Spaces:
Running
Running
jack.li
commited on
Commit
·
6340f23
1
Parent(s):
dc02c06
add api
Browse files- Dockerfile +1 -1
- __pycache__/main.cpython-310.pyc +0 -0
- __pycache__/model.cpython-310.pyc +0 -0
- __pycache__/my_utils.cpython-310.pyc +0 -0
- app.py +2 -2
- download.py +7 -0
- main.py +46 -0
- model.py +760 -0
- requirements.txt +3 -1
- static/en/s1.mp3 +0 -0
- static/en/s2.mp3 +0 -0
- static/zh/s1.mp3 +0 -0
- static/zh/s2.mp3 +0 -0
- text/__pycache__/__init__.cpython-310.pyc +0 -0
- text/__pycache__/chinese.cpython-310.pyc +0 -0
- text/__pycache__/cleaner.cpython-310.pyc +0 -0
- text/__pycache__/symbols.cpython-310.pyc +0 -0
Dockerfile
CHANGED
@@ -25,7 +25,7 @@ ENV HOME=/home/user \
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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-
RUN pip install tqdm
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COPY ./download.py .
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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RUN pip install tqdm nltk
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COPY ./download.py .
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__pycache__/main.cpython-310.pyc
ADDED
Binary file (1.25 kB). View file
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__pycache__/model.cpython-310.pyc
ADDED
Binary file (21 kB). View file
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__pycache__/my_utils.cpython-310.pyc
CHANGED
Binary files a/__pycache__/my_utils.cpython-310.pyc and b/__pycache__/my_utils.cpython-310.pyc differ
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app.py
CHANGED
@@ -27,8 +27,8 @@ logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("multipart").setLevel(logging.WARNING)
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-
from download import *
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download()
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("multipart").setLevel(logging.WARNING)
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# from download import *
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# download()
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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download.py
CHANGED
@@ -43,5 +43,12 @@ def download():
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os.remove(output)
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if __name__ == '__main__':
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download()
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os.remove(output)
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def download_nltk_data():
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import nltk
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nltk.download('averaged_perceptron_tagger')
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nltk.download('cmudict')
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if __name__ == '__main__':
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download()
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main.py
ADDED
@@ -0,0 +1,46 @@
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from fastapi import FastAPI, Body, File, Form, UploadFile
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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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from model import clone_voice
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import os
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from enum import Enum
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import uvicorn
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app = FastAPI()
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app.mount("/static", StaticFiles(directory="static"), name="static")
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class Language(str, Enum):
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en = "English"
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zh = "中文"
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class DefaultVoice(str, Enum):
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zhs1 = "static/zh/s1.mp3"
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zhs2 = "static/zh/s2.mp3"
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ens1 = "static/en/s1.mp3"
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ens2 = "static/en/s2.mp3"
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@app.post("/tts")
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async def tts(
