| | import random |
| | import sys |
| | from importlib.resources import files |
| |
|
| | import soundfile as sf |
| | import tqdm |
| | from cached_path import cached_path |
| | from hydra.utils import get_class |
| | from omegaconf import OmegaConf |
| |
|
| | from f5_tts.infer.utils_infer import ( |
| | infer_process, |
| | load_model, |
| | load_vocoder, |
| | preprocess_ref_audio_text, |
| | remove_silence_for_generated_wav, |
| | save_spectrogram, |
| | transcribe, |
| | ) |
| | from f5_tts.model.utils import seed_everything |
| |
|
| |
|
| | class F5TTS: |
| | def __init__( |
| | self, |
| | model="F5TTS_v1_Base", |
| | ckpt_file="", |
| | vocab_file="", |
| | ode_method="euler", |
| | use_ema=True, |
| | vocoder_local_path=None, |
| | device=None, |
| | hf_cache_dir=None, |
| | ): |
| | model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{model}.yaml"))) |
| | model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}") |
| | model_arc = model_cfg.model.arch |
| |
|
| | self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type |
| | self.target_sample_rate = model_cfg.model.mel_spec.target_sample_rate |
| |
|
| | self.ode_method = ode_method |
| | self.use_ema = use_ema |
| |
|
| | if device is not None: |
| | self.device = device |
| | else: |
| | import torch |
| |
|
| | self.device = ( |
| | "cuda" |
| | if torch.cuda.is_available() |
| | else "xpu" |
| | if torch.xpu.is_available() |
| | else "mps" |
| | if torch.backends.mps.is_available() |
| | else "cpu" |
| | ) |
| |
|
| | |
| | self.vocoder = load_vocoder( |
| | self.mel_spec_type, vocoder_local_path is not None, vocoder_local_path, self.device, hf_cache_dir |
| | ) |
| |
|
| | repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors" |
| |
|
| | |
| | if model == "F5TTS_Base": |
| | if self.mel_spec_type == "vocos": |
| | ckpt_step = 1200000 |
| | elif self.mel_spec_type == "bigvgan": |
| | model = "F5TTS_Base_bigvgan" |
| | ckpt_type = "pt" |
| | elif model == "E2TTS_Base": |
| | repo_name = "E2-TTS" |
| | ckpt_step = 1200000 |
| |
|
| | if not ckpt_file: |
| | ckpt_file = str( |
| | cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}", cache_dir=hf_cache_dir) |
| | ) |
| | self.ema_model = load_model( |
| | model_cls, model_arc, ckpt_file, self.mel_spec_type, vocab_file, self.ode_method, self.use_ema, self.device |
| | ) |
| |
|
| | def transcribe(self, ref_audio, language=None): |
| | return transcribe(ref_audio, language) |
| |
|
| | 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, spec, file_spec): |
| | save_spectrogram(spec, file_spec) |
| |
|
| | 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_spec=None, |
| | seed=None, |
| | ): |
| | if seed is None: |
| | seed = random.randint(0, sys.maxsize) |
| | seed_everything(seed) |
| | self.seed = seed |
| |
|
| | ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text) |
| |
|
| | wav, sr, spec = infer_process( |
| | ref_file, |
| | ref_text, |
| | gen_text, |
| | self.ema_model, |
| | self.vocoder, |
| | self.mel_spec_type, |
| | 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_spec is not None: |
| | self.export_spectrogram(spec, file_spec) |
| |
|
| | return wav, sr, spec |
| |
|
| |
|
| | if __name__ == "__main__": |
| | f5tts = F5TTS() |
| |
|
| | wav, sr, spec = 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_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")), |
| | seed=None, |
| | ) |
| |
|
| | print("seed :", f5tts.seed) |
| |
|
