in self.multipliers:
+ if value is not None:
+ yield output(value)
+ yield output(current)
+ elif current == "double" or current == "triple":
+ if next in self.ones or next in self.zeros:
+ repeats = 2 if current == "double" else 3
+ ones = self.ones.get(next, 0)
+ value = str(value or "") + str(ones) * repeats
+ skip = True
+ else:
+ if value is not None:
+ yield output(value)
+ yield output(current)
+ elif current == "point":
+ if next in self.decimals or next_is_numeric:
+ value = str(value or "") + "."
+ else:
+ # should all have been covered at this point
+ raise ValueError(f"Unexpected token: {current}")
+ else:
+ # all should have been covered at this point
+ raise ValueError(f"Unexpected token: {current}")
+
+ if value is not None:
+ yield output(value)
+
+ def preprocess(self, s: str):
+ # replace "<number> and a half" with "<number> point five"
+ results = []
+
+ segments = re.split(r"\band\s+a\s+half\b", s)
+ for i, segment in enumerate(segments):
+ if len(segment.strip()) == 0:
+ continue
+ if i == len(segments) - 1:
+ results.append(segment)
+ else:
+ results.append(segment)
+ last_word = segment.rsplit(maxsplit=2)[-1]
+ if last_word in self.decimals or last_word in self.multipliers:
+ results.append("point five")
+ else:
+ results.append("and a half")
+
+ s = " ".join(results)
+
+ # put a space at number/letter boundary
+ s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
+ s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
+
+ # but remove spaces which could be a suffix
+ s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
+
+ return s
+
+ def postprocess(self, s: str):
+ def combine_cents(m: Match):
+ try:
+ currency = m.group(1)
+ integer = m.group(2)
+ cents = int(m.group(3))
+ return f"{currency}{integer}.{cents:02d}"
+ except ValueError:
+ return m.string
+
+ def extract_cents(m: Match):
+ try:
+ return f"¢{int(m.group(1))}"
+ except ValueError:
+ return m.string
+
+ # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
+ s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
+ s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
+
+ # write "one(s)" instead of "1(s)", just for the readability
+ s = re.sub(r"\b1(s?)\b", r"one\1", s)
+
+ return s
+
+ def __call__(self, s: str):
+ s = self.preprocess(s)
+ s = " ".join(word for word in self.process_words(s.split()) if word is not None)
+ s = self.postprocess(s)
+
+ return s
+
+
+class EnglishSpellingNormalizer:
+ """
+ Applies British-American spelling mappings as listed in [1].
+
+ [1] https://www.tysto.com/uk-us-spelling-list.html
+ """
+
+ def __init__(self):
+ mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
+ self.mapping = json.load(open(mapping_path))
+
+ def __call__(self, s: str):
+ return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishTextNormalizer:
+ def __init__(self):
+ self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
+ self.replacers = {
+ # common contractions
+ r"\bwon't\b": "will not",
+ r"\bcan't\b": "can not",
+ r"\blet's\b": "let us",
+ r"\bain't\b": "aint",
+ r"\by'all\b": "you all",
+ r"\bwanna\b": "want to",
+ r"\bgotta\b": "got to",
+ r"\bgonna\b": "going to",
+ r"\bi'ma\b": "i am going to",
+ r"\bimma\b": "i am going to",
+ r"\bwoulda\b": "would have",
+ r"\bcoulda\b": "could have",
+ r"\bshoulda\b": "should have",
+ r"\bma'am\b": "madam",
+ # contractions in titles/prefixes
+ r"\bmr\b": "mister ",
+ r"\bmrs\b": "missus ",
+ r"\bst\b": "saint ",
+ r"\bdr\b": "doctor ",
+ r"\bprof\b": "professor ",
+ r"\bcapt\b": "captain ",
+ r"\bgov\b": "governor ",
+ r"\bald\b": "alderman ",
+ r"\bgen\b": "general ",
+ r"\bsen\b": "senator ",
+ r"\brep\b": "representative ",
+ r"\bpres\b": "president ",
+ r"\brev\b": "reverend ",
+ r"\bhon\b": "honorable ",
+ r"\basst\b": "assistant ",
+ r"\bassoc\b": "associate ",
+ r"\blt\b": "lieutenant ",
+ r"\bcol\b": "colonel ",
+ r"\bjr\b": "junior ",
+ r"\bsr\b": "senior ",
+ r"\besq\b": "esquire ",
+ # prefect tenses, ideally it should be any past participles, but it's harder..
