Upload 12 files
Browse files- README.md +51 -6
- app.py +2086 -0
- config.py +204 -0
- gitattributes.txt +35 -0
- gitignore.txt +12 -0
- i18n.py +28 -0
- packages.txt +3 -0
- requirements.txt +22 -0
- rmvpe.py +432 -0
- run.sh +16 -0
- utils.py +151 -0
- vc_infer_pipeline.py +646 -0
README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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---
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title: Magic Vocals
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emoji: 🦀
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colorFrom: red
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colorTo: pink
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sdk: gradio
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sdk_version: 3.42.0
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app_file: app.py
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pinned: false
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license: lgpl-3.0
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---
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## 🔧 Pre-requisites
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Before running the project, you must have the following tool installed on your machine:
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* [Python v3.8.0](https://www.python.org/downloads/release/python-380/)
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Also, you will need to clone the repository:
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```bash
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# Clone the repository
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git clone https://huggingface.co/spaces/mateuseap/magic-vocals/
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# Enter in the root directory
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cd magic-vocals
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```
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## 🚀 How to run
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After you've cloned the repository and entered in the root directory, run the following commands:
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```bash
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# Create and activate a Virtual Environment (make sure you're using Python v3.8.0 to do it)
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python -m venv venv
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. venv/bin/activate
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# Change mode and execute a shell script to configure and run the application
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chmod +x run.sh
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./run.sh
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```
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After the shell script executes everything, the application will be running at http://127.0.0.1:7860! Open up the link in a browser to use the app:
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![Magic Vocals](https://i.imgur.com/V55oKv8.png)
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**You only need to execute the `run.sh` one time**, once you've executed it one time, you just need to activate the virtual environment and run the command below to start the app again:
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```bash
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python app.py
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```
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**THE `run.sh` IS SUPPORTED BY THE FOLLOWING OPERATING SYSTEMS:**
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| OS | Supported |
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|-----------|:---------:|
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| `Windows` | ❌ |
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| `Ubuntu` | ✅ |
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app.py
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|
1 |
+
import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
|
2 |
+
from mega import Mega
|
3 |
+
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
|
4 |
+
import threading
|
5 |
+
from time import sleep
|
6 |
+
from subprocess import Popen
|
7 |
+
import faiss
|
8 |
+
from random import shuffle
|
9 |
+
import json, datetime, requests
|
10 |
+
from gtts import gTTS
|
11 |
+
now_dir = os.getcwd()
|
12 |
+
sys.path.append(now_dir)
|
13 |
+
tmp = os.path.join(now_dir, "TEMP")
|
14 |
+
shutil.rmtree(tmp, ignore_errors=True)
|
15 |
+
shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
|
16 |
+
os.makedirs(tmp, exist_ok=True)
|
17 |
+
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
|
18 |
+
os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
|
19 |
+
os.environ["TEMP"] = tmp
|
20 |
+
warnings.filterwarnings("ignore")
|
21 |
+
torch.manual_seed(114514)
|
22 |
+
from i18n import I18nAuto
|
23 |
+
|
24 |
+
import signal
|
25 |
+
|
26 |
+
import math
|
27 |
+
|
28 |
+
from utils import load_audio, CSVutil
|
29 |
+
|
30 |
+
global DoFormant, Quefrency, Timbre
|
31 |
+
|
32 |
+
if not os.path.isdir('csvdb/'):
|
33 |
+
os.makedirs('csvdb')
|
34 |
+
frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
|
35 |
+
frmnt.close()
|
36 |
+
stp.close()
|
37 |
+
|
38 |
+
try:
|
39 |
+
DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
|
40 |
+
DoFormant = (
|
41 |
+
lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
|
42 |
+
)(DoFormant)
|
43 |
+
except (ValueError, TypeError, IndexError):
|
44 |
+
DoFormant, Quefrency, Timbre = False, 1.0, 1.0
|
45 |
+
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)
|
46 |
+
|
47 |
+
def download_models():
|
48 |
+
# Download hubert base model if not present
|
49 |
+
if not os.path.isfile('./hubert_base.pt'):
|
50 |
+
response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
|
51 |
+
|
52 |
+
if response.status_code == 200:
|
53 |
+
with open('./hubert_base.pt', 'wb') as f:
|
54 |
+
f.write(response.content)
|
55 |
+
print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.")
|
56 |
+
else:
|
57 |
+
raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".")
|
58 |
+
|
59 |
+
# Download rmvpe model if not present
|
60 |
+
if not os.path.isfile('./rmvpe.pt'):
|
61 |
+
response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058')
|
62 |
+
|
63 |
+
if response.status_code == 200:
|
64 |
+
with open('./rmvpe.pt', 'wb') as f:
|
65 |
+
f.write(response.content)
|
66 |
+
print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.")
|
67 |
+
else:
|
68 |
+
raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".")
|
69 |
+
|
70 |
+
download_models()
|
71 |
+
|
72 |
+
print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")
|
73 |
+
|
74 |
+
def formant_apply(qfrency, tmbre):
|
75 |
+
Quefrency = qfrency
|
76 |
+
Timbre = tmbre
|
77 |
+
DoFormant = True
|
78 |
+
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
|
79 |
+
|
80 |
+
return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"})
|
81 |
+
|
82 |
+
def get_fshift_presets():
|
83 |
+
fshift_presets_list = []
|
84 |
+
for dirpath, _, filenames in os.walk("./formantshiftcfg/"):
|
85 |
+
for filename in filenames:
|
86 |
+
if filename.endswith(".txt"):
|
87 |
+
fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/'))
|
88 |
+
|
89 |
+
if len(fshift_presets_list) > 0:
|
90 |
+
return fshift_presets_list
|
91 |
+
else:
|
92 |
+
return ''
|
93 |
+
|
94 |
+
|
95 |
+
|
96 |
+
def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button):
|
97 |
+
|
98 |
+
if (cbox):
|
99 |
+
|
100 |
+
DoFormant = True
|
101 |
+
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
|
102 |
+
#print(f"is checked? - {cbox}\ngot {DoFormant}")
|
103 |
+
|
104 |
+
return (
|
105 |
+
{"value": True, "__type__": "update"},
|
106 |
+
{"visible": True, "__type__": "update"},
|
107 |
+
{"visible": True, "__type__": "update"},
|
108 |
+
{"visible": True, "__type__": "update"},
|
109 |
+
{"visible": True, "__type__": "update"},
|
110 |
+
{"visible": True, "__type__": "update"},
|
111 |
+
)
|
112 |
+
|
113 |
+
|
114 |
+
else:
|
115 |
+
|
116 |
+
DoFormant = False
|
117 |
+
CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
|
118 |
+
|
119 |
+
#print(f"is checked? - {cbox}\ngot {DoFormant}")
|
120 |
+
return (
|
121 |
+
{"value": False, "__type__": "update"},
|
122 |
+
{"visible": False, "__type__": "update"},
|
123 |
+
{"visible": False, "__type__": "update"},
|
124 |
+
{"visible": False, "__type__": "update"},
|
125 |
+
{"visible": False, "__type__": "update"},
|
126 |
+
{"visible": False, "__type__": "update"},
|
127 |
+
{"visible": False, "__type__": "update"},
|
128 |
+
)
|
129 |
+
|
130 |
+
|
131 |
+
|
132 |
+
def preset_apply(preset, qfer, tmbr):
|
133 |
+
if str(preset) != '':
|
134 |
+
with open(str(preset), 'r') as p:
|
135 |
+
content = p.readlines()
|
136 |
+
qfer, tmbr = content[0].split('\n')[0], content[1]
|
137 |
+
|
138 |
+
formant_apply(qfer, tmbr)
|
139 |
+
else:
|
140 |
+
pass
|
141 |
+
return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"})
|
142 |
+
|
143 |
+
def update_fshift_presets(preset, qfrency, tmbre):
|
144 |
+
|
145 |
+
qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
|
146 |
+
|
147 |
+
if (str(preset) != ''):
|
148 |
+
with open(str(preset), 'r') as p:
|
149 |
+
content = p.readlines()
|
150 |
+
qfrency, tmbre = content[0].split('\n')[0], content[1]
|
151 |
+
|
152 |
+
formant_apply(qfrency, tmbre)
|
153 |
+
else:
|
154 |
+
pass
|
155 |
+
return (
|
156 |
+
{"choices": get_fshift_presets(), "__type__": "update"},
|
157 |
+
{"value": qfrency, "__type__": "update"},
|
158 |
+
{"value": tmbre, "__type__": "update"},
|
159 |
+
)
|
160 |
+
|
161 |
+
i18n = I18nAuto()
|
162 |
+
#i18n.print()
|
163 |
+
# 判断是否有能用来训练和加速推理的N卡
|
164 |
+
ngpu = torch.cuda.device_count()
|
165 |
+
gpu_infos = []
|
166 |
+
mem = []
|
167 |
+
if (not torch.cuda.is_available()) or ngpu == 0:
|
168 |
+
if_gpu_ok = False
|
169 |
+
else:
|
170 |
+
if_gpu_ok = False
|
171 |
+
for i in range(ngpu):
|
172 |
+
gpu_name = torch.cuda.get_device_name(i)
|
173 |
+
if (
|
174 |
+
"10" in gpu_name
|
175 |
+
or "16" in gpu_name
|
176 |
+
or "20" in gpu_name
|
177 |
+
or "30" in gpu_name
|
178 |
+
or "40" in gpu_name
|
179 |
+
or "A2" in gpu_name.upper()
|
180 |
+
or "A3" in gpu_name.upper()
|
181 |
+
or "A4" in gpu_name.upper()
|
182 |
+
or "P4" in gpu_name.upper()
|
183 |
+
or "A50" in gpu_name.upper()
|
184 |
+
or "A60" in gpu_name.upper()
|
185 |
+
or "70" in gpu_name
|
186 |
+
or "80" in gpu_name
|
187 |
+
or "90" in gpu_name
|
188 |
+
or "M4" in gpu_name.upper()
|
189 |
+
or "T4" in gpu_name.upper()
|
190 |
+
or "TITAN" in gpu_name.upper()
|
191 |
+
): # A10#A100#V100#A40#P40#M40#K80#A4500
|
192 |
+
if_gpu_ok = True # 至少有一张能用的N卡
|
193 |
+
gpu_infos.append("%s\t%s" % (i, gpu_name))
|
194 |
+
mem.append(
|
195 |
+
int(
|
196 |
+
torch.cuda.get_device_properties(i).total_memory
|
197 |
+
/ 1024
|
198 |
+
/ 1024
|
199 |
+
/ 1024
|
200 |
+
+ 0.4
|
201 |
+
)
|
202 |
+
)
|
203 |
+
if if_gpu_ok == True and len(gpu_infos) > 0:
|
204 |
+
gpu_info = "\n".join(gpu_infos)
|
205 |
+
default_batch_size = min(mem) // 2
|
206 |
+
else:
|
207 |
+
gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
|
208 |
+
default_batch_size = 1
|
209 |
+
gpus = "-".join([i[0] for i in gpu_infos])
|
210 |
+
from lib.infer_pack.models import (
|
211 |
+
SynthesizerTrnMs256NSFsid,
|
212 |
+
SynthesizerTrnMs256NSFsid_nono,
|
213 |
+
SynthesizerTrnMs768NSFsid,
|
214 |
+
SynthesizerTrnMs768NSFsid_nono,
|
215 |
+
)
|
216 |
+
import soundfile as sf
|
217 |
+
from fairseq import checkpoint_utils
|
218 |
+
import gradio as gr
|
219 |
+
import logging
|
220 |
+
from vc_infer_pipeline import VC
|
221 |
+
from config import Config
|
222 |
+
|
223 |
+
config = Config()
|
224 |
+
# from trainset_preprocess_pipeline import PreProcess
|
225 |
+
logging.getLogger("numba").setLevel(logging.WARNING)
|
226 |
+
|
227 |
+
hubert_model = None
|
228 |
+
|
229 |
+
def load_hubert():
|
230 |
+
global hubert_model
|
231 |
+
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
|
232 |
+
["hubert_base.pt"],
|
233 |
+
suffix="",
|
234 |
+
)
|
235 |
+
hubert_model = models[0]
|
236 |
+
hubert_model = hubert_model.to(config.device)
|
237 |
+
if config.is_half:
|
238 |
+
hubert_model = hubert_model.half()
|
239 |
+
else:
|
240 |
+
hubert_model = hubert_model.float()
|
241 |
+
hubert_model.eval()
|
242 |
+
|
243 |
+
|
244 |
+
weight_root = "weights"
|
245 |
+
index_root = "logs"
|
246 |
+
names = []
|
247 |
+
for name in os.listdir(weight_root):
|
248 |
+
if name.endswith(".pth"):
|
249 |
+
names.append(name)
|
250 |
+
index_paths = []
|
251 |
+
for root, dirs, files in os.walk(index_root, topdown=False):
|
252 |
+
for name in files:
|
253 |
+
if name.endswith(".index") and "trained" not in name:
|
254 |
+
index_paths.append("%s/%s" % (root, name))
|
255 |
+
|
256 |
+
|
257 |
+
|
258 |
+
def vc_single(
|
259 |
+
sid,
|
260 |
+
input_audio_path,
|
261 |
+
f0_up_key,
|
262 |
+
f0_file,
|
263 |
+
f0_method,
|
264 |
+
file_index,
|
265 |
+
#file_index2,
|
266 |
+
# file_big_npy,
|
267 |
+
index_rate,
|
268 |
+
filter_radius,
|
269 |
+
resample_sr,
|
270 |
+
rms_mix_rate,
|
271 |
+
protect,
|
272 |
+
crepe_hop_length,
|
273 |
+
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
|
274 |
+
global tgt_sr, net_g, vc, hubert_model, version
|
275 |
+
if input_audio_path is None:
|
276 |
+
return "You need to upload an audio", None
|
277 |
+
f0_up_key = int(f0_up_key)
|
278 |
+
try:
|
279 |
+
audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
|
280 |
+
audio_max = np.abs(audio).max() / 0.95
|
281 |
+
if audio_max > 1:
|
282 |
+
audio /= audio_max
|
283 |
+
times = [0, 0, 0]
|
284 |
+
if hubert_model == None:
|
285 |
+
load_hubert()
|
286 |
+
if_f0 = cpt.get("f0", 1)
|
287 |
+
file_index = (
|
288 |
+
(
|
289 |
+
file_index.strip(" ")
|
290 |
+
.strip('"')
|
291 |
+
.strip("\n")
|
292 |
+
.strip('"')
|
293 |
+
.strip(" ")
|
294 |
+
.replace("trained", "added")
|
295 |
+
)
|
296 |
+
) # 防止小白写错,自动帮他替换掉
|
297 |
+
# file_big_npy = (
|
298 |
+
# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
299 |
+
# )
|
300 |
+
audio_opt = vc.pipeline(
|
301 |
+
hubert_model,
|
302 |
+
net_g,
|
303 |
+
sid,
|
304 |
+
audio,
|
305 |
+
input_audio_path,
|
306 |
+
times,
|
307 |
+
f0_up_key,
|
308 |
+
f0_method,
|
309 |
+
file_index,
|
310 |
+
# file_big_npy,
|
311 |
+
index_rate,
|
312 |
+
if_f0,
|
313 |
+
filter_radius,
|
314 |
+
tgt_sr,
|
315 |
+
resample_sr,
|
316 |
+
rms_mix_rate,
|
317 |
+
version,
|
318 |
+
protect,
|
319 |
+
crepe_hop_length,
|
320 |
+
f0_file=f0_file,
|
321 |
+
)
|
322 |
+
if resample_sr >= 16000 and tgt_sr != resample_sr:
|
323 |
+
tgt_sr = resample_sr
|
324 |
+
index_info = (
|
325 |
+
"Using index:%s." % file_index
|
326 |
+
if os.path.exists(file_index)
|
327 |
+
else "Index not used."
