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import random
import sys
from importlib.resources import files
import soundfile as sf
import torch
import tqdm
from cached_path import cached_path
from f5_tts.infer.utils_infer import (
hop_length,
infer_process,
load_model,
load_vocoder,
preprocess_ref_audio_text,
remove_silence_for_generated_wav,
save_spectrogram,
target_sample_rate,
)
from f5_tts.model import DiT, UNetT
from f5_tts.model.utils import seed_everything
class F5TTS:
def __init__(
self,
model_type="F5-TTS",
ckpt_file="",
vocab_file="",
ode_method="euler",
use_ema=True,
vocoder_name="vocos",
local_path=None,
device=None,
):
# Initialize parameters
self.final_wave = None
self.target_sample_rate = target_sample_rate
self.hop_length = hop_length
self.seed = -1
self.mel_spec_type = vocoder_name
# 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(vocoder_name, local_path)
self.load_ema_model(model_type, ckpt_file, vocoder_name, vocab_file, ode_method, use_ema)
def load_vocoder_model(self, vocoder_name, local_path):
self.vocoder = load_vocoder(vocoder_name, local_path is not None, local_path, self.device)
def load_ema_model(self, model_type, ckpt_file, mel_spec_type, vocab_file, ode_method, use_ema):
if model_type == "F5-TTS":
if not ckpt_file:
if mel_spec_type == "vocos":
ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))
elif mel_spec_type == "bigvgan":
ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base_bigvgan/model_1250000.pt"))
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, mel_spec_type, 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
ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text, device=self.device)
wav, sr, spect = 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_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, # random seed = -1
)
print("seed :", f5tts.seed)
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