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custom_voice_file: UploadFile = File(None, description="用户自定义声音"),
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language: Language = Form(..., description="语言选择"),
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voice: DefaultVoice = Form(None),
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text: str = Form(..., description="转换文本")
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):
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os.makedirs("static/tmp", exist_ok=True)
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if custom_voice_file is not None:
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content = await file.read()
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filename = f"static/tmp/{file.filename}"
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with open(filename, "wb") as f:
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f.write(content)
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voice = filename
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wav_path = clone_voice(
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user_voice=voice, user_text=text, user_lang=language)
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return FileResponse(wav_path)
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if __name__ == '__main__':
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uvicorn.run(app="main:app", port=int(7860), host="0.0.0.0")
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model.py
ADDED
@@ -0,0 +1,760 @@
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1 |
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import gradio as gr
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2 |
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import numpy as np
|
3 |
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import soundfile as sf
|
4 |
+
from datetime import datetime
|
5 |
+
from time import time as ttime
|
6 |
+
from my_utils import load_audio
|
7 |
+
from transformers import pipeline
|
8 |
+
from text.cleaner import clean_text
|
9 |
+
from polyglot.detect import Detector
|
10 |
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from feature_extractor import cnhubert
|
11 |
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from timeit import default_timer as timer
|
12 |
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from text import cleaned_text_to_sequence
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13 |
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from module.models import SynthesizerTrn
|
14 |
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from module.mel_processing import spectrogram_torch
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15 |
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from transformers.pipelines.audio_utils import ffmpeg_read
|
16 |
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import os,re,sys,LangSegment,librosa,pdb,torch,pytz,random
|
17 |
+
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
18 |
+
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
19 |
+
|
20 |
+
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21 |
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import logging
|
22 |
+
logging.getLogger("markdown_it").setLevel(logging.ERROR)
|
23 |
+
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
24 |
+
logging.getLogger("httpcore").setLevel(logging.ERROR)
|
25 |
+
logging.getLogger("httpx").setLevel(logging.ERROR)
|
26 |
+
logging.getLogger("asyncio").setLevel(logging.ERROR)
|
27 |
+
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
|
28 |
+
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
|
29 |
+
logging.getLogger("multipart").setLevel(logging.WARNING)
|
30 |
+
# from download import *
|
31 |
+
# download()
|
32 |
+
|
33 |
+
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
34 |
+
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
35 |
+
tz = pytz.timezone('Asia/Singapore')
|
36 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
37 |
+
|
38 |
+
def abs_path(dir):
|
39 |
+
global_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
|
40 |
+
return(os.path.join(global_dir, dir))
|
41 |
+
gpt_path = abs_path("MODELS/22/22.ckpt")
|
42 |
+
sovits_path=abs_path("MODELS/22/22.pth")
|
43 |
+
cnhubert_base_path = os.environ.get("cnhubert_base_path", "pretrained_models/chinese-hubert-base")
|
44 |
+
bert_path = os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large")
|
45 |
+
|
46 |
+
if not os.path.exists(cnhubert_base_path):
|
47 |
+
cnhubert_base_path = "TencentGameMate/chinese-hubert-base"