|
328 |
+
)
|
329 |
+
return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
|
330 |
+
index_info,
|
331 |
+
times[0],
|
332 |
+
times[1],
|
333 |
+
times[2],
|
334 |
+
), (tgt_sr, audio_opt)
|
335 |
+
except:
|
336 |
+
info = traceback.format_exc()
|
337 |
+
print(info)
|
338 |
+
return info, (None, None)
|
339 |
+
|
340 |
+
|
341 |
+
def vc_multi(
|
342 |
+
sid,
|
343 |
+
dir_path,
|
344 |
+
opt_root,
|
345 |
+
paths,
|
346 |
+
f0_up_key,
|
347 |
+
f0_method,
|
348 |
+
file_index,
|
349 |
+
file_index2,
|
350 |
+
# file_big_npy,
|
351 |
+
index_rate,
|
352 |
+
filter_radius,
|
353 |
+
resample_sr,
|
354 |
+
rms_mix_rate,
|
355 |
+
protect,
|
356 |
+
format1,
|
357 |
+
crepe_hop_length,
|
358 |
+
):
|
359 |
+
try:
|
360 |
+
dir_path = (
|
361 |
+
dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
362 |
+
) # 防止小白拷路径头尾带了空格和"和回车
|
363 |
+
opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
364 |
+
os.makedirs(opt_root, exist_ok=True)
|
365 |
+
try:
|
366 |
+
if dir_path != "":
|
367 |
+
paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
|
368 |
+
else:
|
369 |
+
paths = [path.name for path in paths]
|
370 |
+
except:
|
371 |
+
traceback.print_exc()
|
372 |
+
paths = [path.name for path in paths]
|
373 |
+
infos = []
|
374 |
+
for path in paths:
|
375 |
+
info, opt = vc_single(
|
376 |
+
sid,
|
377 |
+
path,
|
378 |
+
f0_up_key,
|
379 |
+
None,
|
380 |
+
f0_method,
|
381 |
+
file_index,
|
382 |
+
# file_big_npy,
|
383 |
+
index_rate,
|
384 |
+
filter_radius,
|
385 |
+
resample_sr,
|
386 |
+
rms_mix_rate,
|
387 |
+
protect,
|
388 |
+
crepe_hop_length
|
389 |
+
)
|
390 |
+
if "Success" in info:
|
391 |
+
try:
|
392 |
+
tgt_sr, audio_opt = opt
|
393 |
+
if format1 in ["wav", "flac"]:
|
394 |
+
sf.write(
|
395 |
+
"%s/%s.%s" % (opt_root, os.path.basename(path), format1),
|
396 |
+
audio_opt,
|
397 |
+
tgt_sr,
|
398 |
+
)
|
399 |
+
else:
|
400 |
+
path = "%s/%s.wav" % (opt_root, os.path.basename(path))
|
401 |
+
sf.write(
|
402 |
+
path,
|
403 |
+
audio_opt,
|
404 |
+
tgt_sr,
|
405 |
+
)
|
406 |
+
if os.path.exists(path):
|
407 |
+
os.system(
|
408 |
+
"ffmpeg -i %s -vn %s -q:a 2 -y"
|
409 |
+
% (path, path[:-4] + ".%s" % format1)
|
410 |
+
)
|
411 |
+
except:
|
412 |
+
info += traceback.format_exc()
|
413 |
+
infos.append("%s->%s" % (os.path.basename(path), info))
|
414 |
+
yield "\n".join(infos)
|
415 |
+
yield "\n".join(infos)
|
416 |
+
except:
|
417 |
+
yield traceback.format_exc()
|
418 |
+
|
419 |
+
# 一个选项卡全局只能有一个音色
|
420 |
+
def get_vc(sid):
|
421 |
+
global n_spk, tgt_sr, net_g, vc, cpt, version
|
422 |
+
if sid == "" or sid == []:
|
423 |
+
global hubert_model
|
424 |
+
if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
|
425 |
+
print("clean_empty_cache")
|
426 |
+
del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
|
427 |
+
hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
|
428 |
+
if torch.cuda.is_available():
|
429 |
+
torch.cuda.empty_cache()
|
430 |
+
###楼下不这么折腾清理不干净
|
431 |
+
if_f0 = cpt.get("f0", 1)
|
432 |
+
version = cpt.get("version", "v1")
|
433 |
+
if version == "v1":
|
434 |
+
if if_f0 == 1:
|
435 |
+
net_g = SynthesizerTrnMs256NSFsid(
|
436 |
+
*cpt["config"], is_half=config.is_half
|
437 |
+
)
|
438 |
+
else:
|
439 |
+
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
440 |
+
elif version == "v2":
|
441 |
+
if if_f0 == 1:
|
442 |
+
net_g = SynthesizerTrnMs768NSFsid(
|
443 |
+
*cpt["config"], is_half=config.is_half
|
444 |
+
)
|
445 |
+
else:
|
446 |
+
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
|
447 |
+
del net_g, cpt
|
448 |
+
if torch.cuda.is_available():
|
449 |
+
torch.cuda.empty_cache()
|
450 |
+
cpt = None
|
451 |
+
return {"visible": False, "__type__": "update"}
|
452 |
+
person = "%s/%s" % (weight_root, sid)
|
453 |
+
print("loading %s" % person)
|
454 |
+
cpt = torch.load(person, map_location="cpu")
|
455 |
+
tgt_sr = cpt["config"][-1]
|
456 |
+
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
|
457 |
+
if_f0 = cpt.get("f0", 1)
|
458 |
+
version = cpt.get("version", "v1")
|
459 |
+
if version == "v1":
|
460 |
+
if if_f0 == 1:
|
461 |
+
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
|
462 |
+
else:
|
463 |
+
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
464 |
+
elif version == "v2":
|
465 |
+
if if_f0 == 1:
|
466 |
+
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
|
467 |
+
else:
|
468 |
+
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
|
469 |
+
del net_g.enc_q
|
470 |
+
print(net_g.load_state_dict(cpt["weight"], strict=False))
|
471 |
+
net_g.eval().to(config.device)
|
472 |
+
if config.is_half:
|
473 |
+
net_g = net_g.half()
|
474 |
+
else:
|
475 |
+
net_g = net_g.float()
|
476 |
+
vc = VC(tgt_sr, config)
|
477 |
+
n_spk = cpt["config"][-3]
|
478 |
+
return {"visible": False, "maximum": n_spk, "__type__": "update"}
|
479 |
+
|
480 |
+
|
481 |
+
def change_choices():
|
482 |
+
names = []
|
483 |
+
for name in os.listdir(weight_root):
|
484 |
+
if name.endswith(".pth"):
|
485 |
+
names.append(name)
|
486 |
+
index_paths = []
|
487 |
+
for root, dirs, files in os.walk(index_root, topdown=False):
|
488 |
+
for name in files:
|
489 |
+
if name.endswith(".index") and "trained" not in name:
|
490 |
+
index_paths.append("%s/%s" % (root, name))
|
491 |
+
return {"choices": sorted(names), "__type__": "update"}, {
|
492 |
+
"choices": sorted(index_paths),
|
493 |
+
"__type__": "update",
|
494 |
+
}
|
495 |
+
|
496 |
+
|
497 |
+
def clean():
|
498 |
+
return {"value": "", "__type__": "update"}
|
499 |
+
|
500 |
+
|
501 |
+
sr_dict = {
|
502 |
+
"32k": 32000,
|
503 |
+
"40k": 40000,
|
504 |
+
"48k": 48000,
|
505 |
+
}
|
506 |
+
|
507 |
+
|
508 |
+
def if_done(done, p):
|
509 |
+
while 1:
|
510 |
+
if p.poll() == None:
|
511 |
+
sleep(0.5)
|
512 |
+
else:
|
513 |
+
break
|
514 |
+
done[0] = True
|
515 |
+
|
516 |
+
|
517 |
+
def if_done_multi(done, ps):
|
518 |
+
while 1:
|
519 |
+
# poll==None代表进程未结束
|
520 |
+
# 只要有一个进程未结束都不停
|
521 |
+
flag = 1
|
522 |
+
for p in ps:
|
523 |
+
if p.poll() == None:
|
524 |
+
flag = 0
|
525 |
+
sleep(0.5)
|
526 |
+
break
|
527 |
+
if flag == 1:
|
528 |
+
break
|
529 |
+
done[0] = True
|
530 |
+
|
531 |
+
|
532 |
+
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
|
533 |
+
sr = sr_dict[sr]
|
534 |
+
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
535 |
+
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
|
536 |
+
f.close()
|
537 |
+
cmd = (
|
538 |
+
config.python_cmd
|
539 |
+
+ " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
|
540 |
+
% (trainset_dir, sr, n_p, now_dir, exp_dir)
|
541 |
+
+ str(config.noparallel)
|
542 |
+
)
|
543 |
+
print(cmd)
|
544 |
+
p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
|
545 |
+
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
546 |
+
done = [False]
|
547 |
+
threading.Thread(
|
548 |
+
target=if_done,
|
549 |
+
args=(
|
550 |
+
done,
|
551 |
+
p,
|
552 |
+
),
|
553 |
+
).start()
|
554 |
+
while 1:
|
555 |
+
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
556 |
+
yield (f.read())
|
557 |
+
sleep(1)
|
558 |
+
if done[0] == True:
|
559 |
+
break
|
560 |
+
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
561 |
+
log = f.read()
|
562 |
+
print(log)
|
563 |
+
yield log
|
564 |
+
|
565 |
+
# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
|
566 |
+
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl):
|
567 |
+
gpus = gpus.split("-")
|
568 |
+
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
569 |
+
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
|
570 |
+
f.close()
|
571 |
+
if if_f0:
|
572 |
+
cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % (
|
573 |
+
now_dir,
|
574 |
+
exp_dir,
|
575 |
+
n_p,
|
576 |
+
f0method,
|
577 |
+
echl,
|
578 |
+
)
|
579 |
+
print(cmd)
|
580 |
+
p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
|
581 |
+
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
582 |
+
done = [False]
|
583 |
+
threading.Thread(
|
584 |
+
target=if_done,
|
585 |
+
args=(
|
586 |
+
done,
|
587 |
+
p,
|
588 |
+
),
|
589 |
+
).start()
|
590 |
+
while 1:
|
591 |
+
with open(
|
592 |
+
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
|
593 |
+
) as f:
|
594 |
+
yield (f.read())
|
595 |
+
sleep(1)
|
596 |
+
if done[0] == True:
|
597 |
+
break
|
598 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
599 |
+
log = f.read()
|
600 |
+
print(log)
|
601 |
+
yield log
|
602 |
+
####对不同part分别开多进程
|
603 |
+
"""
|
604 |
+
n_part=int(sys.argv[1])
|
605 |
+
i_part=int(sys.argv[2])
|
606 |
+
i_gpu=sys.argv[3]
|
607 |
+
exp_dir=sys.argv[4]
|
608 |
+
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
|
609 |
+
"""
|
610 |
+
leng = len(gpus)
|
611 |
+
ps = []
|
612 |
+
for idx, n_g in enumerate(gpus):
|
613 |
+
cmd = (
|
614 |
+
config.python_cmd
|
615 |
+
+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
|
616 |
+
% (
|
617 |
+
config.device,
|
618 |
+
leng,
|
619 |
+
idx,
|
620 |
+
n_g,
|
621 |
+
now_dir,
|
622 |
+
exp_dir,
|
623 |
+
version19,
|
624 |
+
)
|
625 |
+
)
|
626 |
+
print(cmd)
|
627 |
+
p = Popen(
|
628 |
+
cmd, shell=True, cwd=now_dir
|
629 |
+
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
630 |
+
ps.append(p)
|
631 |
+
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
632 |
+
done = [False]
|
633 |
+
threading.Thread(
|
634 |
+
target=if_done_multi,
|
635 |
+
args=(
|
636 |
+
done,
|
637 |
+
ps,
|
638 |
+
),
|
639 |
+
).start()
|
640 |
+
while 1:
|
641 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
642 |
+
yield (f.read())
|
643 |
+
sleep(1)
|
644 |
+
if done[0] == True:
|
645 |
+
break
|
646 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
647 |
+
log = f.read()
|
648 |
+
print(log)
|
649 |
+
yield log
|
650 |
+
|
651 |
+
|
652 |
+
def change_sr2(sr2, if_f0_3, version19):
|
653 |
+
path_str = "" if version19 == "v1" else "_v2"
|
654 |
+
f0_str = "f0" if if_f0_3 else ""
|
655 |
+
if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
|
656 |
+
if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
|
657 |
+
if (if_pretrained_generator_exist == False):
|
658 |
+
print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
|
659 |
+
if (if_pretrained_discriminator_exist == False):
|
660 |
+
print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
|
661 |
+
return (
|
662 |
+
("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
|
663 |
+
("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
|
664 |
+
{"visible": True, "__type__": "update"}
|
665 |
+
)
|
666 |
+
|
667 |
+
def change_version19(sr2, if_f0_3, version19):
|
668 |
+
path_str = "" if version19 == "v1" else "_v2"
|
669 |
+
f0_str = "f0" if if_f0_3 else ""
|
670 |
+
if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
|
671 |
+
if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
|
672 |
+
if (if_pretrained_generator_exist == False):
|
673 |
+
print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
|
674 |
+
if (if_pretrained_discriminator_exist == False):
|
675 |
+
print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
|
676 |
+
return (
|
677 |
+
("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
|
678 |
+
("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
|
679 |
+
)
|
680 |
+
|
681 |
+
|
682 |
+
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
|
683 |
+
path_str = "" if version19 == "v1" else "_v2"
|
684 |
+
if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
|
685 |
+
if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
|
686 |
+
if (if_pretrained_generator_exist == False):
|
687 |
+
print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
|
688 |
+
if (if_pretrained_discriminator_exist == False):
|
689 |
+
print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
|
690 |
+
if if_f0_3:
|
691 |
+
return (
|
692 |
+
{"visible": True, "__type__": "update"},
|
693 |
+
"pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
|
694 |
+
"pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
|
695 |
+
)
|
696 |
+
return (
|
697 |
+
{"visible": False, "__type__": "update"},
|
698 |
+
("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
|
699 |
+
("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
|
700 |
+
)
|
701 |
+
|
702 |
+
|
703 |
+
global log_interval
|
704 |
+
|
705 |
+
|
706 |
+
def set_log_interval(exp_dir, batch_size12):
|
707 |
+
log_interval = 1
|
708 |
+
|
709 |
+
folder_path = os.path.join(exp_dir, "1_16k_wavs")
|
710 |
+
|
711 |
+
if os.path.exists(folder_path) and os.path.isdir(folder_path):
|
712 |
+
wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")]
|
713 |
+
if wav_files:
|
714 |
+
sample_size = len(wav_files)
|
715 |
+
log_interval = math.ceil(sample_size / batch_size12)
|
716 |
+
if log_interval > 1:
|
717 |
+
log_interval += 1
|
718 |
+
return log_interval
|
719 |
+
|
720 |
+
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
|
721 |
+
def click_train(
|
722 |
+
exp_dir1,
|
723 |
+
sr2,
|
724 |
+
if_f0_3,
|
725 |
+
spk_id5,
|
726 |
+
save_epoch10,
|
727 |
+
total_epoch11,
|
728 |
+
batch_size12,
|
729 |
+
if_save_latest13,
|
730 |
+
pretrained_G14,
|
731 |
+
pretrained_D15,
|
732 |
+
gpus16,
|
733 |
+
if_cache_gpu17,
|
734 |
+
if_save_every_weights18,
|
735 |
+
version19,
|
736 |
+
):
|
737 |
+
CSVutil('csvdb/stop.csv', 'w+', 'formanting', False)
|
738 |
+
# 生成filelist
|
739 |
+
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
740 |
+
os.makedirs(exp_dir, exist_ok=True)
|
741 |
+
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
|
742 |
+
feature_dir = (
|
743 |
+
"%s/3_feature256" % (exp_dir)
|
744 |
+
if version19 == "v1"
|
745 |
+
else "%s/3_feature768" % (exp_dir)
|
746 |
+
)
|
747 |
+
|
748 |
+
log_interval = set_log_interval(exp_dir, batch_size12)
|
749 |
+
|
750 |
+
if if_f0_3:
|
751 |
+
f0_dir = "%s/2a_f0" % (exp_dir)
|
752 |
+
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
|
753 |
+
names = (
|
754 |
+
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
755 |
+
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
756 |
+
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
757 |
+
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
758 |
+
)
|
759 |
+
else:
|
760 |
+
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
761 |
+
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
762 |
+
)
|
763 |
+
opt = []
|
764 |
+
for name in names:
|
765 |
+
if if_f0_3:
|
766 |
+
opt.append(
|
767 |
+
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
|
768 |
+
% (
|
769 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
770 |
+
name,
|
771 |
+
feature_dir.replace("\\", "\\\\"),
|
772 |
+
name,
|
773 |
+
f0_dir.replace("\\", "\\\\"),
|
774 |
+
name,
|
775 |
+
f0nsf_dir.replace("\\", "\\\\"),
|
776 |
+
name,
|
777 |
+
spk_id5,
|
778 |
+
)
|
779 |
+
)
|
780 |
+
else:
|
781 |
+
opt.append(
|
782 |
+
"%s/%s.wav|%s/%s.npy|%s"
|
783 |
+
% (
|
784 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
785 |
+
name,
|
786 |
+
feature_dir.replace("\\", "\\\\"),
|
787 |
+
name,
|
788 |
+
spk_id5,
|
789 |
+
)
|
790 |
+
)
|
791 |
+
fea_dim = 256 if version19 == "v1" else 768
|
792 |
+
if if_f0_3:
|
793 |
+
for _ in range(2):
|
794 |
+
opt.append(
|
795 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
796 |
+
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
|
797 |
+
)
|
798 |
+
else:
|
799 |
+
for _ in range(2):
|
800 |
+
opt.append(
|
801 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
|
802 |
+
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
|
803 |
+
)
|
804 |
+
shuffle(opt)
|
805 |
+
with open("%s/filelist.txt" % exp_dir, "w") as f:
|
806 |
+
f.write("\n".join(opt))
|
807 |
+
print("write filelist done")
|
808 |
+
# 生成config#无需生成config
|
809 |
+
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
|
810 |
+
print("use gpus:", gpus16)
|
811 |
+
if pretrained_G14 == "":
|
812 |
+
print("no pretrained Generator")
|
813 |
+
if pretrained_D15 == "":
|
814 |
+
print("no pretrained Discriminator")
|
815 |
+
if gpus16:
|
816 |
+
cmd = (
|
817 |
+
config.python_cmd
|
818 |
+
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
|
819 |
+
% (
|
820 |
+
exp_dir1,
|
821 |
+
sr2,
|
822 |
+
1 if if_f0_3 else 0,
|
823 |
+
batch_size12,
|
824 |
+
gpus16,
|
825 |
+
total_epoch11,
|
826 |
+
save_epoch10,
|
827 |
+
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
|
828 |
+
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
|
829 |
+
1 if if_save_latest13 == True else 0,
|
830 |
+
1 if if_cache_gpu17 == True else 0,
|
831 |
+
1 if if_save_every_weights18 == True else 0,
|
832 |
+
version19,
|
833 |
+
log_interval,
|
834 |
+
)
|
835 |
+
)
|
836 |
+
else:
|
837 |
+
cmd = (
|
838 |
+
config.python_cmd
|
839 |
+
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
|
840 |
+
% (
|
841 |
+
exp_dir1,
|
842 |
+
sr2,
|
843 |
+
1 if if_f0_3 else 0,
|
844 |
+
batch_size12,
|
845 |
+
total_epoch11,
|
846 |
+
save_epoch10,
|
847 |
+
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
|
848 |
+
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
|
849 |
+
1 if if_save_latest13 == True else 0,
|
850 |
+
1 if if_cache_gpu17 == True else 0,
|
851 |
+
1 if if_save_every_weights18 == True else 0,
|
852 |
+
version19,
|
853 |
+
log_interval,
|
854 |
+
)
|
855 |
+
)
|
856 |
+
print(cmd)
|
857 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
858 |
+
global PID
|
859 |
+
PID = p.pid
|
860 |
+
p.wait()
|
861 |
+
return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"})
|
862 |
+
|
863 |
+
|
864 |
+
# but4.click(train_index, [exp_dir1], info3)
|
865 |
+
def train_index(exp_dir1, version19):
|
866 |
+
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
867 |
+
os.makedirs(exp_dir, exist_ok=True)
|
868 |
+
feature_dir = (
|
869 |
+
"%s/3_feature256" % (exp_dir)
|
870 |
+
if version19 == "v1"
|
871 |
+
else "%s/3_feature768" % (exp_dir)
|
872 |
+
)
|
873 |
+
if os.path.exists(feature_dir) == False:
|
874 |
+
return "请先进行特征提取!"
