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app.py
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app.py
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import os
import re
import sys
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import io
import json
import torchaudio
import wave
from pathlib import Path
print('Starting...')
import shutil
import time
import torch
import torch._dynamo
torch._dynamo.config.suppress_errors = True
torch._dynamo.config.cache_size_limit = 64
torch._dynamo.config.suppress_errors = True
torch.set_float32_matmul_precision('high')
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
import subprocess
import soundfile as sf
import ChatTTS
import datetime
from dotenv import load_dotenv
load_dotenv()
from flask import Flask, request, render_template, jsonify, send_from_directory,send_file,Response, stream_with_context
import logging
from logging.handlers import RotatingFileHandler
from waitress import serve
from random import random
from modelscope import snapshot_download
import numpy as np
import threading
from uilib.cfg import WEB_ADDRESS, SPEAKER_DIR, LOGS_DIR, WAVS_DIR, MODEL_DIR, ROOT_DIR
from uilib import utils,VERSION
from ChatTTS.utils import select_device
from uilib.utils import is_chinese_os,modelscope_status
merge_size=int(os.getenv('merge_size',10))
env_lang=os.getenv('lang','')
if env_lang=='zh':
is_cn= True
elif env_lang=='en':
is_cn=False
else:
is_cn=is_chinese_os()
if not shutil.which("ffmpeg"):
print('请先安装ffmpeg')
time.sleep(60)
exit()
chat = ChatTTS.Chat()
device_str=os.getenv('device','default')
if device_str in ['default','mps']:
device=select_device(min_memory=2047,experimental=True if device_str=='mps' else False)
elif device_str =='cuda':
device=select_device(min_memory=2047)
elif device_str == 'cpu':
device = torch.device("cpu")
chat.load(source="local" if not os.path.exists(MODEL_DIR+"/DVAE_full.pt") else 'custom',custom_path=ROOT_DIR, device=device,compile=True if os.getenv('compile','true').lower()!='false' else False)
# 配置日志
# 禁用 Werkzeug 默认的日志处理器
log = logging.getLogger('werkzeug')
log.handlers[:] = []
log.setLevel(logging.WARNING)
app = Flask(__name__,
static_folder=ROOT_DIR+'/static',
static_url_path='/static',
template_folder=ROOT_DIR+'/templates')
root_log = logging.getLogger() # Flask的根日志记录器
root_log.handlers = []
root_log.setLevel(logging.WARNING)
app.logger.setLevel(logging.WARNING)
# 创建 RotatingFileHandler 对象,设置写入的文件路径和大小限制
file_handler = RotatingFileHandler(LOGS_DIR+f'/{datetime.datetime.now().strftime("%Y%m%d")}.log', maxBytes=1024 * 1024, backupCount=5)
# 创建日志的格式
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# 设置文件处理器的级别和格式
file_handler.setLevel(logging.WARNING)
file_handler.setFormatter(formatter)
# 将文件处理器添加到日志记录器中
app.logger.addHandler(file_handler)
app.jinja_env.globals.update(enumerate=enumerate)
@app.route('/static/<path:filename>')
def static_files(filename):
return send_from_directory(app.config['STATIC_FOLDER'], filename)
@app.route('/')
def index():
speakers=utils.get_speakers()
return render_template(
f"index{'' if is_cn else 'en'}.html",
weburl=WEB_ADDRESS,
speakers=speakers,
version=VERSION
)