|
48 |
+
if not os.path.exists(bert_path):
|
49 |
+
bert_path = "hfl/chinese-roberta-wwm-ext-large"
|
50 |
+
cnhubert.cnhubert_base_path = cnhubert_base_path
|
51 |
+
|
52 |
+
whisper_path = os.environ.get("whisper_path", "pretrained_models/whisper-tiny")
|
53 |
+
if not os.path.exists(whisper_path):
|
54 |
+
whisper_path = "openai/whisper-tiny"
|
55 |
+
|
56 |
+
pipe = pipeline(
|
57 |
+
task="automatic-speech-recognition",
|
58 |
+
model=whisper_path,
|
59 |
+
chunk_length_s=30,
|
60 |
+
device=device,)
|
61 |
+
|
62 |
+
|
63 |
+
is_half = eval(
|
64 |
+
os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
|
65 |
+
)
|
66 |
+
|
67 |
+
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
68 |
+
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
69 |
+
if is_half == True:
|
70 |
+
bert_model = bert_model.half().to(device)
|
71 |
+
else:
|
72 |
+
bert_model = bert_model.to(device)
|
73 |
+
|
74 |
+
|
75 |
+
def get_bert_feature(text, word2ph):
|
76 |
+
with torch.no_grad():
|
77 |
+
inputs = tokenizer(text, return_tensors="pt")
|
78 |
+
for i in inputs:
|
79 |
+
inputs[i] = inputs[i].to(device)
|
80 |
+
res = bert_model(**inputs, output_hidden_states=True)
|
81 |
+
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
82 |
+
assert len(word2ph) == len(text)
|
83 |
+
phone_level_feature = []
|
84 |
+
for i in range(len(word2ph)):
|
85 |
+
repeat_feature = res[i].repeat(word2ph[i], 1)
|
86 |
+
phone_level_feature.append(repeat_feature)
|
87 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
88 |
+
return phone_level_feature.T
|
89 |
+
|
90 |
+
|
91 |
+
class DictToAttrRecursive(dict):
|
92 |
+
def __init__(self, input_dict):
|
93 |
+
super().__init__(input_dict)
|
94 |
+
for key, value in input_dict.items():
|
95 |
+
if isinstance(value, dict):
|
96 |
+
value = DictToAttrRecursive(value)
|
97 |
+
self[key] = value
|
98 |
+
setattr(self, key, value)
|
99 |
+
|
100 |
+
def __getattr__(self, item):
|
101 |
+
try:
|
102 |
+
return self[item]
|
103 |
+
except KeyError:
|
104 |
+
raise AttributeError(f"Attribute {item} not found")
|
105 |
+
|
106 |
+
def __setattr__(self, key, value):
|
107 |
+
if isinstance(value, dict):
|
108 |
+
value = DictToAttrRecursive(value)
|
109 |
+
super(DictToAttrRecursive, self).__setitem__(key, value)
|
110 |
+
super().__setattr__(key, value)
|
111 |
+
|
112 |
+
def __delattr__(self, item):
|
113 |
+
try:
|
114 |
+
del self[item]
|
115 |
+
except KeyError:
|
116 |
+
raise AttributeError(f"Attribute {item} not found")
|
117 |
+
|
118 |
+
|
119 |
+
ssl_model = cnhubert.get_model()
|
120 |
+
if is_half == True:
|
121 |
+
ssl_model = ssl_model.half().to(device)
|
122 |
+
else:
|
123 |
+
ssl_model = ssl_model.to(device)
|
124 |
+
|
125 |
+
|
126 |
+
def change_sovits_weights(sovits_path):
|
127 |
+
global vq_model, hps
|
128 |
+
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
129 |
+
hps = dict_s2["config"]
|
130 |
+
hps = DictToAttrRecursive(hps)
|
131 |
+
hps.model.semantic_frame_rate = "25hz"
|
132 |
+
vq_model = SynthesizerTrn(
|
133 |
+
hps.data.filter_length // 2 + 1,
|
134 |
+
hps.train.segment_size // hps.data.hop_length,
|
135 |
+
n_speakers=hps.data.n_speakers,
|
136 |
+
**hps.model
|
137 |
+
)
|
138 |
+
if ("pretrained" not in sovits_path):
|
139 |
+
del vq_model.enc_q
|
140 |
+
if is_half == True:
|
141 |
+
vq_model = vq_model.half().to(device)
|
142 |
+
else:
|
143 |
+
vq_model = vq_model.to(device)
|
144 |
+
vq_model.eval()
|
145 |
+
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
146 |
+
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
147 |
+
f.write(sovits_path)
|
148 |
+
|
149 |
+
|
150 |
+
change_sovits_weights(sovits_path)
|
151 |
+
|
152 |
+
|
153 |
+
def change_gpt_weights(gpt_path):
|
154 |
+
global hz, max_sec, t2s_model, config
|
155 |
+
hz = 50
|
156 |
+
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
157 |
+
config = dict_s1["config"]
|
158 |
+
max_sec = config["data"]["max_sec"]
|
159 |
+
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
160 |
+
t2s_model.load_state_dict(dict_s1["weight"])
|
161 |
+
if is_half == True:
|
162 |
+
t2s_model = t2s_model.half()
|
163 |
+
t2s_model = t2s_model.to(device)
|
164 |
+
t2s_model.eval()
|
165 |
+
total = sum([param.nelement() for param in t2s_model.parameters()])
|
166 |
+
print("Number of parameter: %.2fM" % (total / 1e6))
|
167 |
+
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
|
168 |
+
|
169 |
+
|
170 |
+
change_gpt_weights(gpt_path)
|
171 |
+
|
172 |
+
|
173 |
+
def get_spepc(hps, filename):
|
174 |
+
audio = load_audio(filename, int(hps.data.sampling_rate))
|
175 |
+
audio = torch.FloatTensor(audio)