|
875 |
+
listdir_res = list(os.listdir(feature_dir))
|
876 |
+
if len(listdir_res) == 0:
|
877 |
+
return "请先进行特征提取!"
|
878 |
+
npys = []
|
879 |
+
for name in sorted(listdir_res):
|
880 |
+
phone = np.load("%s/%s" % (feature_dir, name))
|
881 |
+
npys.append(phone)
|
882 |
+
big_npy = np.concatenate(npys, 0)
|
883 |
+
big_npy_idx = np.arange(big_npy.shape[0])
|
884 |
+
np.random.shuffle(big_npy_idx)
|
885 |
+
big_npy = big_npy[big_npy_idx]
|
886 |
+
np.save("%s/total_fea.npy" % exp_dir, big_npy)
|
887 |
+
# n_ivf = big_npy.shape[0] // 39
|
888 |
+
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
|
889 |
+
infos = []
|
890 |
+
infos.append("%s,%s" % (big_npy.shape, n_ivf))
|
891 |
+
yield "\n".join(infos)
|
892 |
+
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
|
893 |
+
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
|
894 |
+
infos.append("training")
|
895 |
+
yield "\n".join(infos)
|
896 |
+
index_ivf = faiss.extract_index_ivf(index) #
|
897 |
+
index_ivf.nprobe = 1
|
898 |
+
index.train(big_npy)
|
899 |
+
faiss.write_index(
|
900 |
+
index,
|
901 |
+
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
902 |
+
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
903 |
+
)
|
904 |
+
# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
905 |
+
infos.append("adding")
|
906 |
+
yield "\n".join(infos)
|
907 |
+
batch_size_add = 8192
|
908 |
+
for i in range(0, big_npy.shape[0], batch_size_add):
|
909 |
+
index.add(big_npy[i : i + batch_size_add])
|
910 |
+
faiss.write_index(
|
911 |
+
index,
|
912 |
+
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
913 |
+
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
914 |
+
)
|
915 |
+
infos.append(
|
916 |
+
"成功构建索引,added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
917 |
+
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
|
918 |
+
)
|
919 |
+
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
920 |
+
# infos.append("成功构建索引,added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
|
921 |
+
yield "\n".join(infos)
|
922 |
+
|
923 |
+
|
924 |
+
# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
|
925 |
+
def train1key(
|
926 |
+
exp_dir1,
|
927 |
+
sr2,
|
928 |
+
if_f0_3,
|
929 |
+
trainset_dir4,
|
930 |
+
spk_id5,
|
931 |
+
np7,
|
932 |
+
f0method8,
|
933 |
+
save_epoch10,
|
934 |
+
total_epoch11,
|
935 |
+
batch_size12,
|
936 |
+
if_save_latest13,
|
937 |
+
pretrained_G14,
|
938 |
+
pretrained_D15,
|
939 |
+
gpus16,
|
940 |
+
if_cache_gpu17,
|
941 |
+
if_save_every_weights18,
|
942 |
+
version19,
|
943 |
+
echl
|
944 |
+
):
|
945 |
+
infos = []
|
946 |
+
|
947 |
+
def get_info_str(strr):
|
948 |
+
infos.append(strr)
|
949 |
+
return "\n".join(infos)
|
950 |
+
|
951 |
+
model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
952 |
+
preprocess_log_path = "%s/preprocess.log" % model_log_dir
|
953 |
+
extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
|
954 |
+
gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
|
955 |
+
feature_dir = (
|
956 |
+
"%s/3_feature256" % model_log_dir
|
957 |
+
if version19 == "v1"
|
958 |
+
else "%s/3_feature768" % model_log_dir
|
959 |
+
)
|
960 |
+
|
961 |
+
os.makedirs(model_log_dir, exist_ok=True)
|
962 |
+
#########step1:处理数据
|
963 |
+
open(preprocess_log_path, "w").close()
|
964 |
+
cmd = (
|
965 |
+
config.python_cmd
|
966 |
+
+ " trainset_preprocess_pipeline_print.py %s %s %s %s "
|
967 |
+
% (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
|
968 |
+
+ str(config.noparallel)
|
969 |
+
)
|
970 |
+
yield get_info_str(i18n("step1:正在处理数据"))
|
971 |
+
yield get_info_str(cmd)
|
972 |
+
p = Popen(cmd, shell=True)
|
973 |
+
p.wait()
|
974 |
+
with open(preprocess_log_path, "r") as f:
|
975 |
+
print(f.read())
|
976 |
+
#########step2a:提取音高
|
977 |
+
open(extract_f0_feature_log_path, "w")
|
978 |
+
if if_f0_3:
|
979 |
+
yield get_info_str("step2a:正在提取音高")
|
980 |
+
cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % (
|
981 |
+
model_log_dir,
|
982 |
+
np7,
|
983 |
+
f0method8,
|
984 |
+
echl
|
985 |
+
)
|
986 |
+
yield get_info_str(cmd)
|
987 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
988 |
+
p.wait()
|
989 |
+
with open(extract_f0_feature_log_path, "r") as f:
|
990 |
+
print(f.read())
|
991 |
+
else:
|
992 |
+
yield get_info_str(i18n("step2a:无需提取音高"))
|
993 |
+
#######step2b:提取特征
|
994 |
+
yield get_info_str(i18n("step2b:正在提取特征"))
|
995 |
+
gpus = gpus16.split("-")
|
996 |
+
leng = len(gpus)
|
997 |
+
ps = []
|
998 |
+
for idx, n_g in enumerate(gpus):
|
999 |
+
cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
|
1000 |
+
config.device,
|
1001 |
+
leng,
|
1002 |
+
idx,
|
1003 |
+
n_g,
|
1004 |
+
model_log_dir,
|
1005 |
+
version19,
|
1006 |
+
)
|
1007 |
+
yield get_info_str(cmd)
|
1008 |
+
p = Popen(
|
1009 |
+
cmd, shell=True, cwd=now_dir
|
1010 |
+
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
1011 |
+
ps.append(p)
|
1012 |
+
for p in ps:
|
1013 |
+
p.wait()
|
1014 |
+
with open(extract_f0_feature_log_path, "r") as f:
|
1015 |
+
print(f.read())
|
1016 |
+
#######step3a:训练模型
|
1017 |
+
yield get_info_str(i18n("step3a:正在训练模型"))
|
1018 |
+
# 生成filelist
|
1019 |
+
if if_f0_3:
|
1020 |
+
f0_dir = "%s/2a_f0" % model_log_dir
|
1021 |
+
f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
|
1022 |
+
names = (
|
1023 |
+
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
1024 |
+
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
1025 |
+
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
1026 |
+
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
1027 |
+
)
|
1028 |
+
else:
|
1029 |
+
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
1030 |
+
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
1031 |
+
)
|
1032 |
+
opt = []
|
1033 |
+
for name in names:
|
1034 |
+
if if_f0_3:
|
1035 |
+
opt.append(
|
1036 |
+
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
|
1037 |
+
% (
|
1038 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
1039 |
+
name,
|
1040 |
+
feature_dir.replace("\\", "\\\\"),
|
1041 |
+
name,
|
1042 |
+
f0_dir.replace("\\", "\\\\"),
|
1043 |
+
name,
|
1044 |
+
f0nsf_dir.replace("\\", "\\\\"),
|
1045 |
+
name,
|
1046 |
+
spk_id5,
|
1047 |
+
)
|
1048 |
+
)
|
1049 |
+
else:
|
1050 |
+
opt.append(
|
1051 |
+
"%s/%s.wav|%s/%s.npy|%s"
|
1052 |
+
% (
|
1053 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
1054 |
+
name,
|
1055 |
+
feature_dir.replace("\\", "\\\\"),
|
1056 |
+
name,
|
1057 |
+
spk_id5,
|
1058 |
+
)
|
1059 |
+
)
|
1060 |
+
fea_dim = 256 if version19 == "v1" else 768
|
1061 |
+
if if_f0_3:
|
1062 |
+
for _ in range(2):
|
1063 |
+
opt.append(
|
1064 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
1065 |
+
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
|
1066 |
+
)
|
1067 |
+
else:
|
1068 |
+
for _ in range(2):
|
1069 |
+
opt.append(
|
1070 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
|
1071 |
+
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
|
1072 |
+
)
|
1073 |
+
shuffle(opt)
|
1074 |
+
with open("%s/filelist.txt" % model_log_dir, "w") as f:
|
1075 |
+
f.write("\n".join(opt))
|
1076 |
+
yield get_info_str("write filelist done")
|
1077 |
+
if gpus16:
|
1078 |
+
cmd = (
|
1079 |
+
config.python_cmd
|
1080 |
+
+" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
|
1081 |
+
% (
|
1082 |
+
exp_dir1,
|
1083 |
+
sr2,
|
1084 |
+
1 if if_f0_3 else 0,
|
1085 |
+
batch_size12,
|
1086 |
+
gpus16,
|
1087 |
+
total_epoch11,
|
1088 |
+
save_epoch10,
|
1089 |
+
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
|
1090 |
+
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
|
1091 |
+
1 if if_save_latest13 == True else 0,
|
1092 |
+
1 if if_cache_gpu17 == True else 0,
|
1093 |
+
1 if if_save_every_weights18 == True else 0,
|
1094 |
+
version19,
|
1095 |
+
)
|
1096 |
+
)
|
1097 |
+
else:
|
1098 |
+
cmd = (
|
1099 |
+
config.python_cmd
|
1100 |
+
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
|
1101 |
+
% (
|
1102 |
+
exp_dir1,
|
1103 |
+
sr2,
|
1104 |
+
1 if if_f0_3 else 0,
|
1105 |
+
batch_size12,
|
1106 |
+
total_epoch11,
|
1107 |
+
save_epoch10,
|
1108 |
+
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
|
1109 |
+
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
|
1110 |
+
1 if if_save_latest13 == True else 0,
|
1111 |
+
1 if if_cache_gpu17 == True else 0,
|
1112 |
+
1 if if_save_every_weights18 == True else 0,
|
1113 |
+
version19,
|
1114 |
+
)
|
1115 |
+
)
|
1116 |
+
yield get_info_str(cmd)
|
1117 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
1118 |
+
p.wait()
|
1119 |
+
yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"))
|
1120 |
+
#######step3b:训练索引
|
1121 |
+
npys = []
|
1122 |
+
listdir_res = list(os.listdir(feature_dir))
|
1123 |
+
for name in sorted(listdir_res):
|
1124 |
+
phone = np.load("%s/%s" % (feature_dir, name))
|
1125 |
+
npys.append(phone)
|
1126 |
+
big_npy = np.concatenate(npys, 0)
|
1127 |
+
|
1128 |
+
big_npy_idx = np.arange(big_npy.shape[0])
|
1129 |
+
np.random.shuffle(big_npy_idx)
|
1130 |
+
big_npy = big_npy[big_npy_idx]
|
1131 |
+
np.save("%s/total_fea.npy" % model_log_dir, big_npy)
|
1132 |
+
|
1133 |
+
# n_ivf = big_npy.shape[0] // 39
|
1134 |
+
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
|
1135 |
+
yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
|
1136 |
+
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
|
1137 |
+
yield get_info_str("training index")
|
1138 |
+
index_ivf = faiss.extract_index_ivf(index) #
|
1139 |
+
index_ivf.nprobe = 1
|
1140 |
+
index.train(big_npy)
|
1141 |
+
faiss.write_index(
|
1142 |
+
index,
|
1143 |
+
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
1144 |
+
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
1145 |
+
)
|
1146 |
+
yield get_info_str("adding index")
|
1147 |
+
batch_size_add = 8192
|
1148 |
+
for i in range(0, big_npy.shape[0], batch_size_add):
|
1149 |
+
index.add(big_npy[i : i + batch_size_add])
|
1150 |
+
faiss.write_index(
|
1151 |
+
index,
|
1152 |
+
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
1153 |
+
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
1154 |
+
)
|
1155 |
+
yield get_info_str(
|
1156 |
+
"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
1157 |
+
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
|
1158 |
+
)
|
1159 |
+
yield get_info_str(i18n("全流程结束!"))
