Spaces:
Sleeping
Sleeping
File size: 4,280 Bytes
9c54d62 304bc5c 9c54d62 304bc5c 9c54d62 118c154 9c54d62 118c154 9c54d62 304bc5c 9c54d62 b624c42 9c54d62 304bc5c 9c54d62 304bc5c 9c54d62 b624c42 831ba2e 9c54d62 304bc5c 9c54d62 304bc5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
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)
|