|
176 |
+
audio_norm = audio
|
177 |
+
audio_norm = audio_norm.unsqueeze(0)
|
178 |
+
spec = spectrogram_torch(
|
179 |
+
audio_norm,
|
180 |
+
hps.data.filter_length,
|
181 |
+
hps.data.sampling_rate,
|
182 |
+
hps.data.hop_length,
|
183 |
+
hps.data.win_length,
|
184 |
+
center=False,
|
185 |
+
)
|
186 |
+
return spec
|
187 |
+
|
188 |
+
|
189 |
+
dict_language = {
|
190 |
+
("中文1"): "all_zh",#全部按中文识别
|
191 |
+
("English"): "en",#全部按英文识别#######不变
|
192 |
+
("日文1"): "all_ja",#全部按日文识别
|
193 |
+
("中文"): "zh",#按中英混合识别####不变
|
194 |
+
("日本語"): "ja",#按日英混合识别####不变
|
195 |
+
("混合"): "auto",#多语种启动切分识别语种
|
196 |
+
}
|
197 |
+
|
198 |
+
|
199 |
+
def splite_en_inf(sentence, language):
|
200 |
+
pattern = re.compile(r'[a-zA-Z ]+')
|
201 |
+
textlist = []
|
202 |
+
langlist = []
|
203 |
+
pos = 0
|
204 |
+
for match in pattern.finditer(sentence):
|
205 |
+
start, end = match.span()
|
206 |
+
if start > pos:
|
207 |
+
textlist.append(sentence[pos:start])
|
208 |
+
langlist.append(language)
|
209 |
+
textlist.append(sentence[start:end])
|
210 |
+
langlist.append("en")
|
211 |
+
pos = end
|
212 |
+
if pos < len(sentence):
|
213 |
+
textlist.append(sentence[pos:])
|
214 |
+
langlist.append(language)
|
215 |
+
# Merge punctuation into previous word
|
216 |
+
for i in range(len(textlist)-1, 0, -1):
|
217 |
+
if re.match(r'^[\W_]+$', textlist[i]):
|
218 |
+
textlist[i-1] += textlist[i]
|
219 |
+
del textlist[i]
|
220 |
+
del langlist[i]
|
221 |
+
# Merge consecutive words with the same language tag
|
222 |
+
i = 0
|
223 |
+
while i < len(langlist) - 1:
|
224 |
+
if langlist[i] == langlist[i+1]:
|
225 |
+
textlist[i] += textlist[i+1]
|
226 |
+
del textlist[i+1]
|
227 |
+
del langlist[i+1]
|
228 |
+
else:
|
229 |
+
i += 1
|
230 |
+
|
231 |
+
return textlist, langlist
|
232 |
+
|
233 |
+
|
234 |
+
def clean_text_inf(text, language):
|
235 |
+
formattext = ""
|
236 |
+
language = language.replace("all_","")
|
237 |
+
for tmp in LangSegment.getTexts(text):
|
238 |
+
if language == "ja":
|
239 |
+
if tmp["lang"] == language or tmp["lang"] == "zh":
|
240 |
+
formattext += tmp["text"] + " "
|
241 |
+
continue
|
242 |
+
if tmp["lang"] == language:
|
243 |
+
formattext += tmp["text"] + " "
|
244 |
+
while " " in formattext:
|
245 |
+
formattext = formattext.replace(" ", " ")
|
246 |
+
phones, word2ph, norm_text = clean_text(formattext, language)
|
247 |
+
phones = cleaned_text_to_sequence(phones)
|
248 |
+
return phones, word2ph, norm_text
|
249 |
+
|
250 |
+
dtype=torch.float16 if is_half == True else torch.float32
|
251 |
+
def get_bert_inf(phones, word2ph, norm_text, language):
|
252 |
+
language=language.replace("all_","")
|
253 |
+
if language == "zh":
|
254 |
+
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
255 |
+
else:
|
256 |
+
bert = torch.zeros(
|
257 |
+
(1024, len(phones)),
|
258 |
+
dtype=torch.float16 if is_half == True else torch.float32,
|
259 |
+
).to(device)
|
260 |
+
|
261 |
+
return bert
|
262 |
+
|
263 |
+
|
264 |
+
def nonen_clean_text_inf(text, language):
|
265 |
+
if(language!="auto"):
|
266 |
+
textlist, langlist = splite_en_inf(text, language)
|
267 |
+
else:
|
268 |
+
textlist=[]
|
269 |
+
langlist=[]
|
270 |
+
for tmp in LangSegment.getTexts(text):
|
271 |
+
langlist.append(tmp["lang"])
|
272 |
+
textlist.append(tmp["text"])
|
273 |
+
print(textlist)
|
274 |
+
print(langlist)
|
275 |
+
phones_list = []
|
276 |
+
word2ph_list = []
|
277 |
+
norm_text_list = []
|
278 |
+
for i in range(len(textlist)):
|
279 |
+
lang = langlist[i]
|
280 |
+
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
281 |
+
phones_list.append(phones)
|
282 |
+
if lang == "zh":
|
283 |
+
word2ph_list.append(word2ph)
|
284 |
+
norm_text_list.append(norm_text)
|
285 |
+
print(word2ph_list)
|
286 |
+
phones = sum(phones_list, [])
|
287 |
+
word2ph = sum(word2ph_list, [])
|
288 |
+
norm_text = ' '.join(norm_text_list)
|
289 |
+
|
290 |
+
return phones, word2ph, norm_text
|
291 |
+
|
292 |
+
|
293 |
+
def nonen_get_bert_inf(text, language):
|
294 |
+
if(language!="auto"):
|
295 |
+
textlist, langlist = splite_en_inf(text, language)
|
296 |
+
else:
|
297 |
+
textlist=[]
|
298 |
+
langlist=[]
|
299 |
+
for tmp in LangSegment.getTexts(text):
|
300 |
+
langlist.append(tmp["lang"])
|
301 |
+
textlist.append(tmp["text"])
|
302 |
+
print(textlist)
|
303 |
+
print(langlist)
|
304 |
+
bert_list = []
|
305 |
+
for i in range(len(textlist)):
|
306 |
+
lang = langlist[i]
|
307 |
+
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
308 |
+
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
309 |
+
bert_list.append(bert)
|
310 |
+
bert = torch.cat(bert_list, dim=1)