+ r"'d been\b": " had been",
+ r"'s been\b": " has been",
+ r"'d gone\b": " had gone",
+ r"'s gone\b": " has gone",
+ r"'d done\b": " had done", # "'s done" is ambiguous
+ r"'s got\b": " has got",
+ # general contractions
+ r"n't\b": " not",
+ r"'re\b": " are",
+ r"'s\b": " is",
+ r"'d\b": " would",
+ r"'ll\b": " will",
+ r"'t\b": " not",
+ r"'ve\b": " have",
+ r"'m\b": " am",
+ }
+ self.standardize_numbers = EnglishNumberNormalizer()
+ self.standardize_spellings = EnglishSpellingNormalizer()
+
+ def __call__(self, s: str):
+ s = s.lower()
+
+ s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets
+ s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis
+ s = re.sub(self.ignore_patterns, "", s)
+ s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe
+
+ for pattern, replacement in self.replacers.items():
+ s = re.sub(pattern, replacement, s)
+
+ s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits
+ s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers
+ s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics
+
+ s = self.standardize_numbers(s)
+ s = self.standardize_spellings(s)
+
+ # now remove prefix/suffix symbols that are not preceded/followed by numbers
+ s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
+ s = re.sub(r"([^0-9])%", r"\1 ", s)
+
+ s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space
+
+ return s
diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a27cb359ee891590d3f793624f9f8ec768a26cc3
--- /dev/null
+++ b/whisper/tokenizer.py
@@ -0,0 +1,331 @@
+import os
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import GPT2TokenizerFast
+
+LANGUAGES = {
+ "en": "english",
+ "zh": "chinese",
+ "de": "german",
+ "es": "spanish",
+ "ru": "russian",
+ "ko": "korean",
+ "fr": "french",
+ "ja": "japanese",
+ "pt": "portuguese",
+ "tr": "turkish",
+ "pl": "polish",
+ "ca": "catalan",
+ "nl": "dutch",
+ "ar": "arabic",
+ "sv": "swedish",
+ "it": "italian",
+ "id": "indonesian",
+ "hi": "hindi",
+ "fi": "finnish",
+ "vi": "vietnamese",
+ "he": "hebrew",
+ "uk": "ukrainian",
+ "el": "greek",
+ "ms": "malay",
+ "cs": "czech",
+ "ro": "romanian",
+ "da": "danish",
+ "hu": "hungarian",
+ "ta": "tamil",
+ "no": "norwegian",
+ "th": "thai",
+ "ur": "urdu",
+ "hr": "croatian",
+ "bg": "bulgarian",
+ "lt": "lithuanian",
+ "la": "latin",
+ "mi": "maori",
+ "ml": "malayalam",
+ "cy": "welsh",
+ "sk": "slovak",
+ "te": "telugu",
+ "fa": "persian",
+ "lv": "latvian",
+ "bn": "bengali",
+ "sr": "serbian",
+ "az": "azerbaijani",
+ "sl": "slovenian",
+ "kn": "kannada",
+ "et": "estonian",
+ "mk": "macedonian",
+ "br": "breton",
+ "eu": "basque",
+ "is": "icelandic",
+ "hy": "armenian",
+ "ne": "nepali",
+ "mn": "mongolian",
+ "bs": "bosnian",
+ "kk": "kazakh",
+ "sq": "albanian",
+ "sw": "swahili",
+ "gl": "galician",
+ "mr": "marathi",
+ "pa": "punjabi",
+ "si": "sinhala",
+ "km": "khmer",
+ "sn": "shona",
+ "yo": "yoruba",
+ "so": "somali",
+ "af": "afrikaans",
+ "oc": "occitan",
+ "ka": "georgian",
+ "be": "belarusian",
+ "tg": "tajik",
+ "sd": "sindhi",
