| | import random |
| | import sys |
| | import tqdm |
| | from importlib.resources import files |
| |
|
| | import soundfile as sf |
| | import torch |
| | from cached_path import cached_path |
| |
|
| | from f5_tts.model import DiT, UNetT |
| | from f5_tts.model.utils import seed_everything |
| | from f5_tts.infer.utils_infer import ( |
| | load_vocoder, |
| | load_model, |
| | infer_process, |
| | remove_silence_for_generated_wav, |
| | save_spectrogram, |
| | ) |
| |
|
| |
|
| | class F5TTS: |
| | def __init__( |
| | self, |
| | model_type="F5-TTS", |
| | ckpt_file="", |
| | vocab_file="", |
| | ode_method="euler", |
| | use_ema=True, |
| | local_path=None, |
| | device=None, |
| | ): |
| | |
| | self.final_wave = None |
| | self.target_sample_rate = 24000 |
| | self.n_mel_channels = 100 |
| | self.hop_length = 256 |
| | self.target_rms = 0.1 |
| | self.seed = -1 |
| |
|
| | |
| | self.device = device or ( |
| | "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" |
| | ) |
| |
|
| | |
| | self.load_vocoder_model(local_path) |
| | self.load_ema_model(model_type, ckpt_file, vocab_file, ode_method, use_ema) |
| |
|
| | def load_vocoder_model(self, local_path): |
| | self.vocos = load_vocoder(local_path is not None, local_path, self.device) |
| |
|
| | def load_ema_model(self, model_type, ckpt_file, vocab_file, ode_method, use_ema): |
| | if model_type == "F5-TTS": |
| | if not ckpt_file: |
| | ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")) |
| | model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) |
| | model_cls = DiT |
| | elif model_type == "E2-TTS": |
| | if not ckpt_file: |
| | ckpt_file = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors")) |
| | model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) |
| | model_cls = UNetT |
| | else: |
| | raise ValueError(f"Unknown model type: {model_type}") |
| |
|
| | self.ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file, ode_method, use_ema, self.device) |
| |
|
| | def export_wav(self, wav, file_wave, remove_silence=False): |
| | sf.write(file_wave, wav, self.target_sample_rate) |
| |
|
| | if remove_silence: |
| | remove_silence_for_generated_wav(file_wave) |
| |
|
| | def export_spectrogram(self, spect, file_spect): |
| | save_spectrogram(spect, file_spect) |
| |
|
| | def infer( |
| | self, |
| | ref_file, |
| | ref_text, |
| | gen_text, |
| | show_info=print, |
| | progress=tqdm, |
| | target_rms=0.1, |
| | cross_fade_duration=0.15, |
| | sway_sampling_coef=-1, |
| | cfg_strength=2, |
| | nfe_step=32, |
| | speed=1.0, |
| | fix_duration=None, |
| | remove_silence=False, |
| | file_wave=None, |
| | file_spect=None, |
| | seed=-1, |
| | ): |
| | if seed == -1: |
| | seed = random.randint(0, sys.maxsize) |
| | seed_everything(seed) |
| | self.seed = seed |
| | wav, sr, spect = infer_process( |
| | ref_file, |
| | ref_text, |
| | gen_text, |
| | self.ema_model, |
| | show_info=show_info, |
| | progress=progress, |
| | target_rms=target_rms, |
| | cross_fade_duration=cross_fade_duration, |
| | nfe_step=nfe_step, |
| | cfg_strength=cfg_strength, |
| | sway_sampling_coef=sway_sampling_coef, |
| | speed=speed, |
| | fix_duration=fix_duration, |
| | device=self.device, |
| | ) |
| |
|
| | if file_wave is not None: |
| | self.export_wav(wav, file_wave, remove_silence) |
| |
|
| | if file_spect is not None: |
| | self.export_spectrogram(spect, file_spect) |
| |
|
| | return wav, sr, spect |
| |
|
| |
|
| | if __name__ == "__main__": |
| | f5tts = F5TTS() |
| |
|
| | wav, sr, spect = f5tts.infer( |
| | ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")), |
| | ref_text="some call me nature, others call me mother nature.", |
| | 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.""", |
| | file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")), |
| | file_spect=str(files("f5_tts").joinpath("../../tests/api_out.png")), |
| | seed=-1, |
| | ) |
| |
|
| | print("seed :", f5tts.seed) |
| |
|
