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import IPython.display as ipd
import gradio as gr
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
from Utils.PLBERT.util import load_plbert
from text_utils import TextCleaner
from utils import *
from models import *
from nltk.tokenize import word_tokenize
import librosa
import torchaudio
import torch.nn.functional as F
from torch import nn
from munch import Munch
import yaml
import time
import numpy as np
import random
import torch
import nltk
nltk.download('punkt_tab')
from phonemizer import phonemize
from phonemizer.backend import EspeakBackend
global_phonemizer = EspeakBackend(language='en')
torch.manual_seed(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
random.seed(0)
np.random.seed(0)
# load packages
textcleaner = TextCleaner()
# set up a transformation from a sound wave (an amplitude at each sampling step) to a mel spectrogram (80 dimensions).
to_mel = torchaudio.transforms.MelSpectrogram(
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
mean, std = -4, 4
# Creates a binary mask of 1s for values in the tensor and zero for padding to the length of the longest vector.
def length_to_mask(lengths):
mask = torch.arange(lengths.max()).unsqueeze(
0).expand(lengths.shape[0], -1).type_as(lengths)
mask = torch.gt(mask+1, lengths.unsqueeze(1))
return mask
# Converts a waveform to a normalized log-Mel spectrogram tensor.
def preprocess(wave):
wave_tensor = torch.from_numpy(wave).float()
mel_tensor = to_mel(wave_tensor)
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
return mel_tensor
# Loads, trims, resamples an audio file, and computes its style and predictor encodings.
def compute_style(path):
wave, sr = librosa.load(path, sr=24000)
audio, index = librosa.effects.trim(wave, top_db=30)
if sr != 24000:
audio = librosa.resample(audio, sr, 24000)
mel_tensor = preprocess(audio).to(device)
with torch.no_grad():
ref_s = model.style_encoder(mel_tensor.unsqueeze(1)) # gets
ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
return torch.cat([ref_s, ref_p], dim=1)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device != 'cuda':
print("Using cpu as cuda is not available!")
else:
print("Using cuda")
# load phonemizer (converts text into phonemes)
global_phonemizer = phonemizer.backend.EspeakBackend(
language='en-us', preserve_punctuation=True, with_stress=True)
# model_folder_path="Models/LibriTTS-lora-ft/merged" # for inferencing the merged lora
# config = yaml.safe_load(open(model_folder_path + '/config.yml'))
# for inferencing the full fine-tuned model
model_folder_path = "Models/LibriTTS-fft"
# Rohan, why is the file here config_ft whereas for lora above it is config.yml . Are we loading what we think we are?
config = yaml.safe_load(open(model_folder_path + '/config_ft.yml'))
# load pretrained ASR model
ASR_config = config.get('ASR_config', False)
ASR_path = config.get('ASR_path', False)
text_aligner = load_ASR_models(ASR_path, ASR_config)
# load pretrained F0 model
F0_path = config.get('F0_path', False)
pitch_extractor = load_F0_models(F0_path)
# load BERT model
BERT_path = config.get('PLBERT_dir', False)
plbert = load_plbert(BERT_path)
model_params = recursive_munch(config['model_params'])
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
_ = [model[key].eval() for key in model]
_ = [model[key].to(device) for key in model]
files = [f for f in os.listdir(model_folder_path) if f.endswith('.pth')]
sorted_files = sorted(files, key=lambda x: int(x.split('_')[-1].split('.')[0]))
print(sorted_files)
# I'm grabbing the last fine instead
params_whole = torch.load(model_folder_path + '/' +
sorted_files[-1], map_location='cpu')
if 'net' in params_whole.keys():
print('yes')
params = params_whole['net']
else:
params = params_whole
print('no')
for key in model:
if key in params:
print('%s loaded' % key)
try:
model[key].load_state_dict(params[key])
except:
from collections import OrderedDict
state_dict = params[key]
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
model[key].load_state_dict(new_state_dict, strict=False)
# except:
# _load(params[key], model[key])
# Loading the diffusion sampler
sampler = DiffusionSampler(
model.diffusion.diffusion,
sampler=ADPM2Sampler(),
sigma_schedule=KarrasSchedule(
sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
clamp=False
)
def inference(text, ref_s, alpha=0.2, beta=0.2, diffusion_steps=10, embedding_scale=1):
"""
Generate speech from text using a diffusion-based approach with reference style blending.
