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| 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, | |
| ): | |
| # Initialize parameters | |
| 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 | |
| # Set device | |
| self.device = device or ( | |
| "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" | |
| ) | |
| # Load models | |
| 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, # random seed = -1 | |
| ) | |
| print("seed :", f5tts.seed) | |