287 lines
7.7 KiB
Python
287 lines
7.7 KiB
Python
import os
|
|
import sys
|
|
import argparse
|
|
import codecs
|
|
import re
|
|
from pathlib import Path
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
import soundfile as sf
|
|
# import tomli
|
|
from cached_path import cached_path
|
|
import pycld2 as cld
|
|
import cn2an
|
|
|
|
from f5_tts.model import DiT, UNetT
|
|
from f5_tts.infer.utils_infer import (
|
|
mel_spec_type,
|
|
target_rms,
|
|
cross_fade_duration,
|
|
nfe_step,
|
|
cfg_strength,
|
|
sway_sampling_coef,
|
|
speed,
|
|
fix_duration,
|
|
infer_process,
|
|
load_model,
|
|
load_vocoder,
|
|
preprocess_ref_audio_text,
|
|
remove_silence_for_generated_wav,
|
|
)
|
|
|
|
import json
|
|
from time import time, sleep
|
|
from appPublic.dictObject import DictObject
|
|
from appPublic.folderUtils import temp_file
|
|
from appPublic.jsonConfig import getConfig
|
|
from appPublic.worker import awaitify
|
|
from appPublic.uniqueID import getID
|
|
from appPublic.log import debug, info
|
|
from appPublic.background import Background
|
|
from ahserver.webapp import webapp
|
|
from ahserver.serverenv import ServerEnv
|
|
from ahserver.filestorage import FileStorage
|
|
|
|
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
|
|
|
|
def write_wav_buffer(wav, nchannels, framerate):
|
|
fs = FileStorage()
|
|
fn = fs._name2path(f'{getID()}.wav', userid='tmp')
|
|
os.makedirs(os.path.dirname(fn))
|
|
debug(fn)
|
|
with open(fn, "wb") as f:
|
|
sf.write(f.name, wav, framerate)
|
|
return fs.webpath(fn)
|
|
|
|
async_write_wav_buffer = awaitify(write_wav_buffer)
|
|
|
|
def detect_language(txt):
|
|
isReliable, textBytesFound, details = cld.detect(txt)
|
|
debug(f' detect_language():{isReliable=}, {textBytesFound=}, {details=} ')
|
|
return details[0][1]
|
|
|
|
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_voices()
|
|
|
|
def load_model(self):
|
|
self.vocoder = load_vocoder(vocoder_name=self.config.vocoder_name,
|
|
is_local=True,
|
|
local_path=self.config.vocoder_local_path)
|
|
|
|
# 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 self.config.vocoder_name == "vocos":
|
|
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"))
|
|
# ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors; local path
|
|
elif self.config.vocoder_name == "bigvgan":
|
|
repo_name = "F5-TTS"
|
|
exp_name = "F5TTS_Base_bigvgan"
|
|
ckpt_step = 1250000
|
|
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
|
|
|
|
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,
|
|
mel_spec_type=self.config.vocoder_name,
|
|
vocab_file=self.config.vocab_file)
|
|
self.model = self.model.to(self.config.device)
|
|
|
|
def f5tts_infer(self, ref_audio, ref_text, gen_text):
|
|
audio, final_sample_rate, spectragram = \
|
|
infer_process(ref_audio,
|
|
ref_text,
|
|
gen_text,
|
|
self.model,
|
|
self.vocoder,
|
|
mel_spec_type=self.config.vocoder_name,
|
|
speed=self.config.speed or speed)
|
|
return {
|
|
'audio':audio,
|
|
'sample_rate':final_sample_rate,
|
|
'spectragram':spectragram
|
|
}
|
|
|
|
def get_speakers(self):
|
|
t = [{'value':s, 'text':s} for s in self.speakers.keys() ]
|
|
t.append({'value':'main', 'text':'main'})
|
|
return t
|
|
|
|
async def split_text(self, text_gen, speaker):
|
|
reg1 = r"(?=\[\w+\])"
|
|
lang = await awaitify(detect_language)(text_gen)
|
|
if self.config.language.get(lang):
|
|
reg1 = r"{}".format(self.config.language.get(lang).sentence_splitter)
|
|
if lang == 'zh':
|
|
text_gen = await awaitify(cn2an.transform)(text_gen, 'an2cn')
|
|
|
|
chunks = re.split(reg1, text_gen)
|
|
# reg2 = self.config.speaker_match
|
|
reg2 = r"\[\[(\w+)\]\]"
|
|
ret = []
|
|
for text in chunks:
|
|
if text == ['\r', '']:
