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import soundfile as sf |
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import torch |
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import tqdm |
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from cached_path import cached_path |
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from model import DiT, UNetT |
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from model.utils import save_spectrogram |
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from model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav |
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from model.utils import seed_everything |
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import random |
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import sys |
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class F5TTS: |
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def __init__( |
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self, |
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model_type="F5-TTS", |
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ckpt_file="", |
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vocab_file="", |
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ode_method="euler", |
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use_ema=True, |
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local_path=None, |
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device=None, |
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): |
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self.final_wave = None |
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self.target_sample_rate = 24000 |
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self.n_mel_channels = 100 |
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self.hop_length = 256 |
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self.target_rms = 0.1 |
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self.seed = -1 |
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self.device = device or ( |
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"cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" |
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) |
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self.load_vocoder_model(local_path) |
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self.load_ema_model(model_type, ckpt_file, vocab_file, ode_method, use_ema) |
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def load_vocoder_model(self, local_path): |
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self.vocos = load_vocoder(local_path is not None, local_path, self.device) |
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def load_ema_model(self, model_type, ckpt_file, vocab_file, ode_method, use_ema): |
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if model_type == "F5-TTS": |
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if not ckpt_file: |
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ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")) |
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model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) |
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model_cls = DiT |
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elif model_type == "E2-TTS": |
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if not ckpt_file: |
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ckpt_file = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors")) |
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model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) |
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model_cls = UNetT |
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else: |
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raise ValueError(f"Unknown model type: {model_type}") |
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self.ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file, ode_method, use_ema, self.device) |
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def export_wav(self, wav, file_wave, remove_silence=False): |
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sf.write(file_wave, wav, self.target_sample_rate) |
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if remove_silence: |
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remove_silence_for_generated_wav(file_wave) |
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def export_spectrogram(self, spect, file_spect): |
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save_spectrogram(spect, file_spect) |
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def infer( |
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self, |
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ref_file, |
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ref_text, |
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gen_text, |
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show_info=print, |
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progress=tqdm, |
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target_rms=0.1, |
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cross_fade_duration=0.15, |
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sway_sampling_coef=-1, |
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cfg_strength=2, |
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nfe_step=32, |
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speed=1.0, |
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fix_duration=None, |
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remove_silence=False, |
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file_wave=None, |
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file_spect=None, |
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seed=-1, |
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): |
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if seed == -1: |
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seed = random.randint(0, sys.maxsize) |
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seed_everything(seed) |
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self.seed = seed |
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wav, sr, spect = infer_process( |
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ref_file, |
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ref_text, |
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gen_text, |
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self.ema_model, |
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show_info=show_info, |
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progress=progress, |
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target_rms=target_rms, |
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cross_fade_duration=cross_fade_duration, |
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nfe_step=nfe_step, |
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cfg_strength=cfg_strength, |
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sway_sampling_coef=sway_sampling_coef, |
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speed=speed, |
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fix_duration=fix_duration, |
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device=self.device, |
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) |
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if file_wave is not None: |
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self.export_wav(wav, file_wave, remove_silence) |
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if file_spect is not None: |
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self.export_spectrogram(spect, file_spect) |
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return wav, sr, spect |
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if __name__ == "__main__": |
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f5tts = F5TTS() |
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wav, sr, spect = f5tts.infer( |
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ref_file="tests/ref_audio/test_en_1_ref_short.wav", |
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ref_text="some call me nature, others call me mother nature.", |
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gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""", |
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file_wave="tests/out.wav", |
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file_spect="tests/out.png", |
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seed=-1, |
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) |
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print("seed :", f5tts.seed) |
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