f5tts/app/f5tts.py
2024-12-19 16:50:53 +08:00

151 lines
4.1 KiB
Python

import sys
import argparse
import codecs
import re
from pathlib import Path
import numpy as np
import soundfile as sf
# import tomli
from cached_path import cached_path
from f5_tts.model import DiT, UNetT
from f5_tts.model.utils_infer import (
load_vocoder,
load_model,
preprocess_ref_audio_text,
infer_process,
remove_silence_for_generated_wav,
)
import os
import json
from time import time
from appPublic.dictObject import DictObject
from appPublic.folderUtils import temp_file
from appPublic.jsonConfig import getConfig
from appPublic.worker import awaitify
from ahserver.webapp import webapp
from ahserver.serverEnv import ServerEnv
n_mel_channels = 100
hop_length = 256
target_rms = 0.1
nfe_step = 32 # 16, 32
cfg_strength = 2.0
ode_method = "euler"
sway_sampling_coef = -1.0
speed = 1.0
class F5TTS:
def __init__(self):
self.config = getConfig()
# self.vocos = load_vocoder(is_local=True, local_path="../checkpoints/charactr/vocos-mel-24khz")
self.load_model()
self.setup_voice()
def load_model(self):
# load models
ckpt_file = ''
if self.config.modelname == "F5-TTS":
model_cls = DiT
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
if ckpt_file == "":
repo_name = "F5-TTS"
exp_name = "F5TTS_Base"
ckpt_step = 1200000
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
elif self.config.modelname == "E2-TTS":
model_cls = UNetT
model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
if ckpt_file == "":
repo_name = "E2-TTS"
exp_name = "E2TTS_Base"
ckpt_step = 1200000
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
self.model = load_model(model_cls, model_cfg, ckpt_file,
self.config.vocab_file)
self.model = self.model.to(self.config.device)
def setup_voice(self):
main_voice = {"ref_audio": self.config.ref_audio_fn,
"ref_text": self.config.ref_text}
if "voices" not in self.config:
voices = {"main": main_voice}
else:
voices = self.config["voices"]
voices["main"] = main_voice
for voice in voices:
voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
voices[voice]["ref_audio"], voices[voice]["ref_text"]
)
print("Voice:", voice)
print("Ref_audio:", voices[voice]["ref_audio"])
print("Ref_text:", voices[voice]["ref_text"])
self.voices = voices
def _inference_stream(self, prompt):
text_gen = prompt
remove_silence = False
generated_audio_segments = []
reg1 = r"(?=\[\w+\])"
chunks = re.split(reg1, text_gen)
reg2 = r"\[(\w+)\]"
for text in chunks:
match = re.match(reg2, text)
if match:
voice = match[1]
else:
print("No voice tag found, using main.")
voice = "main"
if voice not in self.voices:
print(f"Voice {voice} not found, using main.")
voice = "main"
text = re.sub(reg2, "", text)
gen_text = text.strip()
ref_audio = self.voices[voice]["ref_audio"]
ref_text = self.voices[voice]["ref_text"]
print(f"Voice: {voice}, {self.model}")
audio, final_sample_rate, spectragram = \
infer_process(ref_audio, ref_text, gen_text, self.model)
yield {
'audio':audio,
'sample_rate':final_sample_rate,
'spectragram':spectragram,
'finish':False
}
yield {
'finish':True
}
def _inference(self, prompt):
generated_audio_segments = []
remove_silence = self.config.remove_silence or False
final_sample_rate = 24000
for d in self._inference_stream(prompt):
if not d['finish']:
audio = d['audio']
final_sample_rate = d['sample_rate']
generated_audio_segments.append(audio)
if generated_audio_segments:
final_wave = np.concatenate(generated_audio_segments)
fn = temp_file(suffix='.wav')
with open(fn, "wb") as f:
sf.write(f.name, final_wave, final_sample_rate)
# Remove silence
if remove_silence:
remove_silence_for_generated_wav(f.name)
return fn
def init():
g = ServerEnv()
f5 = F5TTS()
g.infer_stream = awaitify(f5._inference_stream)
g.infer = awaitify(f5._inference)
if __name__ == '__main__':
webapp(init)