Delete cog.py

cog.py

@@ -1,180 +0,0 @@
-# Prediction interface for Cog ⚙️
-# https://cog.run/python
-
-from cog import BasePredictor, Input, Path
-
-import os
-import re
-import torch
-import torchaudio
-import numpy as np
-import tempfile
-from einops import rearrange
-from ema_pytorch import EMA
-from vocos import Vocos
-from pydub import AudioSegment
-from model import CFM, UNetT, DiT, MMDiT
-from cached_path import cached_path
-from model.utils import (
-    get_tokenizer,
-    convert_char_to_pinyin,
-    save_spectrogram,
-)
-from transformers import pipeline
-import librosa
-
-device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
-
-target_sample_rate = 24000
-n_mel_channels = 100
-hop_length = 256
-target_rms = 0.1
-nfe_step = 32  # 16, 32
-cfg_strength = 2.0
-ode_method = 'euler'
-sway_sampling_coef = -1.0
-speed = 1.0
-# fix_duration = 27  # None or float (duration in seconds)
-fix_duration = None
-
-
-class Predictor(BasePredictor):
-    def load_model(exp_name, model_cls, model_cfg, ckpt_step):
-        checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device)
-        vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin")
-        model = CFM(
-            transformer=model_cls(
-                **model_cfg,
-                text_num_embeds=vocab_size,
-                mel_dim=n_mel_channels
-            ),
-            mel_spec_kwargs=dict(
-                target_sample_rate=target_sample_rate,
-                n_mel_channels=n_mel_channels,
-                hop_length=hop_length,
-            ),
-            odeint_kwargs=dict(
-                method=ode_method,
-            ),
-            vocab_char_map=vocab_char_map,
-        ).to(device)
-
-        ema_model = EMA(model, include_online_model=False).to(device)
-        ema_model.load_state_dict(checkpoint['ema_model_state_dict'])
-        ema_model.copy_params_from_ema_to_model()
-
-        return ema_model, model
-    def setup(self) -> None:
-        """Load the model into memory to make running multiple predictions efficient"""
-        # self.model = torch.load("./weights.pth")
-        print("Loading Whisper model...")
-        self.pipe = pipeline(
-            "automatic-speech-recognition",
-            model="openai/whisper-large-v3-turbo",
-            torch_dtype=torch.float16,
-            device=device,
-        )
-        print("Loading F5-TTS model...")
-
-        F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-        self.F5TTS_ema_model, self.F5TTS_base_model = self.load_model("F5TTS_Base", DiT, F5TTS_model_cfg, 1200000)
-
-
-    def predict(
-        self,
-        gen_text: str = Input(description="Text to generate"),
-        ref_audio_orig: Path = Input(description="Reference audio"),
-        remove_silence: bool = Input(description="Remove silences", default=True),
-    ) -> Path:
-        """Run a single prediction on the model"""
-        model_choice = "F5-TTS"
-        print(gen_text)
-        if len(gen_text) > 200:
-            raise gr.Error("Please keep your text under 200 chars.")
-        gr.Info("Converting audio...")
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
-            aseg = AudioSegment.from_file(ref_audio_orig)
-            audio_duration = len(aseg)
-            if audio_duration > 15000:
-                gr.Warning("Audio is over 15s, clipping to only first 15s.")
-                aseg = aseg[:15000]
-            aseg.export(f.name, format="wav")
-            ref_audio = f.name
-        ema_model = self.F5TTS_ema_model
-        base_model = self.F5TTS_base_model
-
-        if not ref_text.strip():
-            gr.Info("No reference text provided, transcribing reference audio...")
-            ref_text = outputs = self.pipe(
-                ref_audio,
-                chunk_length_s=30,
-                batch_size=128,
-                generate_kwargs={"task": "transcribe"},
-                return_timestamps=False,
-            )['text'].strip()
-            gr.Info("Finished transcription")
-        else:
-            gr.Info("Using custom reference text...")
-        audio, sr = torchaudio.load(ref_audio)
-
-        rms = torch.sqrt(torch.mean(torch.square(audio)))
-        if rms < target_rms:
-            audio = audio * target_rms / rms
-        if sr != target_sample_rate:
-            resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
-            audio = resampler(audio)
-        audio = audio.to(device)
-
-        # Prepare the text
-        text_list = [ref_text + gen_text]
-        final_text_list = convert_char_to_pinyin(text_list)
-
-        # Calculate duration
-        ref_audio_len = audio.shape[-1] // hop_length
-        # if fix_duration is not None:
-        #     duration = int(fix_duration * target_sample_rate / hop_length)
-        # else:
-        zh_pause_punc = r"。，、；：？！"
-        ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text))
-        gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text))
-        duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
-
-        # inference
-        gr.Info(f"Generating audio using F5-TTS")
-        with torch.inference_mode():
-            generated, _ = base_model.sample(
-                cond=audio,
-                text=final_text_list,
-                duration=duration,
-                steps=nfe_step,
-                cfg_strength=cfg_strength,
-                sway_sampling_coef=sway_sampling_coef,
-            )
-
-        generated = generated[:, ref_audio_len:, :]
-        generated_mel_spec = rearrange(generated, '1 n d -> 1 d n')
-        gr.Info("Running vocoder")
-        vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
-        generated_wave = vocos.decode(generated_mel_spec.cpu())
-        if rms < target_rms:
-            generated_wave = generated_wave * rms / target_rms
-
-        # wav -> numpy
-        generated_wave = generated_wave.squeeze().cpu().numpy()
-
-        if remove_silence:
-            gr.Info("Removing audio silences... This may take a moment")
-            non_silent_intervals = librosa.effects.split(generated_wave, top_db=30)
-            non_silent_wave = np.array([])
-            for interval in non_silent_intervals:
-                start, end = interval
-                non_silent_wave = np.concatenate([non_silent_wave, generated_wave[start:end]])
-            generated_wave = non_silent_wave
-
-
-        # spectogram
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
-            wav_path = tmp_wav.name
-            torchaudio.save(wav_path, torch.tensor(generated_wave), target_sample_rate)
-
-        return wav_path