|
311 |
+
|
312 |
+
return bert
|
313 |
+
|
314 |
+
|
315 |
+
splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }
|
316 |
+
|
317 |
+
|
318 |
+
def get_first(text):
|
319 |
+
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
320 |
+
text = re.split(pattern, text)[0].strip()
|
321 |
+
return text
|
322 |
+
|
323 |
+
|
324 |
+
def get_cleaned_text_final(text,language):
|
325 |
+
if language in {"en","all_zh","all_ja"}:
|
326 |
+
phones, word2ph, norm_text = clean_text_inf(text, language)
|
327 |
+
elif language in {"zh", "ja","auto"}:
|
328 |
+
phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
|
329 |
+
return phones, word2ph, norm_text
|
330 |
+
|
331 |
+
def get_bert_final(phones, word2ph, text,language,device):
|
332 |
+
if language == "en":
|
333 |
+
bert = get_bert_inf(phones, word2ph, text, language)
|
334 |
+
elif language in {"zh", "ja","auto"}:
|
335 |
+
bert = nonen_get_bert_inf(text, language)
|
336 |
+
elif language == "all_zh":
|
337 |
+
bert = get_bert_feature(text, word2ph).to(device)
|
338 |
+
else:
|
339 |
+
bert = torch.zeros((1024, len(phones))).to(device)
|
340 |
+
return bert
|
341 |
+
|
342 |
+
def merge_short_text_in_array(texts, threshold):
|
343 |
+
if (len(texts)) < 2:
|
344 |
+
return texts
|
345 |
+
result = []
|
346 |
+
text = ""
|
347 |
+
for ele in texts:
|
348 |
+
text += ele
|
349 |
+
if len(text) >= threshold:
|
350 |
+
result.append(text)
|
351 |
+
text = ""
|
352 |
+
if (len(text) > 0):
|
353 |
+
if len(result) == 0:
|
354 |
+
result.append(text)
|
355 |
+
else:
|
356 |
+
result[len(result) - 1] += text
|
357 |
+
return result
|
358 |
+
|
359 |
+
|
360 |
+
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"), volume_scale=1.0):
|
361 |
+
if not duration(ref_wav_path):
|
362 |
+
return None
|
363 |
+
if text == '':
|
364 |
+
wprint("Please enter text to generate/请输入生成文字")
|
365 |
+
return None
|
366 |
+
t0 = ttime()
|
367 |
+
startTime=timer()
|
368 |
+
text=trim_text(text,text_language)
|
369 |
+
change_sovits_weights(sovits_path)
|
370 |
+
tprint(f'🏕️LOADED SoVITS Model: {sovits_path}')
|
371 |
+
change_gpt_weights(gpt_path)
|
372 |
+
tprint(f'🏕️LOADED GPT Model: {gpt_path}')
|
373 |
+
|
374 |
+
prompt_language = dict_language[prompt_language]
|
375 |
+
try:
|
376 |
+
text_language = dict_language[text_language]
|
377 |
+
except KeyError as e:
|
378 |
+
wprint(f"Unsupported language type: {e}")
|
379 |
+
return None
|
380 |
+
|
381 |
+
prompt_text = prompt_text.strip("\n")
|
382 |
+
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
|
383 |
+
text = text.strip("\n")
|
384 |
+
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
385 |
+
#print(("实际输入的参考文本:"), prompt_text)
|
386 |
+
#print(("📝实际输入的目标文本:"), text)
|
387 |
+
zero_wav = np.zeros(
|
388 |
+
int(hps.data.sampling_rate * 0.3),
|
389 |
+
dtype=np.float16 if is_half == True else np.float32,
|
390 |
+
)
|
391 |
+
with torch.no_grad():
|
392 |
+
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
393 |
+
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
394 |
+
errinfo='参考音频在3~10秒范围外,请更换!'
|
395 |
+
raise OSError((errinfo))
|
396 |
+
wav16k = torch.from_numpy(wav16k)
|
397 |
+
zero_wav_torch = torch.from_numpy(zero_wav)
|
398 |
+
if is_half == True:
|
399 |
+
wav16k = wav16k.half().to(device)
|
400 |
+
zero_wav_torch = zero_wav_torch.half().to(device)
|
401 |
+
else:
|
402 |
+
wav16k = wav16k.to(device)
|
403 |
+
zero_wav_torch = zero_wav_torch.to(device)
|
404 |
+
wav16k = torch.cat([wav16k, zero_wav_torch])
|
405 |
+
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
|
406 |
+
"last_hidden_state"
|
407 |
+
].transpose(
|
408 |
+
1, 2
|
409 |
+
) # .float()
|
410 |
+
codes = vq_model.extract_latent(ssl_content)
|
411 |
+
prompt_semantic = codes[0, 0]
|
412 |
+
t1 = ttime()
|
413 |
+
|
414 |
+
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
|
415 |
+
|
416 |
+
if (how_to_cut == ("Split into groups of 4 sentences")):
|
417 |
+
text = cut1(text)
|
418 |
+
elif (how_to_cut == ("Split every 50 characters")):
|
419 |
+
text = cut2(text)
|
420 |
+
elif (how_to_cut == ("Split at CN/JP periods (。)")):
|
421 |
+
text = cut3(text)
|
422 |
+
elif (how_to_cut == ("Split at English periods (.)")):
|
423 |
+
text = cut4(text)
|
424 |
+
elif (how_to_cut == ("Split at punctuation marks")):
|
425 |
+
text = cut5(text)
|
426 |
+
while "\n\n" in text:
|
427 |
+
text = text.replace("\n\n", "\n")
|
428 |
+
print(f"🧨实际输入的目标文本(切句后):{text}\n")
|
429 |
+
texts = text.split("\n")
|
430 |
+
texts = merge_short_text_in_array(texts, 5)
|
431 |
+
audio_opt = []
|
432 |
+
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
