Create cog.py

cog.py

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+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+from cog import BasePredictor, Input, Path
+
+import os
+import re
+import torch
+import torchaudio
+import gradio as gr
+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