+ "gu": "gujarati",
+ "am": "amharic",
+ "yi": "yiddish",
+ "lo": "lao",
+ "uz": "uzbek",
+ "fo": "faroese",
+ "ht": "haitian creole",
+ "ps": "pashto",
+ "tk": "turkmen",
+ "nn": "nynorsk",
+ "mt": "maltese",
+ "sa": "sanskrit",
+ "lb": "luxembourgish",
+ "my": "myanmar",
+ "bo": "tibetan",
+ "tl": "tagalog",
+ "mg": "malagasy",
+ "as": "assamese",
+ "tt": "tatar",
+ "haw": "hawaiian",
+ "ln": "lingala",
+ "ha": "hausa",
+ "ba": "bashkir",
+ "jw": "javanese",
+ "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+ **{language: code for code, language in LANGUAGES.items()},
+ "burmese": "my",
+ "valencian": "ca",
+ "flemish": "nl",
+ "haitian": "ht",
+ "letzeburgesch": "lb",
+ "pushto": "ps",
+ "panjabi": "pa",
+ "moldavian": "ro",
+ "moldovan": "ro",
+ "sinhalese": "si",
+ "castilian": "es",
+}
+
+
+@dataclass(frozen=True)
+class Tokenizer:
+ """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
+
+ tokenizer: "GPT2TokenizerFast"
+ language: Optional[str]
+ sot_sequence: Tuple[int]
+
+ def encode(self, text, **kwargs):
+ return self.tokenizer.encode(text, **kwargs)
+
+ def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
+ return self.tokenizer.decode(token_ids, **kwargs)
+
+ def decode_with_timestamps(self, tokens) -> str:
+ """
+ Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
+ This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+ """
+ outputs = [[]]
+ for token in tokens:
+ if token >= self.timestamp_begin:
+ timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
+ outputs.append(timestamp)
+ outputs.append([])
+ else:
+ outputs[-1].append(token)
+ outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
+ return "".join(outputs)
+
+ @property
+ @lru_cache()
+ def eot(self) -> int:
+ return self.tokenizer.eos_token_id
+
+ @property
+ @lru_cache()
+ def sot(self) -> int:
+ return self._get_single_token_id("<|startoftranscript|>")
+
+ @property
+ @lru_cache()
+ def sot_lm(self) -> int:
+ return self._get_single_token_id("<|startoflm|>")
+
+ @property
+ @lru_cache()
+ def sot_prev(self) -> int:
+ return self._get_single_token_id("<|startofprev|>")
+
+ @property
+ @lru_cache()
+ def no_speech(self) -> int:
+ return self._get_single_token_id("<|nospeech|>")
+
+ @property
+ @lru_cache()
+ def no_timestamps(self) -> int:
+ return self._get_single_token_id("<|notimestamps|>")
+
+ @property
+ @lru_cache()
+ def timestamp_begin(self) -> int:
+ return self.tokenizer.all_special_ids[-1] + 1
+
+ @property
+ @lru_cache()
+ def language_token(self) -> int:
+ """Returns the token id corresponding to the value of the `language` field"""
+ if self.language is None:
+ raise ValueError(f"This tokenizer does not have language token configured")
+
+ additional_tokens = dict(
+ zip(
+ self.tokenizer.additional_special_tokens,
+ self.tokenizer.additional_special_tokens_ids,
+ )
+ )
+ candidate = f"<|{self.language}|>"
+ if candidate in additional_tokens:
+ return additional_tokens[candidate]
+
+ raise KeyError(f"Language {self.language} not found in tokenizer.")