|
433 |
+
|
434 |
+
for text in texts:
|
435 |
+
if (len(text.strip()) == 0):
|
436 |
+
continue
|
437 |
+
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
|
438 |
+
print(("\n🎈实际输入的目标文本(每句):"), text)
|
439 |
+
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
|
440 |
+
try:
|
441 |
+
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
|
442 |
+
except RuntimeError as e:
|
443 |
+
wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
|
444 |
+
return None
|
445 |
+
bert = torch.cat([bert1, bert2], 1)
|
446 |
+
|
447 |
+
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
448 |
+
bert = bert.to(device).unsqueeze(0)
|
449 |
+
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
450 |
+
prompt = prompt_semantic.unsqueeze(0).to(device)
|
451 |
+
t2 = ttime()
|
452 |
+
with torch.no_grad():
|
453 |
+
# pred_semantic = t2s_model.model.infer(
|
454 |
+
pred_semantic, idx = t2s_model.model.infer_panel(
|
455 |
+
all_phoneme_ids,
|
456 |
+
all_phoneme_len,
|
457 |
+
prompt,
|
458 |
+
bert,
|
459 |
+
# prompt_phone_len=ph_offset,
|
460 |
+
top_k=config["inference"]["top_k"],
|
461 |
+
early_stop_num=hz * max_sec,
|
462 |
+
)
|
463 |
+
t3 = ttime()
|
464 |
+
# print(pred_semantic.shape,idx)
|
465 |
+
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
466 |
+
0
|
467 |
+
) # .unsqueeze(0)#mq要多unsqueeze一次
|
468 |
+
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
469 |
+
if is_half == True:
|
470 |
+
refer = refer.half().to(device)
|
471 |
+
else:
|
472 |
+
refer = refer.to(device)
|
473 |
+
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
474 |
+
try:
|
475 |
+
audio = (
|
476 |
+
vq_model.decode(
|
477 |
+
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
478 |
+
)
|
479 |
+
.detach()
|
480 |
+
.cpu()
|
481 |
+
.numpy()[0, 0]
|
482 |
+
)
|
483 |
+
except RuntimeError as e:
|
484 |
+
wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
|
485 |
+
return None
|
486 |
+
|
487 |
+
max_audio=np.abs(audio).max()
|
488 |
+
if max_audio>1:audio/=max_audio
|
489 |
+
audio_opt.append(audio)
|
490 |
+
audio_opt.append(zero_wav)
|
491 |
+
t4 = ttime()
|
492 |
+
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
493 |
+
#yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
494 |
+
audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
495 |
+
|
496 |
+
audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16)
|
497 |
+
output_wav = "output_audio.wav"
|
498 |
+
sf.write(output_wav, audio_data, hps.data.sampling_rate)
|
499 |
+
endTime=timer()
|
500 |
+
tprint(f'🆗TTS COMPLETE,{round(endTime-startTime,4)}s')
|
501 |
+
return output_wav
|
502 |
+
|
503 |
+
def split(todo_text):
|
504 |
+
todo_text = todo_text.replace("……", "。").replace("——", ",")
|
505 |
+
if todo_text[-1] not in splits:
|
506 |
+
todo_text += "。"
|
507 |
+
i_split_head = i_split_tail = 0
|
508 |
+
len_text = len(todo_text)
|
509 |
+
todo_texts = []
|
510 |
+
while 1:
|
511 |
+
if i_split_head >= len_text:
|
512 |
+
break
|
513 |
+
if todo_text[i_split_head] in splits:
|
514 |
+
i_split_head += 1
|
515 |
+
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
516 |
+
i_split_tail = i_split_head
|
517 |
+
else:
|
518 |
+
i_split_head += 1
|
519 |
+
return todo_texts
|
520 |
+
|
521 |
+
|
522 |
+
def cut1(inp):
|
523 |
+
inp = inp.strip("\n")
|
524 |
+
inps = split(inp)
|
525 |
+
split_idx = list(range(0, len(inps), 4))
|
526 |
+
split_idx[-1] = None
|
527 |
+
if len(split_idx) > 1:
|
528 |
+
opts = []
|
529 |
+
for idx in range(len(split_idx) - 1):
|
530 |
+
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
531 |
+
else:
|
532 |
+
opts = [inp]
|
533 |
+
return "\n".join(opts)
|
534 |
+
|
535 |
+
|
536 |
+
def cut2(inp):
|
537 |
+
inp = inp.strip("\n")
|
538 |
+
inps = split(inp)
|
539 |
+
if len(inps) < 2:
|
540 |
+
return inp
|
541 |
+
opts = []
|
542 |
+
summ = 0
|
543 |
+
tmp_str = ""
|
544 |
+
for i in range(len(inps)):
|
545 |
+
summ += len(inps[i])
|
546 |
+
tmp_str += inps[i]
|
547 |
+
if summ > 50:
|
548 |
+
summ = 0
|
549 |
+
opts.append(tmp_str)
|
550 |
+
tmp_str = ""
|
551 |
+
if tmp_str != "":
|
552 |
+
opts.append(tmp_str)
|
553 |
+
# print(opts)
|
554 |
+
if len(opts) > 1 and len(opts[-1]) < 50:
|
555 |
+
opts[-2] = opts[-2] + opts[-1]
|
556 |
+
opts = opts[:-1]
|
557 |
+
return "\n".join(opts)
|
558 |
+
|
559 |
+
|
560 |
+
def cut3(inp):
|
561 |
+
inp = inp.strip("\n")
|
562 |
+
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
563 |
+
|
564 |
+
|
565 |
+
def cut4(inp):
|
566 |
+
inp = inp.strip("\n")
|
567 |
+
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
568 |
+
|
569 |
+
|
570 |
+
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