|
1160 |
+
|
1161 |
+
|
1162 |
+
def whethercrepeornah(radio):
|
1163 |
+
mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
|
1164 |
+
return ({"visible": mango, "__type__": "update"})
|
1165 |
+
|
1166 |
+
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
1167 |
+
def change_info_(ckpt_path):
|
1168 |
+
if (
|
1169 |
+
os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
|
1170 |
+
== False
|
1171 |
+
):
|
1172 |
+
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
1173 |
+
try:
|
1174 |
+
with open(
|
1175 |
+
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
|
1176 |
+
) as f:
|
1177 |
+
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
|
1178 |
+
sr, f0 = info["sample_rate"], info["if_f0"]
|
1179 |
+
version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
|
1180 |
+
return sr, str(f0), version
|
1181 |
+
except:
|
1182 |
+
traceback.print_exc()
|
1183 |
+
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
1184 |
+
|
1185 |
+
|
1186 |
+
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
|
1187 |
+
|
1188 |
+
|
1189 |
+
def export_onnx(ModelPath, ExportedPath, MoeVS=True):
|
1190 |
+
cpt = torch.load(ModelPath, map_location="cpu")
|
1191 |
+
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
|
1192 |
+
hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels,为768Vec做准备
|
1193 |
+
|
1194 |
+
test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
|
1195 |
+
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用)
|
1196 |
+
test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹)
|
1197 |
+
test_pitchf = torch.rand(1, 200) # nsf基频
|
1198 |
+
test_ds = torch.LongTensor([0]) # 说话人ID
|
1199 |
+
test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子)
|
1200 |
+
|
1201 |
+
device = "cpu" # 导出时设备(不影响使用模型)
|
1202 |
+
|
1203 |
+
|
1204 |
+
net_g = SynthesizerTrnMsNSFsidM(
|
1205 |
+
*cpt["config"], is_half=False,version=cpt.get("version","v1")
|
1206 |
+
) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16)
|
1207 |
+
net_g.load_state_dict(cpt["weight"], strict=False)
|
1208 |
+
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
|
1209 |
+
output_names = [
|
1210 |
+
"audio",
|
1211 |
+
]
|
1212 |
+
# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
|
1213 |
+
torch.onnx.export(
|
1214 |
+
net_g,
|
1215 |
+
(
|
1216 |
+
test_phone.to(device),
|
1217 |
+
test_phone_lengths.to(device),
|
1218 |
+
test_pitch.to(device),
|
1219 |
+
test_pitchf.to(device),
|
1220 |
+
test_ds.to(device),
|
1221 |
+
test_rnd.to(device),
|
1222 |
+
),
|
1223 |
+
ExportedPath,
|
1224 |
+
dynamic_axes={
|
1225 |
+
"phone": [1],
|
1226 |
+
"pitch": [1],
|
1227 |
+
"pitchf": [1],
|
1228 |
+
"rnd": [2],
|
1229 |
+
},
|
1230 |
+
do_constant_folding=False,
|
1231 |
+
opset_version=16,
|
1232 |
+
verbose=False,
|
1233 |
+
input_names=input_names,
|
1234 |
+
output_names=output_names,
|
1235 |
+
)
|
1236 |
+
return "Finished"
|
1237 |
+
|
1238 |
+
#region RVC WebUI App
|
1239 |
+
|
1240 |
+
def get_presets():
|
1241 |
+
data = None
|
1242 |
+
with open('../inference-presets.json', 'r') as file:
|
1243 |
+
data = json.load(file)
|
1244 |
+
preset_names = []
|
1245 |
+
for preset in data['presets']:
|
1246 |
+
preset_names.append(preset['name'])
|
1247 |
+
|
1248 |
+
return preset_names
|
1249 |
+
|
1250 |
+
def change_choices2():
|
1251 |
+
audio_files=[]
|
1252 |
+
for filename in os.listdir("./audios"):
|
1253 |
+
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
|
1254 |
+
audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
|
1255 |
+
return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
|
1256 |
+
|
1257 |
+
audio_files=[]
|
1258 |
+
for filename in os.listdir("./audios"):
|
1259 |
+
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
|
1260 |
+
audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
|
1261 |
+
|
1262 |
+
def get_index():
|
1263 |
+
if check_for_name() != '':
|
1264 |
+
chosen_model=sorted(names)[0].split(".")[0]
|
1265 |
+
logs_path="./logs/"+chosen_model
|
1266 |
+
if os.path.exists(logs_path):
|
1267 |
+
for file in os.listdir(logs_path):
|
1268 |
+
if file.endswith(".index"):
|
1269 |
+
return os.path.join(logs_path, file)
|
1270 |
+
return ''
|
1271 |
+
else:
|
1272 |
+
return ''
|
1273 |
+
|
1274 |
+
def get_indexes():
|
1275 |
+
indexes_list=[]
|
1276 |
+
for dirpath, dirnames, filenames in os.walk("./logs/"):
|
1277 |
+
for filename in filenames:
|
1278 |
+
if filename.endswith(".index"):
|
1279 |
+
indexes_list.append(os.path.join(dirpath,filename))
|
1280 |
+
if len(indexes_list) > 0:
|
1281 |
+
return indexes_list
|
1282 |
+
else:
|
1283 |
+
return ''
|
1284 |
+
|
1285 |
+
def get_name():
|
1286 |
+
if len(audio_files) > 0:
|
1287 |
+
return sorted(audio_files)[0]
|
1288 |
+
else:
|
1289 |
+
return ''
|
1290 |
+
|
1291 |
+
def save_to_wav(record_button):
|
1292 |
+
if record_button is None:
|
1293 |
+
pass
|
1294 |
+
else:
|
1295 |
+
path_to_file=record_button
|
1296 |
+
new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
|
1297 |
+
new_path='./audios/'+new_name
|
1298 |
+
shutil.move(path_to_file,new_path)
|
1299 |
+
return new_path
|
1300 |
+
|
1301 |
+
def save_to_wav2(dropbox):
|
1302 |
+
file_path=dropbox.name
|
1303 |
+
shutil.move(file_path,'./audios')
|
1304 |
+
return os.path.join('./audios',os.path.basename(file_path))
|
1305 |
+
|
1306 |
+
def match_index(sid0):
|
1307 |
+
folder=sid0.split(".")[0]
|
1308 |
+
parent_dir="./logs/"+folder
|
1309 |
+
if os.path.exists(parent_dir):
|
1310 |
+
for filename in os.listdir(parent_dir):
|
1311 |
+
if filename.endswith(".index"):
|
1312 |
+
index_path=os.path.join(parent_dir,filename)
|
1313 |
+
return index_path
|
1314 |
+
else:
|
1315 |
+
return ''
|
1316 |
+
|
1317 |
+
def check_for_name():
|
1318 |
+
if len(names) > 0:
|
1319 |
+
return sorted(names)[0]
|
1320 |
+
else:
|
1321 |
+
return ''
|
1322 |
+
|
1323 |
+
def download_from_url(url, model):
|
1324 |
+
if url == '':
|
1325 |
+
return "URL cannot be left empty."
|
1326 |
+
if model =='':
|
1327 |
+
return "You need to name your model. For example: My-Model"
|
1328 |
+
url = url.strip()
|
1329 |
+
zip_dirs = ["zips", "unzips"]
|
1330 |
+
for directory in zip_dirs:
|
1331 |
+
if os.path.exists(directory):
|
1332 |
+
shutil.rmtree(directory)
|
1333 |
+
os.makedirs("zips", exist_ok=True)
|
1334 |
+
os.makedirs("unzips", exist_ok=True)
|
1335 |
+
zipfile = model + '.zip'
|
1336 |
+
zipfile_path = './zips/' + zipfile
|
1337 |
+
try:
|
1338 |
+
if "drive.google.com" in url:
|
1339 |
+
subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
|
1340 |
+
elif "mega.nz" in url:
|
1341 |
+
m = Mega()
|
1342 |
+
m.download_url(url, './zips')
|
1343 |
+
else:
|
1344 |
+
subprocess.run(["wget", url, "-O", zipfile_path])
|
1345 |
+
for filename in os.listdir("./zips"):
|
1346 |
+
if filename.endswith(".zip"):
|
1347 |
+
zipfile_path = os.path.join("./zips/",filename)
|
1348 |
+
shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
|
1349 |
+
else:
|
1350 |
+
return "No zipfile found."
|
1351 |
+
for root, dirs, files in os.walk('./unzips'):
|
1352 |
+
for file in files:
|
1353 |
+
file_path = os.path.join(root, file)
|
1354 |
+
if file.endswith(".index"):
|
1355 |
+
os.mkdir(f'./logs/{model}')
|
1356 |
+
shutil.copy2(file_path,f'./logs/{model}')
|
1357 |
+
elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
|
1358 |
+
shutil.copy(file_path,f'./weights/{model}.pth')
|
1359 |
+
shutil.rmtree("zips")
|
1360 |
+
shutil.rmtree("unzips")
|
1361 |
+
return "Success."
|
1362 |
+
except:
|
1363 |
+
return "There's been an error."
|
1364 |
+
def success_message(face):
|
1365 |
+
return f'{face.name} has been uploaded.', 'None'
|
1366 |
+
def mouth(size, face, voice, faces):
|
1367 |
+
if size == 'Half':
|
1368 |
+
size = 2
|
1369 |
+
else:
|
1370 |
+
size = 1
|
1371 |
+
if faces == 'None':
|
1372 |
+
character = face.name
|
1373 |
+
else:
|
1374 |
+
if faces == 'Ben Shapiro':
|
1375 |
+
character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4'
|
1376 |
+
elif faces == 'Andrew Tate':
|
1377 |
+
character = '/content/wav2lip-HD/inputs/tate-7.mp4'
|
1378 |
+
command = "python inference.py " \
|
1379 |
+
"--checkpoint_path checkpoints/wav2lip.pth " \
|
1380 |
+
f"--face {character} " \
|
1381 |
+
f"--audio {voice} " \
|
1382 |
+
"--pads 0 20 0 0 " \
|
1383 |
+
"--outfile /content/wav2lip-HD/outputs/result.mp4 " \
|
1384 |
+
"--fps 24 " \
|
1385 |
+
f"--resize_factor {size}"
|
1386 |
+
process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master')
|
1387 |
+
stdout, stderr = process.communicate()
|
1388 |
+
return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.'
|
1389 |
+
eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli']
|
1390 |
+
eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O']
|
1391 |
+
chosen_voice = dict(zip(eleven_voices, eleven_voices_ids))
|
1392 |
+
|
1393 |
+
def stoptraining(mim):
|
1394 |
+
if int(mim) == 1:
|
1395 |
+
try:
|
1396 |
+
CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True')
|
1397 |
+
os.kill(PID, signal.SIGTERM)
|
1398 |
+
except Exception as e:
|
1399 |
+
print(f"Couldn't click due to {e}")
|
1400 |
+
return (
|
1401 |
+
{"visible": False, "__type__": "update"},
|
1402 |
+
{"visible": True, "__type__": "update"},
|
1403 |
+
)
|
1404 |
+
|
1405 |
+
|
1406 |
+
def elevenTTS(xiapi, text, id, lang):
|
1407 |
+
if xiapi!= '' and id !='':
|
1408 |
+
choice = chosen_voice[id]
|
1409 |
+
CHUNK_SIZE = 1024
|
1410 |
+
url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}"
|
1411 |
+
headers = {
|
1412 |
+
"Accept": "audio/mpeg",
|
1413 |
+
"Content-Type": "application/json",
|
1414 |
+
"xi-api-key": xiapi
|
1415 |
+
}
|
1416 |
+
if lang == 'en':
|
1417 |
+
data = {
|
1418 |
+
"text": text,
|
1419 |
+
"model_id": "eleven_monolingual_v1",
|
1420 |
+
"voice_settings": {
|
1421 |
+
"stability": 0.5,
|
1422 |
+
"similarity_boost": 0.5
|
1423 |
+
}
|
1424 |
+
}
|
1425 |
+
else:
|
1426 |
+
data = {
|
1427 |
+
"text": text,
|
1428 |
+
"model_id": "eleven_multilingual_v1",
|
1429 |
+
"voice_settings": {
|
1430 |
+
"stability": 0.5,
|
1431 |
+
"similarity_boost": 0.5
|
1432 |
+
}
|
1433 |
+
}
|
1434 |
+
|
1435 |
+
response = requests.post(url, json=data, headers=headers)
|
1436 |
+
with open('./temp_eleven.mp3', 'wb') as f:
|
1437 |
+
for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
|
1438 |
+
if chunk:
|
1439 |
+
f.write(chunk)
|
1440 |
+
aud_path = save_to_wav('./temp_eleven.mp3')
|
1441 |
+
return aud_path, aud_path
|
1442 |
+
else:
|
1443 |
+
tts = gTTS(text, lang=lang)
|
1444 |
+
tts.save('./temp_gTTS.mp3')
|
1445 |
+
aud_path = save_to_wav('./temp_gTTS.mp3')
|
1446 |
+
return aud_path, aud_path
|
1447 |
+
|
1448 |
+
def upload_to_dataset(files, dir):
|
1449 |
+
if dir == '':
|
1450 |
+
dir = './dataset'
|
1451 |
+
if not os.path.exists(dir):
|
1452 |
+
os.makedirs(dir)
|
1453 |
+
count = 0
|
1454 |
+
for file in files:
|
1455 |
+
path=file.name
|
1456 |
+
shutil.copy2(path,dir)
|
1457 |
+
count += 1
|
1458 |
+
return f' {count} files uploaded to {dir}.'
|
1459 |
+
|
1460 |
+
def zip_downloader(model):
|
1461 |
+
if not os.path.exists(f'./weights/{model}.pth'):
|
1462 |
+
return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth'
|
1463 |
+
index_found = False
|
1464 |
+
for file in os.listdir(f'./logs/{model}'):
|
1465 |
+
if file.endswith('.index') and 'added' in file:
|
1466 |
+
log_file = file
|
1467 |
+
index_found = True
|
1468 |
+
if index_found:
|
1469 |
+
return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
|
1470 |
+
else:
|
1471 |
+
return f'./weights/{model}.pth', "Could not find Index file."
|
1472 |
+
|
1473 |
+
with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app:
|
1474 |
+
with gr.Tabs():
|
1475 |
+
with gr.TabItem("Inference"):
|
1476 |
+
gr.HTML("<h1> RVC V2 Huggingface Version </h1>")
|
1477 |
+
|
1478 |
+
# Inference Preset Row
|
1479 |
+
# with gr.Row():
|
1480 |
+
# mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets()))
|
1481 |
+
# mangio_preset_name_save = gr.Textbox(
|
1482 |
+
# label="Your preset name"
|
1483 |
+
# )
|
1484 |
+
# mangio_preset_save_btn = gr.Button('Save Preset', variant="primary")
|
1485 |
+
|
1486 |
+
# Other RVC stuff
|
1487 |
+
with gr.Row():
|
1488 |
+
sid0 = gr.Dropdown(label="1.Choose your Model.", choices=sorted(names), value=check_for_name())
|
1489 |
+
refresh_button = gr.Button("Refresh", variant="primary")
|
1490 |
+
if check_for_name() != '':
|
1491 |
+
get_vc(sorted(names)[0])
|
1492 |
+
vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0)
|
1493 |
+
#clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
|
1494 |
+
spk_item = gr.Slider(
|
1495 |
+
minimum=0,
|
1496 |
+
maximum=2333,
|
1497 |
+
step=1,
|
1498 |
+
label=i18n("请选择说话人id"),
|
1499 |
+
value=0,
|
1500 |
+
visible=False,
|
1501 |
+
interactive=True,
|
1502 |
+
)
|
1503 |
+
#clean_button.click(fn=clean, inputs=[], outputs=[sid0])
|
1504 |
+
sid0.change(
|
1505 |
+
fn=get_vc,
|
1506 |
+
inputs=[sid0],
|
1507 |
+
outputs=[spk_item],
|
1508 |
+
)
|
1509 |
+
but0 = gr.Button("Convert", variant="primary")
|
1510 |
+
with gr.Row():
|
1511 |
+
with gr.Column():
|
1512 |
+
with gr.Row():
|
1513 |
+
dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
|
1514 |
+
with gr.Row():
|
1515 |
+
record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
|
1516 |
+
with gr.Row():
|
1517 |
+
input_audio0 = gr.Dropdown(
|
1518 |
+
label="2.Choose your audio.",
|
1519 |
+
value="./audios/someguy.mp3",
|
1520 |
+
choices=audio_files
|
1521 |
+
)
|
1522 |
+
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
|
1523 |
+
dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
|
1524 |
+
refresh_button2 = gr.Button("Refresh", variant="primary", size='sm')
|
1525 |
+
record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
|
1526 |
+
record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
|
1527 |
+
with gr.Row():
|
1528 |
+
with gr.Accordion('Text To Speech', open=False):
|
1529 |
+
with gr.Column():
|
1530 |
+
lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en')
|
1531 |
+
api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='')
|
1532 |
+
elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices)
|
1533 |
+
with gr.Column():
|
1534 |
+
tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.")
