forked from RejektsAI/EasyTools
-
Notifications
You must be signed in to change notification settings - Fork 0
/
zero.py
323 lines (286 loc) · 11.9 KB
/
zero.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
# Modified from https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
import os
from faster_whisper import WhisperModel
from scipy.io.wavfile import write
from datetime import datetime
import argparse, torch
parser = argparse.ArgumentParser(description='Generate speech from text')
parser.add_argument('--input_file', type=str, help='Path to the input audio file')
parser.add_argument('--audio_lang', type=str, help='Language of the input audio')
parser.add_argument('--text', type=str, help='Text to be processed')
parser.add_argument('--text_lang', type=str, help='Language to translate the text to')
parser.add_argument('--whisper_model', type=str, help='Whisper Model to use', default="medium.en")
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
whisper_model = WhisperModel(args.whisper_model, device=device, compute_type="auto")
gpt_path = os.environ.get(
"gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
)
sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
cnhubert_base_path = os.environ.get(
"cnhubert_base_path", "pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
"bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"
)
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
import gradio as gr
import librosa
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
from feature_extractor import cnhubert
cnhubert.cnhubert_base_path = cnhubert_base_path
from time import time as ttime
import datetime
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from module.mel_processing import spectrogram_torch
from module.models import SynthesizerTrn
from my_utils import load_audio
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
is_half = eval(
os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
)
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half == True:
bert_model = bert_model.half().to(device)
else:
bert_model = bert_model.to(device)
# bert_model=bert_model.to(device)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题,精度随bert_model
res = bert_model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
assert len(word2ph) == len(text)
phone_level_feature = []
for i in range(len(word2ph)):
repeat_feature = res[i].repeat(word2ph[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
# if(is_half==True):phone_level_feature=phone_level_feature.half()
return phone_level_feature.T
n_semantic = 1024
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]
class DictToAttrRecursive:
def __init__(self, input_dict):
for key, value in input_dict.items():
if isinstance(value, dict):
# 如果值是字典,递归调用构造函数
setattr(self, key, DictToAttrRecursive(value))
else:
setattr(self, key, value)
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
dict_s1 = torch.load(gpt_path, map_location="cpu")
sovits_config = dict_s1["config"]
ssl_model = cnhubert.get_model()
if is_half == True:
ssl_model = ssl_model.half().to(device)
else:
ssl_model = ssl_model.to(device)
vq_model = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model,
)
if is_half == True:
vq_model = vq_model.half().to(device)
else:
vq_model = vq_model.to(device)
vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
hz = 50
max_sec = sovits_config["data"]["max_sec"]
# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
t2s_model = Text2SemanticLightningModule(sovits_config, "ojbk", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True:
t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
def get_spepc(hps, filename):
audio = load_audio(filename, int(hps.data.sampling_rate))
audio = torch.FloatTensor(audio)
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(
audio_norm,
hps.data.filter_length,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
center=False,
)
return spec
dict_language = {"Chinese": "zh", "English": "en", "Japanese": "ja"}
splits = {
",",
"。",
"?",
"!",
",",
".",
"?",
"!",
"~",
":",
":",
"—",
"…",
}
def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", ",")
if todo_text[-1] not in splits:
todo_text += "。"
i_split_head = i_split_tail = 0
len_text = len(todo_text)
todo_texts = []
while 1:
if i_split_head >= len_text:
break # There must be punctuation at the end, so you can jump out directly. The last paragraph has been added last time
if todo_text[i_split_head] in splits:
i_split_head += 1
todo_texts.append(todo_text[i_split_tail:i_split_head])
i_split_tail = i_split_head
else:
i_split_head += 1
return todo_texts
def cut1(inp):
inp = inp.strip("\n")
inps = split(inp)
split_idx = list(range(0, len(inps), 3))
split_idx[-1] = None
if len(split_idx) > 1:
opts = []
for idx in range(len(split_idx) - 1):
opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
else:
opts = [inp]
return "\n".join(opts)
def transcribe(inf_ref):
file = inf_ref
segments, _ = whisper_model.transcribe(audio=file,beam_size=1,vad_filter=True,best_of=1)
sentences = [sentence.text for sentence in segments]
transcription = " ".join(sentences)
return transcription
def show(path,ext='',on_error=None):
try:
audios = list(filter(lambda x: x.endswith(ext), os.listdir(path)))
audio_paths = []
for audio in audios:
audio_paths.append(os.path.join(path,audio))
return audio_paths
except:
return on_error
def return_to(element):
return element
def upload_audio(numpy):
name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
sample_rate, numpy_array = numpy
try:
write(filename=f"audios/{name}",data=numpy_array,rate=sample_rate)
except Exception as e:
print(f"Could not write audio because of error: {e}")
return os.path.join("audios",name),{"choices":show("audios"),"__type__":"update","value":os.path.join("audios",name)}
# Load audio file and get transcription
def get_tts_wav(ref_wav_path, prompt_language, text, text_language):
prompt_text = transcribe(ref_wav_path)
if prompt_text != "":
prompt_text = cut1(prompt_text)
t0 = ttime()
prompt_text = prompt_text.strip("\n")
prompt_language, text = prompt_language, text.strip("\n")
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000) # Load 16kHz audio
wav16k = torch.from_numpy(wav16k)
if is_half == True:
wav16k = wav16k.half().to(device)
else:
wav16k = wav16k.to(device)
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
"last_hidden_state"
].transpose(
1, 2
) # Get SSL model output
codes = vq_model.extract_latent(ssl_content) # Vector quantize SSL output
prompt_semantic = codes[0, 0] # Get prompt semantic code
t1 = ttime()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language) # Clean prompt text
phones1 = cleaned_text_to_sequence(phones1)
texts = text.split("\n")
audio_opt = []
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
for text in texts:
phones2, word2ph2, norm_text2 = clean_text(text, text_language) # Clean target text
phones2 = cleaned_text_to_sequence(phones2)
if prompt_language == "zh":
bert1 = get_bert_feature(norm_text1, word2ph1).to(device) # Get BERT embedding for prompt
else:
bert1 = torch.zeros(
(1024, len(phones1)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
if text_language == "zh":
bert2 = get_bert_feature(norm_text2, word2ph2).to(device) # Get BERT embedding for target
else:
bert2 = torch.zeros((1024, len(phones2))).to(bert1)
bert = torch.cat([bert1, bert2], 1) # Concatenate BERT embeddings
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0) # Phoneme IDs
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) # Length of phoneme sequence
prompt = prompt_semantic.unsqueeze(0).to(device) # Prompt semantic code
t2 = ttime()
with torch.no_grad():
# Generate semantic code
pred_semantic, idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
prompt,
bert,
top_k=sovits_config["inference"]["top_k"],
early_stop_num=hz * max_sec,
)
t3 = ttime()
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # Get relevant part of semantic code
refer = get_spepc(hps, ref_wav_path) # Get spectrogram
if is_half == True:
refer = refer.half().to(device)
else:
refer = refer.to(device)
audio = (
vq_model.decode( # Decode audio
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
)
.detach()
.cpu()
.numpy()[0, 0]
)
audio_opt.append(audio)
audio_opt.append(zero_wav)
t4 = ttime()
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
current_datetime = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
audio_path = f"../audios/spoken_{current_datetime}.wav"
write(audio_path, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16), hps.data.sampling_rate)
return audio_path, True
#yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
else:
print("Prompt text empty.")
return None, False
if "__name__" == "__main__":
get_tts_wav(args.input_file, args.audio_lang, args.text, args.text_lang)