# 根据文本返回tts结果,返回 filename=文件名 url=可下载地址
# 请求端根据需要自行选择使用哪个
# params:
#
# text:待合成文字
# prompt:
# voice:音色
# custom_voice:自定义音色值
# skip_refine: 1=跳过refine_text阶段,0=不跳过
# temperature
# top_p
# top_k
# speed
# text_seed
# refine_max_new_token
# infer_max_new_token
# wav
audio_queue=[]
@app.route('/tts', methods=['GET', 'POST'])
def tts():
global audio_queue
# 原始字符串
text = request.args.get("text","").strip() or request.form.get("text","").strip()
prompt = request.args.get("prompt","").strip() or request.form.get("prompt",'')
# 默认值
defaults = {
"custom_voice": 0,
"voice": "2222",
"temperature": 0.3,
"top_p": 0.7,
"top_k": 20,
"skip_refine": 0,
"speed":5,
"text_seed":42,
"refine_max_new_token": 384,
"infer_max_new_token": 2048,
"wav": 0,
"is_stream":0
}
# 获取
custom_voice = utils.get_parameter(request, "custom_voice", defaults["custom_voice"], int)
voice = str(custom_voice) if custom_voice > 0 else utils.get_parameter(request, "voice", defaults["voice"], str)
temperature = utils.get_parameter(request, "temperature", defaults["temperature"], float)
top_p = utils.get_parameter(request, "top_p", defaults["top_p"], float)
top_k = utils.get_parameter(request, "top_k", defaults["top_k"], int)
skip_refine = utils.get_parameter(request, "skip_refine", defaults["skip_refine"], int)
is_stream = utils.get_parameter(request, "is_stream", defaults["is_stream"], int)
speed = utils.get_parameter(request, "speed", defaults["speed"], int)
text_seed = utils.get_parameter(request, "text_seed", defaults["text_seed"], int)
refine_max_new_token = utils.get_parameter(request, "refine_max_new_token", defaults["refine_max_new_token"], int)
infer_max_new_token = utils.get_parameter(request, "infer_max_new_token", defaults["infer_max_new_token"], int)
wav = utils.get_parameter(request, "wav", defaults["wav"], int)
app.logger.info(f"[tts]{text=}\n{voice=},{skip_refine=}\n")
if not text:
return jsonify({"code": 1, "msg": "text params lost"})
# 固定音色
rand_spk=None
# voice可能是 {voice}.csv or {voice}.pt or number
voice=voice.replace('.csv','.pt')
seed_path=f'{SPEAKER_DIR}/{voice}'
print(f'{voice=}')
#if voice.endswith('.csv') and os.path.exists(seed_path):
# rand_spk=utils.load_speaker(voice)
# print(f'当前使用音色 {seed_path=}')
#el
if voice.endswith('.pt') and os.path.exists(seed_path):
#如果.env中未指定设备,则使用 ChatTTS相同算法找设备,否则使用指定设备
rand_spk=torch.load(seed_path, map_location=device)
print(f'当前使用音色 {seed_path=}')
# 否则 判断是否存在 {voice}.csv
#elif os.path.exists(f'{SPEAKER_DIR}/{voice}.csv'):
# rand_spk=utils.load_speaker(voice)
# print(f'当前使用音色 {SPEAKER_DIR}/{voice}.csv')
if rand_spk is None:
print(f'当前使用音色:根据seed={voice}获取随机音色')
voice_int=re.findall(r'^(\d+)',voice)
if len(voice_int)>0:
voice=int(voice_int[0])
else:
voice=2222
torch.manual_seed(voice)
#std, mean = chat.sample_random_speaker
rand_spk = chat.sample_random_speaker()
#rand_spk = torch.randn(768) * std + mean
# 保存音色
torch.save(rand_spk,f"{SPEAKER_DIR}/{voice}.pt")
#utils.save_speaker(voice,rand_spk)
audio_files = []
start_time = time.time()
# 中英按语言分行
text_list=[t.strip() for t in text.split("\n") if t.strip()]
new_text=utils.split_text(text_list)
if text_seed>0:
torch.manual_seed(text_seed)
params_infer_code = ChatTTS.Chat.InferCodeParams(
spk_emb=rand_spk,
prompt=f"[speed_{speed}]",
top_P=top_p,
top_K=top_k,
temperature=temperature,
max_new_token=infer_max_new_token
)
params_refine_text = ChatTTS.Chat.RefineTextParams(