+
+ @property
+ @lru_cache()
+ def all_language_tokens(self) -> Tuple[int]:
+ result = []
+ for token, token_id in zip(
+ self.tokenizer.additional_special_tokens,
+ self.tokenizer.additional_special_tokens_ids,
+ ):
+ if token.strip("<|>") in LANGUAGES:
+ result.append(token_id)
+ return tuple(result)
+
+ @property
+ @lru_cache()
+ def all_language_codes(self) -> Tuple[str]:
+ return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
+
+ @property
+ @lru_cache()
+ def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+ return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+ @property
+ @lru_cache()
+ def non_speech_tokens(self) -> Tuple[int]:
+ """
+ Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+ annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+ - ♪♪♪
+ - ( SPEAKING FOREIGN LANGUAGE )
+ - [DAVID] Hey there,
+
+ keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+ """
+ symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
+ symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+
+ # symbols that may be a single token or multiple tokens depending on the tokenizer.
+ # In case they're multiple tokens, suppress the first token, which is safe because:
+ # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+ # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+ miscellaneous = set("♩♪♫♬♭♮♯")
+ assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+ # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+ result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
+ for symbol in symbols + list(miscellaneous):
+ for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
+ if len(tokens) == 1 or symbol in miscellaneous:
+ result.add(tokens[0])
+
+ return tuple(sorted(result))
+
+ def _get_single_token_id(self, text) -> int:
+ tokens = self.tokenizer.encode(text)
+ assert len(tokens) == 1, f"{text} is not encoded as a single token"
+ return tokens[0]
+
+
+@lru_cache(maxsize=None)
+def build_tokenizer(name: str = "gpt2"):
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
+ path = os.path.join(os.path.dirname(__file__), "assets", name)
+ tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+ specials = [
+ "<|startoftranscript|>",
+ *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+ "<|translate|>",
+ "<|transcribe|>",
+ "<|startoflm|>",
+ "<|startofprev|>",
+ "<|nospeech|>",
+ "<|notimestamps|>",
+ ]
+
+ tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+ return tokenizer
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+ multilingual: bool,
+ *,
+ task: Optional[str] = None, # Literal["transcribe", "translate", None]
+ language: Optional[str] = None,
+) -> Tokenizer:
+ if language is not None:
+ language = language.lower()
+ if language not in LANGUAGES:
+ if language in TO_LANGUAGE_CODE:
+ language = TO_LANGUAGE_CODE[language]
+ else:
+ raise ValueError(f"Unsupported language: {language}")
+
+ if multilingual:
+ tokenizer_name = "multilingual"
+ task = task or "transcribe"
+ language = language or "en"
+ else:
+ tokenizer_name = "gpt2"
+ task = None
+ language = None
+
+ tokenizer = build_tokenizer(name=tokenizer_name)
+ all_special_ids: List[int] = tokenizer.all_special_ids
+ sot: int = all_special_ids[1]
+ translate: int = all_special_ids[-6]
+ transcribe: int = all_special_ids[-5]
+
+ langs = tuple(LANGUAGES.keys())
+ sot_sequence = [sot]
+ if language is not None:
+ sot_sequence.append(sot + 1 + langs.index(language))
+ if task is not None:
+ sot_sequence.append(transcribe if task == "transcribe" else translate)
+
+ return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/whisper/utils.py b/whisper/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dacc173c40bcd6e999d728862e29a968000b12e
--- /dev/null
+++ b/whisper/utils.py
@@ -0,0 +1,163 @@
+import json
+import os
+import sys
+import zlib
+from typing import Callable, TextIO
+
+system_encoding = sys.getdefaultencoding()
+
+if system_encoding != "utf-8":
+ def make_safe(string):
+ # replaces any character not representable using the system default encoding with an '?',
+ # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
+ return string.encode(system_encoding, errors="replace").decode(system_encoding)
+else:
+ def make_safe(string):
+ # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
+ return string
+
+
+def exact_div(x, y):
+ assert x % y == 0
+ return x // y
+