571 |
+
def cut5(inp):
|
572 |
+
# if not re.search(r'[^\w\s]', inp[-1]):
|
573 |
+
# inp += '。'
|
574 |
+
inp = inp.strip("\n")
|
575 |
+
punds = r'[,.;?!、,。?!;:…]'
|
576 |
+
items = re.split(f'({punds})', inp)
|
577 |
+
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
578 |
+
if len(items)%2 == 1:
|
579 |
+
mergeitems.append(items[-1])
|
580 |
+
opt = "\n".join(mergeitems)
|
581 |
+
return opt
|
582 |
+
|
583 |
+
|
584 |
+
|
585 |
+
def custom_sort_key(s):
|
586 |
+
# 使用正则表达式提取字符串中的数字部分和非��字部分
|
587 |
+
parts = re.split('(\d+)', s)
|
588 |
+
# 将数字部分转换为整数,非数字部分保持不变
|
589 |
+
parts = [int(part) if part.isdigit() else part for part in parts]
|
590 |
+
return parts
|
591 |
+
|
592 |
+
#==========custom functions============
|
593 |
+
|
594 |
+
def tprint(text):
|
595 |
+
now=datetime.now(tz).strftime('%H:%M:%S')
|
596 |
+
print(f'UTC+8 - {now} - {text}')
|
597 |
+
|
598 |
+
def wprint(text):
|
599 |
+
tprint(text)
|
600 |
+
gr.Warning(text)
|
601 |
+
|
602 |
+
def lang_detector(text):
|
603 |
+
min_chars = 5
|
604 |
+
if len(text) < min_chars:
|
605 |
+
return "Input text too short/输入文本太短"
|
606 |
+
try:
|
607 |
+
detector = Detector(text).language
|
608 |
+
lang_info = str(detector)
|
609 |
+
code = re.search(r"name: (\w+)", lang_info).group(1)
|
610 |
+
if code == 'Japanese':
|
611 |
+
return "日本語"
|
612 |
+
elif code == 'Chinese':
|
613 |
+
return "中文"
|
614 |
+
elif code == 'English':
|
615 |
+
return 'English'
|
616 |
+
else:
|
617 |
+
return code
|
618 |
+
except Exception as e:
|
619 |
+
return f"ERROR:{str(e)}"
|
620 |
+
|
621 |
+
def trim_text(text,language):
|
622 |
+
limit_cj = 120 #character
|
623 |
+
limit_en = 60 #words
|
624 |
+
search_limit_cj = limit_cj+30
|
625 |
+
search_limit_en = limit_en +30
|
626 |
+
text = text.replace('\n', '').strip()
|
627 |
+
|
628 |
+
if language =='English':
|
629 |
+
words = text.split()
|
630 |
+
if len(words) <= limit_en:
|
631 |
+
return text
|
632 |
+
# English
|
633 |
+
for i in range(limit_en, -1, -1):
|
634 |
+
if any(punct in words[i] for punct in splits):
|
635 |
+
return ' '.join(words[:i+1])
|
636 |
+
for i in range(limit_en, min(len(words), search_limit_en)):
|
637 |
+
if any(punct in words[i] for punct in splits):
|
638 |
+
return ' '.join(words[:i+1])
|
639 |
+
return ' '.join(words[:limit_en])
|
640 |
+
|
641 |
+
else:#中文日文
|
642 |
+
if len(text) <= limit_cj:
|
643 |
+
return text
|
644 |
+
for i in range(limit_cj, -1, -1):
|
645 |
+
if text[i] in splits:
|
646 |
+
return text[:i+1]
|
647 |
+
for i in range(limit_cj, min(len(text), search_limit_cj)):
|
648 |
+
if text[i] in splits:
|
649 |
+
return text[:i+1]
|
650 |
+
return text[:limit_cj]
|
651 |
+
|
652 |
+
def duration(audio_file_path):
|
653 |
+
if not audio_file_path:
|
654 |
+
wprint("Failed to obtain uploaded audio/未找到音频文件")
|
655 |
+
return False
|
656 |
+
try:
|
657 |
+
audio_duration = librosa.get_duration(filename=audio_file_path)
|
658 |
+
if not 3 < audio_duration < 10:
|
659 |
+
wprint("The audio length must be between 3~10 seconds/音频时长须在3~10秒之间")
|
660 |
+
return False
|
661 |
+
return True
|
662 |
+
except FileNotFoundError:
|
663 |
+
return False
|
664 |
+
|
665 |
+
def update_model(choice):
|
666 |
+
global gpt_path, sovits_path
|
667 |
+
model_info = models[choice]
|
668 |
+
gpt_path = abs_path(model_info["gpt_weight"])
|
669 |
+
sovits_path = abs_path(model_info["sovits_weight"])
|
670 |
+
model_name = choice
|
671 |
+
tone_info = model_info["tones"]["tone1"]
|
672 |
+
tone_sample_path = abs_path(tone_info["sample"])
|
673 |
+
tprint(f'✅SELECT MODEL:{choice}')
|
674 |
+
# 返回默认tone“tone1”
|
675 |
+
return (
|
676 |
+
tone_info["example_voice_wav"],
|
677 |
+
tone_info["example_voice_wav_words"],
|
678 |
+
model_info["default_language"],
|
679 |
+
model_info["default_language"],
|
680 |
+
model_name,
|
681 |
+
"tone1" ,
|
682 |
+
tone_sample_path
|
683 |
+
)
|
684 |
+
|
685 |
+
def update_tone(model_choice, tone_choice):
|
686 |
+
model_info = models[model_choice]
|
687 |
+
tone_info = model_info["tones"][tone_choice]
|
688 |
+
example_voice_wav = abs_path(tone_info["example_voice_wav"])
|
689 |
+
example_voice_wav_words = tone_info["example_voice_wav_words"]
|
690 |
+
tone_sample_path = abs_path(tone_info["sample"])
|
691 |
+
return example_voice_wav, example_voice_wav_words,tone_sample_path
|
692 |
+
|
693 |
+
def transcribe(voice):
|
694 |
+
time1=timer()
|
695 |
+
tprint('⚡Start Clone - transcribe')
|
696 |
+
task="transcribe"
|
697 |
+
if voice is None:
|
698 |
+
wprint("No audio file submitted! Please upload or record an audio file before submitting your request.")