prompt=prompt,
top_P=top_p,
top_K=top_k,
temperature=temperature,
max_new_token=refine_max_new_token
)
print(f'{prompt=}')
# 将少于30个字符的行同其他行拼接
retext=[]
short_text=""
for it in new_text:
if len(it)<30:
short_text+=f"{it} [uv_break] "
if len(short_text)>30:
retext.append(short_text)
short_text=""
else:
retext.append(short_text+it)
short_text=""
if len(short_text)>30 or len(retext)<1:
retext.append(short_text)
elif short_text:
retext[-1]+=f" [uv_break] {short_text}"
new_text=retext
new_text_list=[new_text[i:i+merge_size] for i in range(0,len(new_text),merge_size)]
filename_list=[]
audio_time=0
inter_time=0
for i,te in enumerate(new_text_list):
print(f'{te=}')
wavs = chat.infer(
te,
#use_decoder=False,
stream=True if is_stream==1 else False,
skip_refine_text=skip_refine,
do_text_normalization=False,
do_homophone_replacement=True,
params_refine_text=params_refine_text,
params_infer_code=params_infer_code
)
end_time = time.time()
inference_time = end_time - start_time
inference_time_rounded = round(inference_time, 2)
inter_time+=inference_time_rounded
print(f"推理时长: {inference_time_rounded} 秒")
for j,w in enumerate(wavs):
filename = datetime.datetime.now().strftime('%H%M%S_')+f"use{inference_time_rounded}s-seed{voice}-te{temperature}-tp{top_p}-tk{top_k}-textlen{len(text)}-{str(random())[2:7]}" + f"-{i}-{j}.wav"
filename_list.append(filename)
torchaudio.save(WAVS_DIR+'/'+filename, torch.from_numpy(w).unsqueeze(0), 24000)
txt_tmp="\n".join([f"file '{WAVS_DIR}/{it}'" for it in filename_list])
txt_name=f'{time.time()}.txt'
with open(f'{WAVS_DIR}/{txt_name}','w',encoding='utf-8') as f:
f.write(txt_tmp)
outname=datetime.datetime.now().strftime('%H%M%S_')+f"use{inter_time}s-audio{audio_time}s-seed{voice}-te{temperature}-tp{top_p}-tk{top_k}-textlen{len(text)}-{str(random())[2:7]}" + "-merge.wav"
try:
subprocess.run(["ffmpeg","-hide_banner", "-ignore_unknown","-y","-f","concat","-safe","0","-i",f'{WAVS_DIR}/{txt_name}',"-c:a","copy",WAVS_DIR + '/' + outname],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
encoding="utf-8",
check=True,
text=True,
creationflags=0 if sys.platform != 'win32' else subprocess.CREATE_NO_WINDOW)
except Exception as e:
return jsonify({"code":1,"msg":str(e)})
audio_files.append({
"filename": WAVS_DIR + '/' + outname,
"url": f"http://{request.host}/static/wavs/{outname}",
"inference_time": round(inter_time,2),
"audio_duration": -1
})
result_dict={"code": 0, "msg": "ok", "audio_files": audio_files}
try:
if torch.cuda.is_available():
torch.cuda.empty_cache()
except Exception:
pass
# 兼容pyVideoTrans接口调用
if len(audio_files)==1:
result_dict["filename"]=audio_files[0]['filename']
result_dict["url"]=audio_files[0]['url']
if wav>0:
return send_file(audio_files[0]['filename'], mimetype='audio/x-wav')
else:
return jsonify(result_dict)
@app.route('/clear_wavs', methods=['POST'])
def clear_wavs():
dir_path = 'static/wavs' # wav音频文件存储目录
success, message = utils.ClearWav(dir_path)
if success:
return jsonify({"code": 0, "msg": message})
else:
return jsonify({"code": 1, "msg": message})
try:
host = WEB_ADDRESS.split(':')
print(f'Start:{WEB_ADDRESS}')
threading.Thread(target=utils.openweb,args=(f'http://{WEB_ADDRESS}',)).start()
serve(app,host=host[0], port=int(host[1]))
except Exception as e:
print(e)