+
+def str2bool(string):
+ str2val = {"True": True, "False": False}
+ if string in str2val:
+ return str2val[string]
+ else:
+ raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
+
+
+def optional_int(string):
+ return None if string == "None" else int(string)
+
+
+def optional_float(string):
+ return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+ text_bytes = text.encode("utf-8")
+ return len(text_bytes) / len(zlib.compress(text_bytes))
+
+
+def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
+ assert seconds >= 0, "non-negative timestamp expected"
+ milliseconds = round(seconds * 1000.0)
+
+ hours = milliseconds // 3_600_000
+ milliseconds -= hours * 3_600_000
+
+ minutes = milliseconds // 60_000
+ milliseconds -= minutes * 60_000
+
+ seconds = milliseconds // 1_000
+ milliseconds -= seconds * 1_000
+
+ hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+ return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+
+
+class ResultWriter:
+ extension: str
+
+ def __init__(self, output_dir: str):
+ self.output_dir = output_dir
+
+ def __call__(self, result: dict, audio_path: str):
+ audio_basename = os.path.basename(audio_path)
+ output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension)
+
+ with open(output_path, "w", encoding="utf-8") as f:
+ self.write_result(result, file=f)
+
+ def write_result(self, result: dict, file: TextIO):
+ raise NotImplementedError
+
+
+class WriteTXT(ResultWriter):
+ extension: str = "txt"
+
+ def write_result(self, result: dict, file: TextIO):
+ for segment in result["segments"]:
+ print(segment['text'].strip(), file=file, flush=True)
+
+
+class WriteVTT(ResultWriter):
+ extension: str = "vtt"
+
+ def write_result(self, result: dict, file: TextIO):
+ print("WEBVTT\n", file=file)
+ for segment in result["segments"]:
+ print(
+ f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
+ f"{segment['text'].strip().replace('-->', '->')}\n",
+ file=file,
+ flush=True,
+ )
+
+
+class WriteSRT(ResultWriter):
+ extension: str = "srt"
+
+ def write_result(self, result: dict, file: TextIO):
+ for i, segment in enumerate(result["segments"], start=1):
+ # write srt lines
+ print(
+ f"{i}\n"
+ f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
+ f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
+ f"{segment['text'].strip().replace('-->', '->')}\n",
+ file=file,
+ flush=True,
+ )
+
+
+class WriteTSV(ResultWriter):
+ """
+ Write a transcript to a file in TSV (tab-separated values) format containing lines like:
+ <start time in integer milliseconds>\t<end time in integer milliseconds>\t<transcript text>
+
+ Using integer milliseconds as start and end times means there's no chance of interference from
+ an environment setting a language encoding that causes the decimal in a floating point number
+ to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
+ """
+ extension: str = "tsv"
+
+ def write_result(self, result: dict, file: TextIO):
+ print("start", "end", "text", sep="\t", file=file)
+ for segment in result["segments"]:
+ print(round(1000 * segment['start']), file=file, end="\t")
+ print(round(1000 * segment['end']), file=file, end="\t")
+ print(segment['text'].strip().replace("\t", " "), file=file, flush=True)
+
+
+class WriteJSON(ResultWriter):
+ extension: str = "json"
+
+ def write_result(self, result: dict, file: TextIO):
+ json.dump(result, file)
+
+
+def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
+ writers = {
+ "txt": WriteTXT,
+ "vtt": WriteVTT,
+ "srt": WriteSRT,
+ "tsv": WriteTSV,
+ "json": WriteJSON,
+ }
+
+ if output_format == "all":
+ all_writers = [writer(output_dir) for writer in writers.values()]
+
+ def write_all(result: dict, file: TextIO):
+ for writer in all_writers:
+ writer(result, file)
+
+ return write_all
+
+ return writers[output_format](output_dir)
+
diff --git a/whisper_pretrain/README.md b/whisper_pretrain/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1093e1dcbb792f7a86a0d3a3a1bf14691ab998b5
--- /dev/null
+++ b/whisper_pretrain/README.md
@@ -0,0 +1,3 @@
+Path for:
+
+ medium.pt
\ No newline at end of file
diff --git a/whisper_pretrain/medium.pt b/whisper_pretrain/medium.pt
new file mode 100644
index 0000000000000000000000000000000000000000..8aca41c710014a3d39774cd7592fa086177c672f
--- /dev/null
+++ b/whisper_pretrain/medium.pt
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