|
699 |
+
R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True)
|
700 |
+
text=R['text']
|
701 |
+
lang=R['chunks'][0]['language']
|
702 |
+
if lang=='english':
|
703 |
+
language='English'
|
704 |
+
elif lang =='chinese':
|
705 |
+
language='中文'
|
706 |
+
elif lang=='japanese':
|
707 |
+
language = '日本語'
|
708 |
+
|
709 |
+
time2=timer()
|
710 |
+
tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s')
|
711 |
+
tprint(f'\nTRANSCRIBE RESULT:\n 🔣Language:{language} \n 🔣Text:{text}' )
|
712 |
+
return text,language
|
713 |
+
|
714 |
+
def clone_voice(user_voice,user_text,user_lang):
|
715 |
+
if not duration(user_voice):
|
716 |
+
return None
|
717 |
+
if user_text == '':
|
718 |
+
wprint("Please enter text to generate/请输入生成文字")
|
719 |
+
return None
|
720 |
+
user_text=trim_text(user_text,user_lang)
|
721 |
+
time1=timer()
|
722 |
+
global gpt_path, sovits_path
|
723 |
+
gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
|
724 |
+
#tprint(f'Model loaded:{gpt_path}')
|
725 |
+
sovits_path = abs_path("pretrained_models/s2G488k.pth")
|
726 |
+
#tprint(f'Model loaded:{sovits_path}')
|
727 |
+
try:
|
728 |
+
prompt_text, prompt_language = transcribe(user_voice)
|
729 |
+
except UnboundLocalError as e:
|
730 |
+
wprint(f"The language in the audio cannot be recognized :{str(e)}")
|
731 |
+
return None
|
732 |
+
|
733 |
+
output_wav = get_tts_wav(
|
734 |
+
user_voice,
|
735 |
+
prompt_text,
|
736 |
+
prompt_language,
|
737 |
+
user_text,
|
738 |
+
user_lang,
|
739 |
+
how_to_cut="Do not split",
|
740 |
+
volume_scale=1.0)
|
741 |
+
time2=timer()
|
742 |
+
tprint(f'🆗CLONE COMPLETE,{round(time2-time1,4)}s')
|
743 |
+
return output_wav
|
744 |
+
|
745 |
+
with open('dummy') as f:
|
746 |
+
dummy_txt = f.read().strip().splitlines()
|
747 |
+
|
748 |
+
def dice():
|
749 |
+
return random.choice(dummy_txt), '🎲'
|
750 |
+
|
751 |
+
from info import models
|
752 |
+
models_by_language = {
|
753 |
+
"English": [],
|
754 |
+
"中文": [],
|
755 |
+
"日本語": []
|
756 |
+
}
|
757 |
+
for model_name, model_info in models.items():
|
758 |
+
language = model_info["default_language"]
|
759 |
+
models_by_language[language].append((model_name, model_info))
|
760 |
+
|
requirements.txt
CHANGED
@@ -28,4 +28,6 @@ pyicu
|
|
28 |
morfessor
|
29 |
pycld2
|
30 |
polyglot
|
31 |
-
wordsegment
|
|
|
|
|
|
28 |
morfessor
|
29 |
pycld2
|
30 |
polyglot
|
31 |
+
wordsegment
|
32 |
+
fastapi
|
33 |
+
uvicorn
|
static/en/s1.mp3
ADDED
Binary file (44.1 kB). View file
|
|
static/en/s2.mp3
ADDED
Binary file (53.8 kB). View file
|
|
static/zh/s1.mp3
ADDED
Binary file (32.4 kB). View file
|
|
static/zh/s2.mp3
ADDED
Binary file (32.4 kB). View file
|
|
text/__pycache__/__init__.cpython-310.pyc
CHANGED
Binary files a/text/__pycache__/__init__.cpython-310.pyc and b/text/__pycache__/__init__.cpython-310.pyc differ
|
|
text/__pycache__/chinese.cpython-310.pyc
CHANGED
Binary files a/text/__pycache__/chinese.cpython-310.pyc and b/text/__pycache__/chinese.cpython-310.pyc differ
|
|
text/__pycache__/cleaner.cpython-310.pyc
CHANGED
Binary files a/text/__pycache__/cleaner.cpython-310.pyc and b/text/__pycache__/cleaner.cpython-310.pyc differ
|
|
text/__pycache__/symbols.cpython-310.pyc
CHANGED
Binary files a/text/__pycache__/symbols.cpython-310.pyc and b/text/__pycache__/symbols.cpython-310.pyc differ
|
|