|
1535 |
+
tts_button = gr.Button(value="Speak")
|
1536 |
+
tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0])
|
1537 |
+
with gr.Row():
|
1538 |
+
with gr.Accordion('Wav2Lip', open=False):
|
1539 |
+
with gr.Row():
|
1540 |
+
size = gr.Radio(label='Resolution:',choices=['Half','Full'])
|
1541 |
+
face = gr.UploadButton("Upload A Character",type='file')
|
1542 |
+
faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate'])
|
1543 |
+
with gr.Row():
|
1544 |
+
preview = gr.Textbox(label="Status:",interactive=False)
|
1545 |
+
face.upload(fn=success_message,inputs=[face], outputs=[preview, faces])
|
1546 |
+
with gr.Row():
|
1547 |
+
animation = gr.Video(type='filepath')
|
1548 |
+
refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation])
|
1549 |
+
with gr.Row():
|
1550 |
+
animate_button = gr.Button('Animate')
|
1551 |
+
|
1552 |
+
with gr.Column():
|
1553 |
+
with gr.Accordion("Index Settings", open=False):
|
1554 |
+
file_index1 = gr.Dropdown(
|
1555 |
+
label="3. Path to your added.index file (if it didn't automatically find it.)",
|
1556 |
+
choices=get_indexes(),
|
1557 |
+
value=get_index(),
|
1558 |
+
interactive=True,
|
1559 |
+
)
|
1560 |
+
sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
|
1561 |
+
refresh_button.click(
|
1562 |
+
fn=change_choices, inputs=[], outputs=[sid0, file_index1]
|
1563 |
+
)
|
1564 |
+
# file_big_npy1 = gr.Textbox(
|
1565 |
+
# label=i18n("特征文件路径"),
|
1566 |
+
# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
|
1567 |
+
# interactive=True,
|
1568 |
+
# )
|
1569 |
+
index_rate1 = gr.Slider(
|
1570 |
+
minimum=0,
|
1571 |
+
maximum=1,
|
1572 |
+
label=i18n("检索特征占比"),
|
1573 |
+
value=0.66,
|
1574 |
+
interactive=True,
|
1575 |
+
)
|
1576 |
+
vc_output2 = gr.Audio(
|
1577 |
+
label="Output Audio (Click on the Three Dots in the Right Corner to Download)",
|
1578 |
+
type='filepath',
|
1579 |
+
interactive=False,
|
1580 |
+
)
|
1581 |
+
animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
|
1582 |
+
with gr.Accordion("Advanced Settings", open=False):
|
1583 |
+
f0method0 = gr.Radio(
|
1584 |
+
label="Optional: Change the Pitch Extraction Algorithm.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.",
|
1585 |
+
choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny
|
1586 |
+
value="rmvpe",
|
1587 |
+
interactive=True,
|
1588 |
+
)
|
1589 |
+
|
1590 |
+
crepe_hop_length = gr.Slider(
|
1591 |
+
minimum=1,
|
1592 |
+
maximum=512,
|
1593 |
+
step=1,
|
1594 |
+
label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
|
1595 |
+
value=120,
|
1596 |
+
interactive=True,
|
1597 |
+
visible=False,
|
1598 |
+
)
|
1599 |
+
f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
|
1600 |
+
filter_radius0 = gr.Slider(
|
1601 |
+
minimum=0,
|
1602 |
+
maximum=7,
|
1603 |
+
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"),
|
1604 |
+
value=3,
|
1605 |
+
step=1,
|
1606 |
+
interactive=True,
|
1607 |
+
)
|
1608 |
+
resample_sr0 = gr.Slider(
|
1609 |
+
minimum=0,
|
1610 |
+
maximum=48000,
|
1611 |
+
label=i18n("后处理重采样至最终采样率,0为不进行重采样"),
|
1612 |
+
value=0,
|
1613 |
+
step=1,
|
1614 |
+
interactive=True,
|
1615 |
+
visible=False
|
1616 |
+
)
|
1617 |
+
rms_mix_rate0 = gr.Slider(
|
1618 |
+
minimum=0,
|
1619 |
+
maximum=1,
|
1620 |
+
label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"),
|
1621 |
+
value=0.21,
|
1622 |
+
interactive=True,
|
1623 |
+
)
|
1624 |
+
protect0 = gr.Slider(
|
1625 |
+
minimum=0,
|
1626 |
+
maximum=0.5,
|
1627 |
+
label=i18n("保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果"),
|
1628 |
+
value=0.33,
|
1629 |
+
step=0.01,
|
1630 |
+
interactive=True,
|
1631 |
+
)
|
1632 |
+
formanting = gr.Checkbox(
|
1633 |
+
value=bool(DoFormant),
|
1634 |
+
label="[EXPERIMENTAL] Formant shift inference audio",
|
1635 |
+
info="Used for male to female and vice-versa conversions",
|
1636 |
+
interactive=True,
|
1637 |
+
visible=True,
|
1638 |
+
)
|
1639 |
+
|
1640 |
+
formant_preset = gr.Dropdown(
|
1641 |
+
value='',
|
1642 |
+
choices=get_fshift_presets(),
|
1643 |
+
label="browse presets for formanting",
|
1644 |
+
visible=bool(DoFormant),
|
1645 |
+
)
|
1646 |
+
formant_refresh_button = gr.Button(
|
1647 |
+
value='\U0001f504',
|
1648 |
+
visible=bool(DoFormant),
|
1649 |
+
variant='primary',
|
1650 |
+
)
|
1651 |
+
#formant_refresh_button = ToolButton( elem_id='1')
|
1652 |
+
#create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets")
|
1653 |
+
|
1654 |
+
qfrency = gr.Slider(
|
1655 |
+
value=Quefrency,
|
1656 |
+
info="Default value is 1.0",
|
1657 |
+
label="Quefrency for formant shifting",
|
1658 |
+
minimum=0.0,
|
1659 |
+
maximum=16.0,
|
1660 |
+
step=0.1,
|
1661 |
+
visible=bool(DoFormant),
|
1662 |
+
interactive=True,
|
1663 |
+
)
|
1664 |
+
tmbre = gr.Slider(
|
1665 |
+
value=Timbre,
|
1666 |
+
info="Default value is 1.0",
|
1667 |
+
label="Timbre for formant shifting",
|
1668 |
+
minimum=0.0,
|
1669 |
+
maximum=16.0,
|
1670 |
+
step=0.1,
|
1671 |
+
visible=bool(DoFormant),
|
1672 |
+
interactive=True,
|
1673 |
+
)
|
1674 |
+
|
1675 |
+
formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre])
|
1676 |
+
frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant))
|
1677 |
+
formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button])
|
1678 |
+
frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre])
|
1679 |
+
formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre])
|
1680 |
+
with gr.Row():
|
1681 |
+
vc_output1 = gr.Textbox("")
|
1682 |
+
f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False)
|
1683 |
+
|
1684 |
+
but0.click(
|
1685 |
+
vc_single,
|
1686 |
+
[
|
1687 |
+
spk_item,
|
1688 |
+
input_audio0,
|
1689 |
+
vc_transform0,
|
1690 |
+
f0_file,
|
1691 |
+
f0method0,
|
1692 |
+
file_index1,
|
1693 |
+
# file_index2,
|
1694 |
+
# file_big_npy1,
|
1695 |
+
index_rate1,
|
1696 |
+
filter_radius0,
|
1697 |
+
resample_sr0,
|
1698 |
+
rms_mix_rate0,
|
1699 |
+
protect0,
|
1700 |
+
crepe_hop_length
|
1701 |
+
],
|
1702 |
+
[vc_output1, vc_output2],
|
1703 |
+
)
|
1704 |
+
|
1705 |
+
with gr.Accordion("Batch Conversion",open=False):
|
1706 |
+
with gr.Row():
|
1707 |
+
with gr.Column():
|
1708 |
+
vc_transform1 = gr.Number(
|
1709 |
+
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
|
1710 |
+
)
|
1711 |
+
opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
|
1712 |
+
f0method1 = gr.Radio(
|
1713 |
+
label=i18n(
|
1714 |
+
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
|
1715 |
+
),
|
1716 |
+
choices=["pm", "harvest", "crepe", "rmvpe"],
|
1717 |
+
value="rmvpe",
|
1718 |
+
interactive=True,
|
1719 |
+
)
|
1720 |
+
filter_radius1 = gr.Slider(
|
1721 |
+
minimum=0,
|
1722 |
+
maximum=7,
|
1723 |
+
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"),
|
1724 |
+
value=3,
|
1725 |
+
step=1,
|
1726 |
+
interactive=True,
|
1727 |
+
)
|
1728 |
+
with gr.Column():
|
1729 |
+
file_index3 = gr.Textbox(
|
1730 |
+
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
|
1731 |
+
value="",
|
1732 |
+
interactive=True,
|
1733 |
+
)
|
1734 |
+
file_index4 = gr.Dropdown(
|
1735 |
+
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
|
1736 |
+
choices=sorted(index_paths),
|
1737 |
+
interactive=True,
|
1738 |
+
)
|
1739 |
+
refresh_button.click(
|
1740 |
+
fn=lambda: change_choices()[1],
|
1741 |
+
inputs=[],
|
1742 |
+
outputs=file_index4,
|
1743 |
+
)
|
1744 |
+
# file_big_npy2 = gr.Textbox(
|
1745 |
+
# label=i18n("特征文件路径"),
|
1746 |
+
# value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
|
1747 |
+
# interactive=True,
|
1748 |
+
# )
|
1749 |
+
index_rate2 = gr.Slider(
|
1750 |
+
minimum=0,
|
1751 |
+
maximum=1,
|
1752 |
+
label=i18n("检索特征占比"),
|
1753 |
+
value=1,
|
1754 |
+
interactive=True,
|
1755 |
+
)
|
1756 |
+
with gr.Column():
|
1757 |
+
resample_sr1 = gr.Slider(
|
1758 |
+
minimum=0,
|
1759 |
+
maximum=48000,
|
1760 |
+
label=i18n("后处理重采样至最终采样率,0为不进行重采样"),
|
1761 |
+
value=0,
|
1762 |
+
step=1,
|
1763 |
+
interactive=True,
|
1764 |
+
)
|
1765 |
+
rms_mix_rate1 = gr.Slider(
|
1766 |
+
minimum=0,
|
1767 |
+
maximum=1,
|
1768 |
+
label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"),
|
1769 |
+
value=1,
|
1770 |
+
interactive=True,
|
1771 |
+
)
|
1772 |
+
protect1 = gr.Slider(
|
1773 |
+
minimum=0,
|
1774 |
+
maximum=0.5,
|
1775 |
+
label=i18n(
|
1776 |
+
"保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果"
|
1777 |
+
),
|
1778 |
+
value=0.33,
|
1779 |
+
step=0.01,
|
1780 |
+
interactive=True,
|
1781 |
+
)
|
1782 |
+
with gr.Column():
|
1783 |
+
dir_input = gr.Textbox(
|
1784 |
+
label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
|
1785 |
+
value="E:\codes\py39\\test-20230416b\\todo-songs",
|
1786 |
+
)
|
1787 |
+
inputs = gr.File(
|
1788 |
+
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
|
1789 |
+
)
|
1790 |
+
with gr.Row():
|
1791 |
+
format1 = gr.Radio(
|
1792 |
+
label=i18n("导出文件格式"),
|
1793 |
+
choices=["wav", "flac", "mp3", "m4a"],
|
1794 |
+
value="flac",
|
1795 |
+
interactive=True,
|
1796 |
+
)
|
1797 |
+
but1 = gr.Button(i18n("转换"), variant="primary")
|
1798 |
+
vc_output3 = gr.Textbox(label=i18n("输出信息"))
|
1799 |
+
but1.click(
|
1800 |
+
vc_multi,
|
1801 |
+
[
|
1802 |
+
spk_item,
|
1803 |
+
dir_input,
|
1804 |
+
opt_input,
|
1805 |
+
inputs,
|
1806 |
+
vc_transform1,
|
1807 |
+
f0method1,
|
1808 |
+
file_index3,
|
1809 |
+
file_index4,
|
1810 |
+
# file_big_npy2,
|
1811 |
+
index_rate2,
|
1812 |
+
filter_radius1,
|
1813 |
+
resample_sr1,
|
1814 |
+
rms_mix_rate1,
|
1815 |
+
protect1,
|
1816 |
+
format1,
|
1817 |
+
crepe_hop_length,
|
1818 |
+
],
|
1819 |
+
[vc_output3],
|
1820 |
+
)
|
1821 |
+
but1.click(fn=lambda: easy_uploader.clear())
|
1822 |
+
with gr.TabItem("Download Model"):
|
1823 |
+
with gr.Row():
|
1824 |
+
url=gr.Textbox(label="Enter the URL to the Model:")
|
1825 |
+
with gr.Row():
|
1826 |
+
model = gr.Textbox(label="Name your model:")
|
1827 |
+
download_button=gr.Button("Download")
|
1828 |
+
with gr.Row():
|
1829 |
+
status_bar=gr.Textbox(label="")
|
1830 |
+
download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
|
1831 |
+
with gr.Row():
|
1832 |
+
gr.Markdown(
|
1833 |
+
"""
|
1834 |
+
Made with ❤️ by [Alice Oliveira](https://github.com/aliceoq) | Hosted with ❤️ by [Mateus Elias](https://github.com/mateuseap)
|
1835 |
+
"""
|
1836 |
+
)
|
1837 |
+
|
1838 |
+
def has_two_files_in_pretrained_folder():
|
1839 |
+
pretrained_folder = "./pretrained/"
|
1840 |
+
if not os.path.exists(pretrained_folder):
|
1841 |
+
return False
|
1842 |
+
|
1843 |
+
files_in_folder = os.listdir(pretrained_folder)
|
1844 |
+
num_files = len(files_in_folder)
|
1845 |
+
return num_files >= 2
|
1846 |
+
|
1847 |
+
if has_two_files_in_pretrained_folder():
|
1848 |
+
print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------")
|
1849 |
+
with gr.TabItem("Train", visible=False):
|
1850 |
+
with gr.Row():
|
1851 |
+
with gr.Column():
|
1852 |
+
exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice")
|
1853 |
+
sr2 = gr.Radio(
|
1854 |
+
label=i18n("目标采样率"),
|
1855 |
+
choices=["40k", "48k"],
|
1856 |
+
value="40k",
|
1857 |
+
interactive=True,
|
1858 |
+
visible=False
|
1859 |
+
)
|
1860 |
+
if_f0_3 = gr.Radio(
|
1861 |
+
label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
|
1862 |
+
choices=[True, False],
|
1863 |
+
value=True,
|
1864 |
+
interactive=True,
|
1865 |
+
visible=False
|
1866 |
+
)
|
1867 |
+
version19 = gr.Radio(
|
1868 |
+
label="RVC version",
|
1869 |
+
choices=["v1", "v2"],
|
1870 |
+
value="v2",
|
1871 |
+
interactive=True,
|
1872 |
+
visible=False,
|
1873 |
+
)
|
1874 |
+
np7 = gr.Slider(
|
1875 |
+
minimum=0,
|
1876 |
+
maximum=config.n_cpu,
|
1877 |
+
step=1,
|
1878 |
+
label="# of CPUs for data processing (Leave as it is)",
|
1879 |
+
value=config.n_cpu,
|
1880 |
+
interactive=True,
|
1881 |
+
visible=True
|
1882 |
+
)
|
1883 |
+
trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset")
|
1884 |
+
easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio'])
|
1885 |
+
but1 = gr.Button("1. Process The Dataset", variant="primary")
|
1886 |
+
info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="")
|
1887 |
+
easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1])
|
1888 |
+
but1.click(
|
1889 |
+
preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
|
1890 |
+
)
|
1891 |
+
with gr.Column():
|
1892 |
+
spk_id5 = gr.Slider(
|
1893 |
+
minimum=0,
|
1894 |
+
maximum=4,
|
1895 |
+
step=1,
|
1896 |
+
label=i18n("请指定说话人id"),
|
1897 |
+
value=0,
|
1898 |
+
interactive=True,
|
1899 |
+
visible=False
|
1900 |
+
)
|
1901 |
+
with gr.Accordion('GPU Settings', open=False, visible=False):
|
1902 |
+
gpus6 = gr.Textbox(
|
1903 |
+
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"),
|
1904 |
+
value=gpus,
|
1905 |
+
interactive=True,
|
1906 |
+
visible=False
|
1907 |
+
)
|
1908 |
+
gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
|
1909 |
+
f0method8 = gr.Radio(
|
1910 |
+
label=i18n(
|
1911 |
+
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
|
1912 |
+
),
|
1913 |
+
choices=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training.