Parameters:
- text: The input text to convert to speech.
- ref_s: The reference style and predictor encoder features from an audio snippet.
- alpha: Blending factor for the reference style (lower alpha means more like the reference).
- beta: Blending factor for the predictor features (lower beta means more like the reference).
- diffusion_steps: Number of steps in the diffusion process (more steps improve quality).
- embedding_scale: Scaling factor for the BERT embeddings.
"""
# Clean up and tokenize the input text
text = text.strip()
ps = global_phonemizer.phonemize([text])
ps = word_tokenize(ps[0])
ps = ' '.join(ps)
tokens = textcleaner(ps)
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
with torch.no_grad():
# Get the length of the input tokens
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
# Create a mask for the input text to handle variable lengths
text_mask = length_to_mask(input_lengths).to(device)
# Encode the text using the text encoder
t_en = model.text_encoder(tokens, input_lengths, text_mask)
# Use BERT to get the prosodic text encoding (to be used for style prediction).
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
# Further reduce the dimensions of the BERT embeddings to be suitable for the predictor
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
# Generate an output style + predictor vector
s_pred = sampler(
noise=torch.randn((1, 256)).unsqueeze(1).to(device),
embedding=bert_dur, # BERT output embeddings
embedding_scale=embedding_scale,
features=ref_s, # Style and predictor features from reference audio
num_steps=diffusion_steps
).squeeze(1)
# Split the generated features into style and predictor components
s = s_pred[:, 128:]
ref = s_pred[:, :128]
# Blend the generated style features with the reference style
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
# Blend the generated predictor features with the reference predictor
s = beta * s + (1 - beta) * ref_s[:, 128:]
# Use the predictor to encode the text with the generated features
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
# Pass through the LSTM to get duration predictions
x, _ = model.predictor.lstm(d)
duration = model.predictor.duration_proj(x)
# Process the duration predictions
duration = torch.sigmoid(duration).sum(axis=-1)
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
# Create a target alignment for the predicted durations
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
c_frame = 0
for i in range(pred_aln_trg.size(0)):
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
c_frame += int(pred_dur[i].data)
# Encode the prosody using the target alignment
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
if model_params.decoder.type == "hifigan":
# Adjust for HiFi-GAN decoder input format
asr_new = torch.zeros_like(en)
asr_new[:, :, 0] = en[:, :, 0]
asr_new[:, :, 1:] = en[:, :, 0:-1]
en = asr_new
# Predict F0 and N features (fundamental frequency and noise)
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
# Create the alignment for the text encoder output
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
if model_params.decoder.type == "hifigan":
# Adjust for HiFi-GAN decoder input format
asr_new = torch.zeros_like(asr)
asr_new[:, :, 0] = asr[:, :, 0]
asr_new[:, :, 1:] = asr[:, :, 0:-1]
asr = asr_new
# Decode the final audio output using the decoder
out = model.decoder(asr, F0_pred, N_pred, ref.squeeze().unsqueeze(0))
# Return the generated audio, excluding a small pulse at the end
# weird pulse at the end of the model, need to be fixed later
return out.squeeze().cpu().numpy()[..., :-50]
import numpy as np
import gradio as gr
def tts_model(text):
# Assuming a reference path is used for style (you can adjust this path as needed)
ref_s = compute_style("Trelis_Data/wavs/med5_0.wav")
# Run inference to generate the output wav
wav = inference(text, ref_s, alpha=0.3, beta=0.3,
diffusion_steps=10, embedding_scale=1)
# Convert 1D wav array to 2D to match Gradio's expectations (mono audio)
wav = np.expand_dims(wav, axis=1)
# Return the audio as a tuple with sample rate
return 24000, wav # Assuming a 24000 Hz sample rate for the output audio
# Create a Gradio interface
interface = gr.Interface(
fn=tts_model,
inputs=gr.Textbox(label="Input Text"), # Input text for speech generation
outputs=gr.Audio(label="Generated Audio", type="numpy"), # Generated TTS audio
live=False
)
# Launch the Gradio interface
interface.launch(share=True)
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