| import soundfile as sf |
| import torch |
| import tqdm |
| from cached_path import cached_path |
|
|
| from model import DiT, UNetT |
| from model.utils import save_spectrogram |
|
|
| from model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav |
| from model.utils import seed_everything |
| import random |
| import sys |
|
|
|
|
| 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="tests/ref_audio/test_en_1_ref_short.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="tests/out.wav", |
| file_spect="tests/out.png", |
| seed=-1, |
| ) |
|
|
| print("seed :", f5tts.seed) |
|
|