|
1914 |
+
value="rmvpe",
|
1915 |
+
interactive=True,
|
1916 |
+
)
|
1917 |
+
|
1918 |
+
extraction_crepe_hop_length = gr.Slider(
|
1919 |
+
minimum=1,
|
1920 |
+
maximum=512,
|
1921 |
+
step=1,
|
1922 |
+
label=i18n("crepe_hop_length"),
|
1923 |
+
value=128,
|
1924 |
+
interactive=True,
|
1925 |
+
visible=False,
|
1926 |
+
)
|
1927 |
+
f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length])
|
1928 |
+
but2 = gr.Button("2. Pitch Extraction", variant="primary")
|
1929 |
+
info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8)
|
1930 |
+
but2.click(
|
1931 |
+
extract_f0_feature,
|
1932 |
+
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length],
|
1933 |
+
[info2],
|
1934 |
+
)
|
1935 |
+
with gr.Row():
|
1936 |
+
with gr.Column():
|
1937 |
+
total_epoch11 = gr.Slider(
|
1938 |
+
minimum=1,
|
1939 |
+
maximum=5000,
|
1940 |
+
step=10,
|
1941 |
+
label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):",
|
1942 |
+
value=250,
|
1943 |
+
interactive=True,
|
1944 |
+
)
|
1945 |
+
butstop = gr.Button(
|
1946 |
+
"Stop Training",
|
1947 |
+
variant='primary',
|
1948 |
+
visible=False,
|
1949 |
+
)
|
1950 |
+
but3 = gr.Button("3. Train Model", variant="primary", visible=True)
|
1951 |
+
|
1952 |
+
but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
|
1953 |
+
butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3])
|
1954 |
+
|
1955 |
+
|
1956 |
+
but4 = gr.Button("4.Train Index", variant="primary")
|
1957 |
+
info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10)
|
1958 |
+
with gr.Accordion("Training Preferences (You can leave these as they are)", open=False):
|
1959 |
+
#gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
|
1960 |
+
with gr.Column():
|
1961 |
+
save_epoch10 = gr.Slider(
|
1962 |
+
minimum=1,
|
1963 |
+
maximum=200,
|
1964 |
+
step=1,
|
1965 |
+
label="Backup every X amount of epochs:",
|
1966 |
+
value=10,
|
1967 |
+
interactive=True,
|
1968 |
+
)
|
1969 |
+
batch_size12 = gr.Slider(
|
1970 |
+
minimum=1,
|
1971 |
+
maximum=40,
|
1972 |
+
step=1,
|
1973 |
+
label="Batch Size (LEAVE IT unless you know what you're doing!):",
|
1974 |
+
value=default_batch_size,
|
1975 |
+
interactive=True,
|
1976 |
+
)
|
1977 |
+
if_save_latest13 = gr.Checkbox(
|
1978 |
+
label="Save only the latest '.ckpt' file to save disk space.",
|
1979 |
+
value=True,
|
1980 |
+
interactive=True,
|
1981 |
+
)
|
1982 |
+
if_cache_gpu17 = gr.Checkbox(
|
1983 |
+
label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.",
|
1984 |
+
value=False,
|
1985 |
+
interactive=True,
|
1986 |
+
)
|
1987 |
+
if_save_every_weights18 = gr.Checkbox(
|
1988 |
+
label="Save a small final model to the 'weights' folder at each save point.",
|
1989 |
+
value=True,
|
1990 |
+
interactive=True,
|
1991 |
+
)
|
1992 |
+
zip_model = gr.Button('5. Download Model')
|
1993 |
+
zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:')
|
1994 |
+
zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3])
|
1995 |
+
with gr.Group():
|
1996 |
+
with gr.Accordion("Base Model Locations:", open=False, visible=False):
|
1997 |
+
pretrained_G14 = gr.Textbox(
|
1998 |
+
label=i18n("加载预训练底模G路径"),
|
1999 |
+
value="pretrained_v2/f0G40k.pth",
|
2000 |
+
interactive=True,
|
2001 |
+
)
|
2002 |
+
pretrained_D15 = gr.Textbox(
|
2003 |
+
label=i18n("加载预训练底模D路径"),
|
2004 |
+
value="pretrained_v2/f0D40k.pth",
|
2005 |
+
interactive=True,
|
2006 |
+
)
|
2007 |
+
gpus16 = gr.Textbox(
|
2008 |
+
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"),
|
2009 |
+
value=gpus,
|
2010 |
+
interactive=True,
|
2011 |
+
)
|
2012 |
+
sr2.change(
|
2013 |
+
change_sr2,
|
2014 |
+
[sr2, if_f0_3, version19],
|
2015 |
+
[pretrained_G14, pretrained_D15, version19],
|
2016 |
+
)
|
2017 |
+
version19.change(
|
2018 |
+
change_version19,
|
2019 |
+
[sr2, if_f0_3, version19],
|
2020 |
+
[pretrained_G14, pretrained_D15],
|
2021 |
+
)
|
2022 |
+
if_f0_3.change(
|
2023 |
+
change_f0,
|
2024 |
+
[if_f0_3, sr2, version19],
|
2025 |
+
[f0method8, pretrained_G14, pretrained_D15],
|
2026 |
+
)
|
2027 |
+
but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False)
|
2028 |
+
but3.click(
|
2029 |
+
click_train,
|
2030 |
+
[
|
2031 |
+
exp_dir1,
|
2032 |
+
sr2,
|
2033 |
+
if_f0_3,
|
2034 |
+
spk_id5,
|
2035 |
+
save_epoch10,
|
2036 |
+
total_epoch11,
|
2037 |
+
batch_size12,
|
2038 |
+
if_save_latest13,
|
2039 |
+
pretrained_G14,
|
2040 |
+
pretrained_D15,
|
2041 |
+
gpus16,
|
2042 |
+
if_cache_gpu17,
|
2043 |
+
if_save_every_weights18,
|
2044 |
+
version19,
|
2045 |
+
],
|
2046 |
+
[
|
2047 |
+
info3,
|
2048 |
+
butstop,
|
2049 |
+
but3,
|
2050 |
+
],
|
2051 |
+
)
|
2052 |
+
but4.click(train_index, [exp_dir1, version19], info3)
|
2053 |
+
but5.click(
|
2054 |
+
train1key,
|
2055 |
+
[
|
2056 |
+
exp_dir1,
|
2057 |
+
sr2,
|
2058 |
+
if_f0_3,
|
2059 |
+
trainset_dir4,
|
2060 |
+
spk_id5,
|
2061 |
+
np7,
|
2062 |
+
f0method8,
|
2063 |
+
save_epoch10,
|
2064 |
+
total_epoch11,
|
2065 |
+
batch_size12,
|
2066 |
+
if_save_latest13,
|
2067 |
+
pretrained_G14,
|
2068 |
+
pretrained_D15,
|
2069 |
+
gpus16,
|
2070 |
+
if_cache_gpu17,
|
2071 |
+
if_save_every_weights18,
|
2072 |
+
version19,
|
2073 |
+
extraction_crepe_hop_length
|
2074 |
+
],
|
2075 |
+
info3,
|
2076 |
+
)
|
2077 |
+
|
2078 |
+
else:
|
2079 |
+
print(
|
2080 |
+
"Pretrained weights not downloaded. Disabling training tab.\n"
|
2081 |
+
"Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n"
|
2082 |
+
"-------------------------------\n"
|
2083 |
+
)
|
2084 |
+
|
2085 |
+
app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True)
|
2086 |
+
#endregion
|
config.py
ADDED
@@ -0,0 +1,204 @@
|
|
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|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import sys
|
3 |
+
import torch
|
4 |
+
import json
|
5 |
+
from multiprocessing import cpu_count
|
6 |
+
|
7 |
+
global usefp16
|
8 |
+
usefp16 = False
|
9 |
+
|
10 |
+
|
11 |
+
def use_fp32_config():
|
12 |
+
usefp16 = False
|
13 |
+
device_capability = 0
|
14 |
+
if torch.cuda.is_available():
|
15 |
+
device = torch.device("cuda:0") # Assuming you have only one GPU (index 0).
|
16 |
+
device_capability = torch.cuda.get_device_capability(device)[0]
|
17 |
+
if device_capability >= 7:
|
18 |
+
usefp16 = True
|
19 |
+
for config_file in ["32k.json", "40k.json", "48k.json"]:
|
20 |
+
with open(f"configs/{config_file}", "r") as d:
|
21 |
+
data = json.load(d)
|
22 |
+
|
23 |
+
if "train" in data and "fp16_run" in data["train"]:
|
24 |
+
data["train"]["fp16_run"] = True
|
25 |
+
|
26 |
+
with open(f"configs/{config_file}", "w") as d:
|
27 |
+
json.dump(data, d, indent=4)
|
28 |
+
|
29 |
+
print(f"Set fp16_run to true in {config_file}")
|
30 |
+
|
31 |
+
with open(
|
32 |
+
"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
|
33 |
+
) as f:
|
34 |
+
strr = f.read()
|
35 |
+
|
36 |
+
strr = strr.replace("3.0", "3.7")
|
37 |
+
|
38 |
+
with open(
|
39 |
+
"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
|
40 |
+
) as f:
|
41 |
+
f.write(strr)
|
42 |
+
else:
|
43 |
+
for config_file in ["32k.json", "40k.json", "48k.json"]:
|
44 |
+
with open(f"configs/{config_file}", "r") as f:
|
45 |
+
data = json.load(f)
|
46 |
+
|
47 |
+
if "train" in data and "fp16_run" in data["train"]:
|
48 |
+
data["train"]["fp16_run"] = False
|
49 |
+
|
50 |
+
with open(f"configs/{config_file}", "w") as d:
|
51 |
+
json.dump(data, d, indent=4)
|
52 |
+
|
53 |
+
print(f"Set fp16_run to false in {config_file}")
|
54 |
+
|
55 |
+
with open(
|
56 |
+
"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
|
57 |
+
) as f:
|
58 |
+
strr = f.read()
|
59 |
+
|
60 |
+
strr = strr.replace("3.7", "3.0")
|
61 |
+
|
62 |
+
with open(
|
63 |
+
"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
|
64 |
+
) as f:
|
65 |
+
f.write(strr)
|
66 |
+
else:
|
67 |
+
print(
|
68 |
+
"CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed."
|
69 |
+
)
|
70 |
+
return (usefp16, device_capability)
|
71 |
+
|
72 |
+
|
73 |
+
class Config:
|
74 |
+
def __init__(self):
|
75 |
+
self.device = "cuda:0"
|
76 |
+
self.is_half = True
|
77 |
+
self.n_cpu = 0
|
78 |
+
self.gpu_name = None
|
79 |
+
self.gpu_mem = None
|
80 |
+
(
|
81 |
+
self.python_cmd,
|
82 |
+
self.listen_port,
|
83 |
+
self.iscolab,
|
84 |
+
self.noparallel,
|
85 |
+
self.noautoopen,
|
86 |
+
self.paperspace,
|
87 |
+
self.is_cli,
|
88 |
+
) = self.arg_parse()
|
89 |
+
|
90 |
+
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
|
91 |
+
|
92 |
+
@staticmethod
|
93 |
+
def arg_parse() -> tuple:
|
94 |
+
exe = sys.executable or "python"
|
95 |
+
parser = argparse.ArgumentParser()
|
96 |
+
parser.add_argument("--port", type=int, default=7865, help="Listen port")
|
97 |
+
parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
|
98 |
+
parser.add_argument("--colab", action="store_true", help="Launch in colab")
|
99 |
+
parser.add_argument(
|
100 |
+
"--noparallel", action="store_true", help="Disable parallel processing"
|
101 |
+
)
|
102 |
+
parser.add_argument(
|
103 |
+
"--noautoopen",
|
104 |
+
action="store_true",
|
105 |
+
help="Do not open in browser automatically",
|
106 |
+
)
|
107 |
+
parser.add_argument( # Fork Feature. Paperspace integration for web UI
|
108 |
+
"--paperspace",
|
109 |
+
action="store_true",
|
110 |
+
help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
|
111 |
+
)
|
112 |
+
parser.add_argument( # Fork Feature. Embed a CLI into the infer-web.py
|
113 |
+
"--is_cli",
|
114 |
+
action="store_true",
|
115 |
+
help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
|
116 |
+
)
|
117 |
+
cmd_opts = parser.parse_args()
|
118 |
+
|
119 |
+
cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
|
120 |
+
|
121 |
+
return (
|
122 |
+
cmd_opts.pycmd,
|
123 |
+
cmd_opts.port,
|
124 |
+
cmd_opts.colab,
|
125 |
+
cmd_opts.noparallel,
|
126 |
+
cmd_opts.noautoopen,
|
127 |
+
cmd_opts.paperspace,
|
128 |
+
cmd_opts.is_cli,
|
129 |
+
)
|
130 |
+
|
131 |
+
# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
|
132 |
+
# check `getattr` and try it for compatibility
|
133 |
+
@staticmethod
|
134 |
+
def has_mps() -> bool:
|
135 |
+
if not torch.backends.mps.is_available():
|
136 |
+
return False
|
137 |
+
try:
|
138 |
+
torch.zeros(1).to(torch.device("mps"))
|
139 |
+
return True
|
140 |
+
except Exception:
|
141 |
+
return False
|
142 |
+
|
143 |
+
def device_config(self) -> tuple:
|
144 |
+
if torch.cuda.is_available():
|
145 |
+
i_device = int(self.device.split(":")[-1])
|
146 |
+
self.gpu_name = torch.cuda.get_device_name(i_device)
|
147 |
+
if (
|
148 |
+
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
|
149 |
+
or "P40" in self.gpu_name.upper()
|
150 |
+
or "1060" in self.gpu_name
|
151 |
+
or "1070" in self.gpu_name
|
152 |
+
or "1080" in self.gpu_name
|
153 |
+
):
|
154 |
+
print("Found GPU", self.gpu_name, ", force to fp32")
|
155 |
+
self.is_half = False
|
156 |
+
else:
|
157 |
+
print("Found GPU", self.gpu_name)
|
158 |
+
use_fp32_config()
|
159 |
+
self.gpu_mem = int(
|
160 |
+
torch.cuda.get_device_properties(i_device).total_memory
|
161 |
+
/ 1024
|
162 |
+
/ 1024
|
163 |
+
/ 1024
|
164 |
+
+ 0.4
|
165 |
+
)
|
166 |
+
if self.gpu_mem <= 4:
|
167 |
+
with open("trainset_preprocess_pipeline_print.py", "r") as f:
|
168 |
+
strr = f.read().replace("3.7", "3.0")
|
169 |
+
with open("trainset_preprocess_pipeline_print.py", "w") as f:
|
170 |
+
f.write(strr)
|
171 |
+
elif self.has_mps():
|
172 |
+
print("No supported Nvidia GPU found, use MPS instead")
|
173 |
+
self.device = "mps"
|
174 |
+
self.is_half = False
|
175 |
+
use_fp32_config()
|
176 |
+
else:
|
177 |
+
print("No supported Nvidia GPU found, use CPU instead")
|
178 |
+
self.device = "cpu"
|
179 |
+
self.is_half = False
|
180 |
+
use_fp32_config()
|
181 |
+
|
182 |
+
if self.n_cpu == 0:
|
183 |
+
self.n_cpu = cpu_count()
|
184 |
+
|
185 |
+
if self.is_half:
|
186 |
+
# 6G显存配置
|
187 |
+
x_pad = 3
|
188 |
+
x_query = 10
|
189 |
+
x_center = 60
|
190 |
+
x_max = 65
|
191 |
+
else:
|
192 |
+
# 5G显存配置
|
193 |
+
x_pad = 1
|
194 |
+
x_query = 6
|
195 |
+
x_center = 38
|
196 |
+
x_max = 41
|
197 |
+
|
198 |
+
if self.gpu_mem != None and self.gpu_mem <= 4:
|
199 |
+
x_pad = 1
|
200 |
+
x_query = 5
|
201 |
+
x_center = 30
|
202 |
+
x_max = 32
|
203 |
+
|
204 |
+
return x_pad, x_query, x_center, x_max
|
gitattributes.txt
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
gitignore.txt
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
__pycache__/
|
2 |
+
weights/
|
3 |
+
TEMP/
|
4 |
+
logs/
|
5 |
+
csvdb/
|
6 |
+
|
7 |
+
# Environment
|
8 |
+
venv/
|
9 |
+
|
10 |
+
# Models
|
11 |
+
hubert_base.pt
|
12 |
+
rmvpe.pt
|
i18n.py
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import locale
|
2 |
+
import json
|
3 |
+
import os
|
4 |
+
|
5 |
+
|
6 |
+
def load_language_list(language):
|
7 |
+
with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
|
8 |
+
language_list = json.load(f)
|
9 |
+
return language_list
|
10 |
+
|
11 |
+
|
12 |
+
class I18nAuto:
|
13 |
+
def __init__(self, language=None):
|
14 |
+
if language in ["Auto", None]:
|
15 |
+
language = locale.getdefaultlocale()[
|
16 |
+
0
|
17 |
+
] # getlocale can't identify the system's language ((None, None))
|
18 |
+
if not os.path.exists(f"./i18n/{language}.json"):
|
19 |
+
language = "en_US"
|
20 |
+
self.language = language
|
21 |
+
# print("Use Language:", language)
|
22 |
+
self.language_map = load_language_list(language)
|
23 |
+
|
24 |
+
def __call__(self, key):
|
25 |
+
return self.language_map.get(key, key)
|
26 |
+
|
27 |
+
def print(self):
|
28 |
+
print("Use Language:", self.language)
|
packages.txt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
build-essential
|
2 |
+
ffmpeg
|
3 |
+
aria2
|
requirements.txt
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gTTS
|
2 |
+
elevenlabs
|
3 |
+
stftpitchshift==1.5.1
|
4 |
+
torchcrepe
|
5 |
+
setuptools
|
6 |
+
wheel
|
7 |
+
httpx==0.23.0
|
8 |
+
faiss-gpu
|
9 |
+
fairseq
|
10 |
+
gradio==3.34.0
|
11 |
+
ffmpeg-python
|
12 |
+
praat-parselmouth
|
13 |
+
pyworld
|
14 |
+
numpy==1.23.5
|
15 |
+
i18n
|
16 |
+
numba==0.56.4
|
17 |
+
librosa==0.9.2
|
18 |
+
mega.py
|
19 |
+
gdown
|
20 |
+
onnxruntime
|
21 |
+
pyngrok==4.1.12
|
22 |
+
torch
|
rmvpe.py
ADDED