|
|
continue
|
|
voice = speaker
|
|
match = re.match(reg2, text)
|
|
if match:
|
|
debug(f'{text=}, match {reg2=}')
|
|
voice = match[1]
|
|
if voice not in self.voices:
|
|
voice = "main"
|
|
debug(f'{text} inferences with speaker({voice})..{reg2=}')
|
|
text = re.sub(reg2, "", text)
|
|
gen_text = text.strip()
|
|
ref_audio = self.voices[voice]["ref_audio"]
|
|
ref_text = self.voices[voice]["ref_text"]
|
|
ret.append({'text':gen_text, 'ref_audio':ref_audio, 'ref_text':ref_text})
|
|
return ret
|
|
|
|
async def infer_stream(self, prompt, speaker):
|
|
async for a in self._inference_stream(prompt, speaker):
|
|
wavdata = a['audio']
|
|
samplerate = a['sample_rate']
|
|
b = await async_write_wav_buffer(wavdata, 1, samplerate)
|
|
yield b
|
|
|
|
async def _inference_stream(self, prompt, speaker):
|
|
text_gen = prompt
|
|
chunks = await self.split_text(prompt, speaker)
|
|
for chunk in chunks:
|
|
gen_text = chunk['text']
|
|
ref_audio = chunk['ref_audio']
|
|
ref_text = chunk['ref_text']
|
|
infer = awaitify(self.f5tts_infer)
|
|
try:
|
|
d = await infer(ref_audio, ref_text, gen_text)
|
|
yield d
|
|
except:
|
|
pass
|
|
|
|
def setup_voices(self):
|
|
config = getConfig()
|
|
d = None
|
|
with codecs.open(config.speakers_file, 'r', 'utf-8') as f:
|
|
b = f.read()
|
|
self.speakers = json.loads(b)
|
|
ref_audio, ref_text = preprocess_ref_audio_text(config.ref_audio, config.ref_text)
|
|
self.voices = {
|
|
"main":{
|
|
'ref_text':ref_text,
|
|
'ref_audio':ref_audio
|
|
}
|
|
}
|
|
for k,v in self.speakers.items():
|
|
ref_audio, ref_text = preprocess_ref_audio_text(v['ref_audio'], v['ref_text'])
|
|
self.voices[k] = {
|
|
'ref_text':ref_text,
|
|
'ref_audio':ref_audio
|
|
}
|
|
|
|
async def add_voice(self, speaker, ref_audio, ref_text):
|
|
debug(f'{speaker=}, {ref_audio=}, {ref_text=}');
|
|
config = getConfig()
|
|
ref_audio = FileStorage().realPath(ref_audio)
|
|
self.speakers[speaker] = {
|
|
'ref_text':ref_text,
|
|
'ref_audio':ref_audio
|
|
}
|
|
f = awaitify(preprocess_ref_audio_text)
|
|
ref_audio, ref_text = await f(ref_audio, ref_text)
|
|
self.voices[speaker] = {
|
|
'ref_text':ref_text,
|
|
'ref_audio':ref_audio
|
|
}
|
|
with codecs.open(config.speakers_file, 'w', 'utf-8') as f:
|
|
f.write(json.dumps(self.speakers, indent=4))
|
|
return None
|
|
|
|
async def _inference(self, prompt, speaker):
|
|
generated_audio_segments = []
|
|
remove_silence = self.config.remove_silence or False
|
|
final_sample_rate = 24000
|
|
async for d in self._inference_stream(prompt, speaker):
|
|
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)
|
|
return await async_write_wav_buffer(final_wave, 1, final_sample_rate)
|
|
|
|
def UiError(title="出错", message="出错啦", timeout=5):
|
|
return {
|
|
"widgettype":"Error",
|
|
"options":{
|
|
"author":"tr",
|
|
"timeout":timeout,
|
|
"cwidth":15,
|
|
"cheight":10,
|
|
"title":title,
|
|
"auto_open":True,
|
|
"auto_dismiss":True,
|
|
"auto_destroy":True,
|
|
"message":message
|
|
}
|
|
}
|
|
|
|
|
|
def UiMessage(title="消息", message="后台消息", timeout=5):
|
|
return {
|
|
"widgettype":"Message",
|
|
"options":{
|
|
"author":"tr",
|
|
"timeout":timeout,
|
|
"cwidth":15,
|
|
"cheight":10,
|
|
"title":title,
|
|
"auto_open":True,
|
|
"auto_dismiss":True,
|
|
"auto_destroy":True,
|
|
"message":message
|
|
}
|
|
}
|
|
|
|
def test1():
|
|
sleep(36000)
|
|
return {}
|
|
|
|
def init():
|
|
g = ServerEnv()
|
|
f5 = F5TTS()
|
|
g.infer_stream = f5.infer_stream
|
|
g.get_speakers = f5.get_speakers
|
|
g.infer = f5._inference
|
|
g.test1 = awaitify(test1)
|
|
g.add_voice = f5.add_voice
|
|
g.UiError = UiError
|
|
g.UiMessage = UiMessage
|
|
|
|
if __name__ == '__main__':
|
|
webapp(init)
|