@@ -0,0 +1,432 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import sys, torch, numpy as np, traceback, pdb
|
2 |
+
import torch.nn as nn
|
3 |
+
from time import time as ttime
|
4 |
+
import torch.nn.functional as F
|
5 |
+
|
6 |
+
|
7 |
+
class BiGRU(nn.Module):
|
8 |
+
def __init__(self, input_features, hidden_features, num_layers):
|
9 |
+
super(BiGRU, self).__init__()
|
10 |
+
self.gru = nn.GRU(
|
11 |
+
input_features,
|
12 |
+
hidden_features,
|
13 |
+
num_layers=num_layers,
|
14 |
+
batch_first=True,
|
15 |
+
bidirectional=True,
|
16 |
+
)
|
17 |
+
|
18 |
+
def forward(self, x):
|
19 |
+
return self.gru(x)[0]
|
20 |
+
|
21 |
+
|
22 |
+
class ConvBlockRes(nn.Module):
|
23 |
+
def __init__(self, in_channels, out_channels, momentum=0.01):
|
24 |
+
super(ConvBlockRes, self).__init__()
|
25 |
+
self.conv = nn.Sequential(
|
26 |
+
nn.Conv2d(
|
27 |
+
in_channels=in_channels,
|
28 |
+
out_channels=out_channels,
|
29 |
+
kernel_size=(3, 3),
|
30 |
+
stride=(1, 1),
|
31 |
+
padding=(1, 1),
|
32 |
+
bias=False,
|
33 |
+
),
|
34 |
+
nn.BatchNorm2d(out_channels, momentum=momentum),
|
35 |
+
nn.ReLU(),
|
36 |
+
nn.Conv2d(
|
37 |
+
in_channels=out_channels,
|
38 |
+
out_channels=out_channels,
|
39 |
+
kernel_size=(3, 3),
|
40 |
+
stride=(1, 1),
|
41 |
+
padding=(1, 1),
|
42 |
+
bias=False,
|
43 |
+
),
|
44 |
+
nn.BatchNorm2d(out_channels, momentum=momentum),
|
45 |
+
nn.ReLU(),
|
46 |
+
)
|
47 |
+
if in_channels != out_channels:
|
48 |
+
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
|
49 |
+
self.is_shortcut = True
|
50 |
+
else:
|
51 |
+
self.is_shortcut = False
|
52 |
+
|
53 |
+
def forward(self, x):
|
54 |
+
if self.is_shortcut:
|
55 |
+
return self.conv(x) + self.shortcut(x)
|
56 |
+
else:
|
57 |
+
return self.conv(x) + x
|
58 |
+
|
59 |
+
|
60 |
+
class Encoder(nn.Module):
|
61 |
+
def __init__(
|
62 |
+
self,
|
63 |
+
in_channels,
|
64 |
+
in_size,
|
65 |
+
n_encoders,
|
66 |
+
kernel_size,
|
67 |
+
n_blocks,
|
68 |
+
out_channels=16,
|
69 |
+
momentum=0.01,
|
70 |
+
):
|
71 |
+
super(Encoder, self).__init__()
|
72 |
+
self.n_encoders = n_encoders
|
73 |
+
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
|
74 |
+
self.layers = nn.ModuleList()
|
75 |
+
self.latent_channels = []
|
76 |
+
for i in range(self.n_encoders):
|
77 |
+
self.layers.append(
|
78 |
+
ResEncoderBlock(
|
79 |
+
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
|
80 |
+
)
|
81 |
+
)
|
82 |
+
self.latent_channels.append([out_channels, in_size])
|
83 |
+
in_channels = out_channels
|
84 |
+
out_channels *= 2
|
85 |
+
in_size //= 2
|
86 |
+
self.out_size = in_size
|
87 |
+
self.out_channel = out_channels
|
88 |
+
|
89 |
+
def forward(self, x):
|
90 |
+
concat_tensors = []
|
91 |
+
x = self.bn(x)
|
92 |
+
for i in range(self.n_encoders):
|
93 |
+
_, x = self.layers[i](x)
|
94 |
+
concat_tensors.append(_)
|
95 |
+
return x, concat_tensors
|
96 |
+
|
97 |
+
|
98 |
+
class ResEncoderBlock(nn.Module):
|
99 |
+
def __init__(
|
100 |
+
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
|
101 |
+
):
|
102 |
+
super(ResEncoderBlock, self).__init__()
|
103 |
+
self.n_blocks = n_blocks
|
104 |
+
self.conv = nn.ModuleList()
|
105 |
+
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
|
106 |
+
for i in range(n_blocks - 1):
|
107 |
+
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
|
108 |
+
self.kernel_size = kernel_size
|
109 |
+
if self.kernel_size is not None:
|
110 |
+
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
|
111 |
+
|
112 |
+
def forward(self, x):
|
113 |
+
for i in range(self.n_blocks):
|
114 |
+
x = self.conv[i](x)
|
115 |
+
if self.kernel_size is not None:
|
116 |
+
return x, self.pool(x)
|
117 |
+
else:
|
118 |
+
return x
|
119 |
+
|
120 |
+
|
121 |
+
class Intermediate(nn.Module): #
|
122 |
+
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
|
123 |
+
super(Intermediate, self).__init__()
|
124 |
+
self.n_inters = n_inters
|
125 |
+
self.layers = nn.ModuleList()
|
126 |
+
self.layers.append(
|
127 |
+
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
|
128 |
+
)
|
129 |
+
for i in range(self.n_inters - 1):
|
130 |
+
self.layers.append(
|
131 |
+
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
|
132 |
+
)
|
133 |
+
|
134 |
+
def forward(self, x):
|
135 |
+
for i in range(self.n_inters):
|
136 |
+
x = self.layers[i](x)
|
137 |
+
return x
|
138 |
+
|
139 |
+
|
140 |
+
class ResDecoderBlock(nn.Module):
|
141 |
+
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
|
142 |
+
super(ResDecoderBlock, self).__init__()
|
143 |
+
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
|
144 |
+
self.n_blocks = n_blocks
|
145 |
+
self.conv1 = nn.Sequential(
|
146 |
+
nn.ConvTranspose2d(
|
147 |
+
in_channels=in_channels,
|
148 |
+
out_channels=out_channels,
|
149 |
+
kernel_size=(3, 3),
|
150 |
+
stride=stride,
|
151 |
+
padding=(1, 1),
|
152 |
+
output_padding=out_padding,
|
153 |
+
bias=False,
|
154 |
+
),
|
155 |
+
nn.BatchNorm2d(out_channels, momentum=momentum),
|
156 |
+
nn.ReLU(),
|
157 |
+
)
|
158 |
+
self.conv2 = nn.ModuleList()
|
159 |
+
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
|
160 |
+
for i in range(n_blocks - 1):
|
161 |
+
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
|
162 |
+
|
163 |
+
def forward(self, x, concat_tensor):
|
164 |
+
x = self.conv1(x)
|
165 |
+
x = torch.cat((x, concat_tensor), dim=1)
|
166 |
+
for i in range(self.n_blocks):
|
167 |
+
x = self.conv2[i](x)
|
168 |
+
return x
|
169 |
+
|
170 |
+
|
171 |
+
class Decoder(nn.Module):
|
172 |
+
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
|
173 |
+
super(Decoder, self).__init__()
|
174 |
+
self.layers = nn.ModuleList()
|
175 |
+
self.n_decoders = n_decoders
|
176 |
+
for i in range(self.n_decoders):
|
177 |
+
out_channels = in_channels // 2
|
178 |
+
self.layers.append(
|
179 |
+
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
|
180 |
+
)
|
181 |
+
in_channels = out_channels
|
182 |
+
|
183 |
+
def forward(self, x, concat_tensors):
|
184 |
+
for i in range(self.n_decoders):
|
185 |
+
x = self.layers[i](x, concat_tensors[-1 - i])
|
186 |
+
return x
|
187 |
+
|
188 |
+
|
189 |
+
class DeepUnet(nn.Module):
|
190 |
+
def __init__(
|
191 |
+
self,
|
192 |
+
kernel_size,
|
193 |
+
n_blocks,
|
194 |
+
en_de_layers=5,
|
195 |
+
inter_layers=4,
|
196 |
+
in_channels=1,
|
197 |
+
en_out_channels=16,
|
198 |
+
):
|
199 |
+
super(DeepUnet, self).__init__()
|
200 |
+
self.encoder = Encoder(
|
201 |
+
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
|
202 |
+
)
|
203 |
+
self.intermediate = Intermediate(
|
204 |
+
self.encoder.out_channel // 2,
|
205 |
+
self.encoder.out_channel,
|
206 |
+
inter_layers,
|
207 |
+
n_blocks,
|
208 |
+
)
|
209 |
+
self.decoder = Decoder(
|
210 |
+
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
|
211 |
+
)
|
212 |
+
|
213 |
+
def forward(self, x):
|
214 |
+
x, concat_tensors = self.encoder(x)
|
215 |
+
x = self.intermediate(x)
|
216 |
+
x = self.decoder(x, concat_tensors)
|
217 |
+
return x
|
218 |
+
|
219 |
+
|
220 |
+
class E2E(nn.Module):
|
221 |
+
def __init__(
|
222 |
+
self,
|
223 |
+
n_blocks,
|
224 |
+
n_gru,
|
225 |
+
kernel_size,
|
226 |
+
en_de_layers=5,
|
227 |
+
inter_layers=4,
|
228 |
+
in_channels=1,
|
229 |
+
en_out_channels=16,
|
230 |
+
):
|
231 |
+
super(E2E, self).__init__()
|
232 |
+
self.unet = DeepUnet(
|
233 |
+
kernel_size,
|
234 |
+
n_blocks,
|
235 |
+
en_de_layers,
|
236 |
+
inter_layers,
|
237 |
+
in_channels,
|
238 |
+
en_out_channels,
|
239 |
+
)
|
240 |
+
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
|
241 |
+
if n_gru:
|
242 |
+
self.fc = nn.Sequential(
|
243 |
+
BiGRU(3 * 128, 256, n_gru),
|
244 |
+
nn.Linear(512, 360),
|
245 |
+
nn.Dropout(0.25),
|
246 |
+
nn.Sigmoid(),
|
247 |
+
)
|
248 |
+
else:
|
249 |
+
self.fc = nn.Sequential(
|
250 |
+
nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
|
251 |
+
)
|
252 |
+
|
253 |
+
def forward(self, mel):
|
254 |
+
mel = mel.transpose(-1, -2).unsqueeze(1)
|
255 |
+
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
|
256 |
+
x = self.fc(x)
|
257 |
+
return x
|
258 |
+
|
259 |
+
|
260 |
+
from librosa.filters import mel
|
261 |
+
|
262 |
+
|
263 |
+
class MelSpectrogram(torch.nn.Module):
|
264 |
+
def __init__(
|
265 |
+
self,
|
266 |
+
is_half,
|
267 |
+
n_mel_channels,
|
268 |
+
sampling_rate,
|
269 |
+
win_length,
|
270 |
+
hop_length,
|
271 |
+
n_fft=None,
|
272 |
+
mel_fmin=0,
|
273 |
+
mel_fmax=None,
|
274 |
+
clamp=1e-5,
|
275 |
+
):
|
276 |
+
super().__init__()
|
277 |
+
n_fft = win_length if n_fft is None else n_fft
|
278 |
+
self.hann_window = {}
|
279 |
+
mel_basis = mel(
|
280 |
+
sr=sampling_rate,
|
281 |
+
n_fft=n_fft,
|
282 |
+
n_mels=n_mel_channels,
|
283 |
+
fmin=mel_fmin,
|
284 |
+
fmax=mel_fmax,
|
285 |
+
htk=True,
|
286 |
+
)
|
287 |
+
mel_basis = torch.from_numpy(mel_basis).float()
|
288 |
+
self.register_buffer("mel_basis", mel_basis)
|
289 |
+
self.n_fft = win_length if n_fft is None else n_fft
|
290 |
+
self.hop_length = hop_length
|
291 |
+
self.win_length = win_length
|
292 |
+
self.sampling_rate = sampling_rate
|
293 |
+
self.n_mel_channels = n_mel_channels
|
294 |
+
self.clamp = clamp
|
295 |
+
self.is_half = is_half
|
296 |
+
|
297 |
+
def forward(self, audio, keyshift=0, speed=1, center=True):
|
298 |
+
factor = 2 ** (keyshift / 12)
|
299 |
+
n_fft_new = int(np.round(self.n_fft * factor))
|
300 |
+
win_length_new = int(np.round(self.win_length * factor))
|
301 |
+
hop_length_new = int(np.round(self.hop_length * speed))
|
302 |
+
keyshift_key = str(keyshift) + "_" + str(audio.device)
|
303 |
+
if keyshift_key not in self.hann_window:
|
304 |
+
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
|
305 |
+
audio.device
|
306 |
+
)
|
307 |
+
fft = torch.stft(
|
308 |
+
audio,
|
309 |
+
n_fft=n_fft_new,
|
310 |
+
hop_length=hop_length_new,
|
311 |
+
win_length=win_length_new,
|
312 |
+
window=self.hann_window[keyshift_key],
|
313 |
+
center=center,
|
314 |
+
return_complex=True,
|
315 |
+
)
|
316 |
+
magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
|
317 |
+
if keyshift != 0:
|
318 |
+
size = self.n_fft // 2 + 1
|
319 |
+
resize = magnitude.size(1)
|
320 |
+
if resize < size:
|
321 |
+
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
|
322 |
+
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
|
323 |
+
mel_output = torch.matmul(self.mel_basis, magnitude)
|
324 |
+
if self.is_half == True:
|
325 |
+
mel_output = mel_output.half()
|
326 |
+
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
|
327 |
+
return log_mel_spec
|
328 |
+
|
329 |
+
|
330 |
+
class RMVPE:
|
331 |
+
def __init__(self, model_path, is_half, device=None):
|
332 |
+
self.resample_kernel = {}
|
333 |
+
model = E2E(4, 1, (2, 2))
|
334 |
+
ckpt = torch.load(model_path, map_location="cpu")
|
335 |
+
model.load_state_dict(ckpt)
|
336 |
+
model.eval()
|
337 |
+
if is_half == True:
|
338 |
+
model = model.half()
|
339 |
+
self.model = model
|
340 |
+
self.resample_kernel = {}
|
341 |
+
self.is_half = is_half
|
342 |
+
if device is None:
|
343 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
344 |
+
self.device = device
|
345 |
+
self.mel_extractor = MelSpectrogram(
|
346 |
+
is_half, 128, 16000, 1024, 160, None, 30, 8000
|
347 |
+
).to(device)
|
348 |
+
self.model = self.model.to(device)
|
349 |
+
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
|
350 |
+
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
|
351 |
+
|
352 |
+
def mel2hidden(self, mel):
|
353 |
+
with torch.no_grad():
|
354 |
+
n_frames = mel.shape[-1]
|
355 |
+
mel = F.pad(
|
356 |
+
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
|
357 |
+
)
|
358 |
+
hidden = self.model(mel)
|
359 |
+
return hidden[:, :n_frames]
|
360 |
+
|
361 |
+
def decode(self, hidden, thred=0.03):
|
362 |
+
cents_pred = self.to_local_average_cents(hidden, thred=thred)
|
363 |
+
f0 = 10 * (2 ** (cents_pred / 1200))
|
364 |
+
f0[f0 == 10] = 0
|
365 |
+
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
|
366 |
+
return f0
|
367 |
+
|
368 |
+
def infer_from_audio(self, audio, thred=0.03):
|
369 |
+
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
|
370 |
+
# torch.cuda.synchronize()
|
371 |
+
# t0=ttime()
|
372 |
+
mel = self.mel_extractor(audio, center=True)
|
373 |
+
# torch.cuda.synchronize()
|
374 |
+
# t1=ttime()
|
375 |
+
hidden = self.mel2hidden(mel)
|
376 |
+
# torch.cuda.synchronize()
|
377 |
+
# t2=ttime()
|
378 |
+
hidden = hidden.squeeze(0).cpu().numpy()
|
379 |
+
if self.is_half == True:
|
380 |
+
hidden = hidden.astype("float32")
|
381 |
+
f0 = self.decode(hidden, thred=thred)
|
382 |
+
# torch.cuda.synchronize()
|
383 |
+
# t3=ttime()
|
384 |
+
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
|
385 |
+
return f0
|
386 |
+
|
387 |
+
def to_local_average_cents(self, salience, thred=0.05):
|
388 |
+
# t0 = ttime()
|
389 |
+
center = np.argmax(salience, axis=1) # 帧长#index
|
390 |
+
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
|
391 |
+
# t1 = ttime()
|
392 |
+
center += 4
|
393 |
+
todo_salience = []
|
394 |
+
todo_cents_mapping = []
|
395 |
+
starts = center - 4
|
396 |
+
ends = center + 5
|
397 |
+
for idx in range(salience.shape[0]):
|
398 |
+
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
|
399 |
+
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
|
400 |
+
# t2 = ttime()
|
401 |
+
todo_salience = np.array(todo_salience) # 帧长,9
|
402 |
+
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长,9
|
403 |
+
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
|
404 |
+
weight_sum = np.sum(todo_salience, 1) # 帧长
|
405 |
+
devided = product_sum / weight_sum # 帧长
|
406 |
+
# t3 = ttime()
|
407 |
+
maxx = np.max(salience, axis=1) # 帧长
|
408 |
+
devided[maxx <= thred] = 0
|
409 |
+
# t4 = ttime()
|
410 |
+
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
411 |
+
return devided
|
412 |
+
|
413 |
+
|
414 |
+
# if __name__ == '__main__':
|
415 |
+
# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
|
416 |
+
# if len(audio.shape) > 1:
|
417 |
+
# audio = librosa.to_mono(audio.transpose(1, 0))
|
418 |
+
# audio_bak = audio.copy()
|
419 |
+
# if sampling_rate != 16000:
|
420 |
+
# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
421 |
+
# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
|
422 |
+
# thred = 0.03 # 0.01
|
423 |
+
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
424 |
+
# rmvpe = RMVPE(model_path,is_half=False, device=device)
|
425 |
+
# t0=ttime()
|
426 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
427 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
428 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
429 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
430 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
431 |
+
# t1=ttime()
|
432 |
+
# print(f0.shape,t1-t0)
|
run.sh
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Install Debian packages
|
2 |
+
sudo apt-get update
|
3 |
+
sudo apt-get install -qq -y build-essential ffmpeg aria2
|
4 |
+
|
5 |
+
# Upgrade pip and setuptools
|
6 |
+
pip install --upgrade pip
|
7 |
+
pip install --upgrade setuptools
|
8 |
+
|
9 |
+
# Install wheel package (built-package format for Python)
|
10 |
+
pip install wheel
|
11 |
+
|
12 |
+
# Install Python packages using pip
|
13 |
+
pip install -r requirements.txt
|
14 |
+
|
15 |
+
# Run application locally at http://127.0.0.1:7860
|
16 |
+
python app.py
|
utils.py
ADDED
@@ -0,0 +1,151 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import ffmpeg
|
2 |
+
import numpy as np
|
3 |
+
|
4 |
+
# import praatio
|
5 |
+
# import praatio.praat_scripts
|
6 |
+
import os
|
7 |
+
import sys
|
8 |
+
|
9 |
+
import random
|
10 |
+
|
11 |
+
import csv
|
12 |
+
|
13 |
+
platform_stft_mapping = {
|
14 |
+
"linux": "stftpitchshift",
|
15 |
+
"darwin": "stftpitchshift",
|
16 |
+
"win32": "stftpitchshift.exe",
|
17 |
+
}
|
18 |
+
|
19 |
+
stft = platform_stft_mapping.get(sys.platform)
|
20 |
+
# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
|
21 |
+
|
22 |
+
|
23 |
+
def CSVutil(file, rw, type, *args):
|
24 |
+
if type == "formanting":
|
25 |
+
if rw == "r":
|
26 |
+
with open(file) as fileCSVread:
|
27 |
+
csv_reader = list(csv.reader(fileCSVread))
|
28 |
+
return (
|
29 |
+
(csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
|
30 |
+
if csv_reader is not None
|
31 |
+
else (lambda: exec('raise ValueError("No data")'))()
|
32 |
+
)
|
33 |
+
else:
|
34 |
+
if args:
|
35 |
+
doformnt = args[0]
|
36 |
+
else:
|
37 |
+
doformnt = False
|
38 |
+
qfr = args[1] if len(args) > 1 else 1.0
|
39 |
+
tmb = args[2] if len(args) > 2 else 1.0
|
40 |
+
with open(file, rw, newline="") as fileCSVwrite:
|
41 |
+
csv_writer = csv.writer(fileCSVwrite, delimiter=",")
|
42 |
+
csv_writer.writerow([doformnt, qfr, tmb])
|
43 |
+
elif type == "stop":
|
44 |
+
stop = args[0] if args else False
|
45 |
+
with open(file, rw, newline="") as fileCSVwrite:
|
46 |
+
csv_writer = csv.writer(fileCSVwrite, delimiter=",")
|
47 |
+
csv_writer.writerow([stop])
|
48 |
+
|
49 |
+
|
50 |
+
def load_audio(file, sr, DoFormant, Quefrency, Timbre):
|
51 |
+
converted = False
|
52 |
+
DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
|
53 |
+
try:
|
54 |
+
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
|
55 |
+
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
|
56 |
+
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
|
57 |
+
file = (
|
58 |
+
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
59 |
+
) # 防止小白拷路径头尾带了空格和"和回车
|
60 |
+
file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
61 |
+
|
62 |
+
# print(f"dofor={bool(DoFormant)} timbr={Timbre} quef={Quefrency}\n")
|
63 |
+
|
64 |
+
if (
|
65 |
+
lambda DoFormant: True
|
66 |
+
if DoFormant.lower() == "true"
|
67 |
+
else (False if DoFormant.lower() == "false" else DoFormant)
|
68 |
+
)(DoFormant):
|
69 |
+
numerator = round(random.uniform(1, 4), 4)
|
70 |
+
# os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
|
71 |
+
# print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
|
72 |
+
|
73 |
+
if not file.endswith(".wav"):
|
74 |
+
if not os.path.isfile(f"{file_formanted}.wav"):
|
75 |
+
converted = True
|
76 |
+
# print(f"\nfile = {file}\n")
|
77 |
+
# print(f"\nfile_formanted = {file_formanted}\n")
|
78 |
+
converting = (
|
79 |
+
ffmpeg.input(file_formanted, threads=0)
|
80 |
+
.output(f"{file_formanted}.wav")
|
81 |
+
.run(
|
82 |
+
cmd=["ffmpeg", "-nostdin"],
|
83 |
+
capture_stdout=True,
|
84 |
+
capture_stderr=True,
|
85 |
+
)
|
86 |
+
)
|
87 |
+
else:
|
88 |
+
pass
|
89 |
+
|
90 |
+
file_formanted = (
|
91 |
+
f"{file_formanted}.wav"
|
92 |
+
if not file_formanted.endswith(".wav")
|
93 |
+
else file_formanted
|
94 |
+
)
|
95 |
+
|
96 |
+
print(f" · Formanting {file_formanted}...\n")
|
97 |
+
|
98 |
+
os.system(
|
99 |
+
'%s -i "%s" -q "%s" -t "%s" -o "%sFORMANTED_%s.wav"'
|
100 |
+
% (
|
101 |
+
stft,
|
102 |
+
file_formanted,
|
103 |
+
Quefrency,
|
104 |
+
Timbre,
|
105 |
+
file_formanted,
|
106 |
+
str(numerator),
|
107 |
+
)
|
108 |
+
)
|
109 |
+
|
110 |
+
print(f" · Formanted {file_formanted}!\n")
|
111 |
+
|
112 |
+
# filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
|
113 |
+
# file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
|
114 |
+
# print("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
|
115 |
+
|
116 |
+
out, _ = (
|
117 |
+
ffmpeg.input(
|
118 |
+
"%sFORMANTED_%s.wav" % (file_formanted, str(numerator)), threads=0
|
119 |
+
)
|
120 |
+
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
121 |
+
.run(
|
122 |
+
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
|
123 |
+
)
|
124 |
+
)
|
125 |
+
|
126 |
+
try:
|
127 |
+
os.remove("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
|
128 |
+
except Exception:
|
129 |
+
pass
|
130 |
+
print("couldn't remove formanted type of file")
|
131 |
+
|
132 |
+
else:
|
133 |
+
out, _ = (
|
134 |
+
ffmpeg.input(file, threads=0)
|
135 |
+
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
136 |
+
.run(
|
137 |
+
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
|
138 |
+
)
|
139 |
+
)
|
140 |
+
except Exception as e:
|
141 |
+
raise RuntimeError(f"Failed to load audio: {e}")
|
142 |
+
|
143 |
+
if converted:
|
144 |
+
try:
|
145 |
+
os.remove(file_formanted)
|
146 |
+
except Exception:
|
147 |
+
pass
|
148 |
+
print("couldn't remove converted type of file")
|
149 |
+
converted = False
|
150 |
+
|
151 |
+
return np.frombuffer(out, np.float32).flatten()
|
vc_infer_pipeline.py
ADDED
@@ -0,0 +1,646 @@
|
|
|
|
|
|
|
|
|
|
|
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|
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|
1 |
+
import numpy as np, parselmouth, torch, pdb, sys, os
|
2 |
+
from time import time as ttime
|
3 |
+
import torch.nn.functional as F
|
4 |
+
import torchcrepe # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe)
|
5 |
+
from torch import Tensor
|
6 |
+
import scipy.signal as signal
|
7 |
+
import pyworld, os, traceback, faiss, librosa, torchcrepe
|
8 |
+
from scipy import signal
|
9 |
+
from functools import lru_cache
|
10 |
+
|
11 |
+
now_dir = os.getcwd()
|
12 |
+
sys.path.append(now_dir)
|
13 |
+
|
14 |
+
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
|
15 |
+
|
16 |
+
input_audio_path2wav = {}
|
17 |
+
|
18 |
+
|
19 |
+
@lru_cache
|
20 |
+
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
|
21 |
+
audio = input_audio_path2wav[input_audio_path]
|
22 |
+
f0, t = pyworld.harvest(
|
23 |
+
audio,
|
24 |
+
fs=fs,
|
25 |
+
f0_ceil=f0max,
|
26 |
+
f0_floor=f0min,
|
27 |
+
frame_period=frame_period,
|
28 |
+
)
|
29 |
+
f0 = pyworld.stonemask(audio, f0, t, fs)
|
30 |
+
return f0
|
31 |
+
|
32 |
+
|
33 |
+
def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频,2是输出音频,rate是2的占比
|
34 |
+
# print(data1.max(),data2.max())
|
35 |
+
rms1 = librosa.feature.rms(
|
36 |
+
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
|
37 |
+
) # 每半秒一个点
|
38 |
+
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
|
39 |
+
rms1 = torch.from_numpy(rms1)
|
40 |
+
rms1 = F.interpolate(
|
41 |
+
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
|
42 |
+
).squeeze()
|
43 |
+
rms2 = torch.from_numpy(rms2)
|
44 |
+
rms2 = F.interpolate(
|
45 |
+
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
|
46 |
+
).squeeze()
|
47 |
+
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
|
48 |
+
data2 *= (
|
49 |
+
torch.pow(rms1, torch.tensor(1 - rate))
|
50 |
+
* torch.pow(rms2, torch.tensor(rate - 1))
|
51 |
+
).numpy()
|
52 |
+
return data2
|
53 |
+
|
54 |
+
|
55 |
+
class VC(object):
|
56 |
+
def __init__(self, tgt_sr, config):
|
57 |
+
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
|
58 |
+
config.x_pad,
|
59 |
+
config.x_query,
|
60 |
+
config.x_center,
|
61 |
+
config.x_max,
|
62 |
+
config.is_half,
|
63 |
+
)
|
64 |
+
self.sr = 16000 # hubert输入采样率
|
65 |
+
self.window = 160 # 每帧点数
|
66 |
+
self.t_pad = self.sr * self.x_pad # 每条前后pad时间
|
67 |
+
self.t_pad_tgt = tgt_sr * self.x_pad
|
68 |
+
self.t_pad2 = self.t_pad * 2
|
69 |
+
self.t_query = self.sr * self.x_query # 查询切点前后查询时间
|
70 |
+
self.t_center = self.sr * self.x_center # 查询切点位置
|
71 |
+
self.t_max = self.sr * self.x_max # 免查询时长阈值
|
72 |
+
self.device = config.device
|
73 |
+
|
74 |
+
# Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
|
75 |
+
def get_optimal_torch_device(self, index: int = 0) -> torch.device:
|
76 |
+
# Get cuda device
|
77 |
+
if torch.cuda.is_available():
|
78 |
+
return torch.device(
|
79 |
+
f"cuda:{index % torch.cuda.device_count()}"
|
80 |
+
) # Very fast
|
81 |
+
elif torch.backends.mps.is_available():
|
82 |
+
return torch.device("mps")
|
83 |
+
# Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
|
84 |
+
# Else wise return the "cpu" as a torch device,
|
85 |
+
return torch.device("cpu")
|
86 |
+
|
87 |
+
# Fork Feature: Compute f0 with the crepe method
|
88 |
+
def get_f0_crepe_computation(
|
89 |
+
self,
|
90 |
+
x,
|
91 |
+
f0_min,
|
92 |
+
f0_max,
|
93 |
+
p_len,
|
94 |
+
hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
|
95 |
+
model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
|
96 |
+
):
|
97 |
+
x = x.astype(
|
98 |
+
np.float32
|
99 |
+
) # fixes the F.conv2D exception. We needed to convert double to float.
|
100 |
+
x /= np.quantile(np.abs(x), 0.999)
|
101 |
+
torch_device = self.get_optimal_torch_device()
|
102 |
+
audio = torch.from_numpy(x).to(torch_device, copy=True)
|
103 |
+
audio = torch.unsqueeze(audio, dim=0)
|
104 |
+
if audio.ndim == 2 and audio.shape[0] > 1:
|
105 |
+
audio = torch.mean(audio, dim=0, keepdim=True).detach()
|
106 |
+
audio = audio.detach()
|
107 |
+
print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
|
108 |
+
pitch: Tensor = torchcrepe.predict(
|
109 |
+
audio,
|
110 |
+
self.sr,
|
111 |
+
hop_length,
|
112 |
+
f0_min,
|
113 |
+
f0_max,
|
114 |
+
model,
|
115 |
+
batch_size=hop_length * 2,
|
116 |
+
device=torch_device,
|
117 |
+
pad=True,
|
118 |
+
)
|
119 |
+
p_len = p_len or x.shape[0] // hop_length
|
120 |
+
# Resize the pitch for final f0
|
121 |
+
source = np.array(pitch.squeeze(0).cpu().float().numpy())
|
122 |
+
source[source < 0.001] = np.nan
|
123 |
+
target = np.interp(
|
124 |
+
np.arange(0, len(source) * p_len, len(source)) / p_len,
|
125 |
+
np.arange(0, len(source)),
|
126 |
+
source,
|
127 |
+
)
|
128 |
+
f0 = np.nan_to_num(target)
|
129 |
+
return f0 # Resized f0
|
130 |
+
|
131 |
+
def get_f0_official_crepe_computation(
|
132 |
+
self,
|
133 |
+
x,
|
134 |
+
f0_min,
|
135 |
+
f0_max,
|
136 |
+
model="full",
|
137 |
+
):
|
138 |
+
# Pick a batch size that doesn't cause memory errors on your gpu
|
139 |
+
batch_size = 512
|
140 |
+
# Compute pitch using first gpu
|
141 |
+
audio = torch.tensor(np.copy(x))[None].float()
|
142 |
+
f0, pd = torchcrepe.predict(
|
143 |
+
audio,
|
144 |
+
self.sr,
|
145 |
+
self.window,
|
146 |
+
f0_min,
|
147 |
+
f0_max,
|
148 |
+
model,
|
149 |
+
batch_size=batch_size,
|
150 |
+
device=self.device,
|
151 |
+
return_periodicity=True,
|
152 |
+
)
|
153 |
+
pd = torchcrepe.filter.median(pd, 3)
|
154 |
+
f0 = torchcrepe.filter.mean(f0, 3)
|
155 |
+
f0[pd < 0.1] = 0
|
156 |
+
f0 = f0[0].cpu().numpy()
|
157 |
+
return f0
|
158 |
+
|
159 |
+
# Fork Feature: Compute pYIN f0 method
|
160 |
+
def get_f0_pyin_computation(self, x, f0_min, f0_max):
|
161 |
+
y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
|
162 |
+
f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
|
163 |
+
f0 = f0[1:] # Get rid of extra first frame
|
164 |
+
return f0
|
165 |
+
|
166 |
+
# Fork Feature: Acquire median hybrid f0 estimation calculation
|
167 |
+
def get_f0_hybrid_computation(
|
168 |
+
self,
|
169 |
+
methods_str,
|
170 |
+
input_audio_path,
|
171 |
+
x,
|
172 |
+
f0_min,
|
173 |
+
f0_max,
|
174 |
+
p_len,
|
175 |
+
filter_radius,
|
176 |
+
crepe_hop_length,
|
177 |
+
time_step,
|
178 |
+
):
|
179 |
+
# Get various f0 methods from input to use in the computation stack
|
180 |
+
s = methods_str
|
181 |
+
s = s.split("hybrid")[1]
|
182 |
+
s = s.replace("[", "").replace("]", "")
|
183 |
+
methods = s.split("+")
|
184 |
+
f0_computation_stack = []
|
185 |
+
|
186 |
+
print("Calculating f0 pitch estimations for methods: %s" % str(methods))
|
187 |
+
x = x.astype(np.float32)
|
188 |
+
x /= np.quantile(np.abs(x), 0.999)
|
189 |
+
# Get f0 calculations for all methods specified
|
190 |
+
for method in methods:
|
191 |
+
f0 = None
|
192 |
+
if method == "pm":
|
193 |
+
f0 = (
|
194 |
+
parselmouth.Sound(x, self.sr)
|
195 |
+
.to_pitch_ac(
|
196 |
+
time_step=time_step / 1000,
|
197 |
+
voicing_threshold=0.6,
|
198 |
+
pitch_floor=f0_min,
|
199 |
+
pitch_ceiling=f0_max,
|
200 |
+
)
|
201 |
+
.selected_array["frequency"]
|
202 |
+
)
|
203 |
+
pad_size = (p_len - len(f0) + 1) // 2
|
204 |
+
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
205 |
+
f0 = np.pad(
|
206 |
+
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
207 |
+
)
|
208 |
+
elif method == "crepe":
|
209 |
+
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
|
210 |
+
f0 = f0[1:] # Get rid of extra first frame
|
211 |
+
elif method == "crepe-tiny":
|
212 |
+
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
|
213 |
+
f0 = f0[1:] # Get rid of extra first frame
|
214 |
+
elif method == "mangio-crepe":
|
215 |
+
f0 = self.get_f0_crepe_computation(
|
216 |
+
x, f0_min, f0_max, p_len, crepe_hop_length
|
217 |
+
)
|
218 |
+
elif method == "mangio-crepe-tiny":
|
219 |
+
f0 = self.get_f0_crepe_computation(
|
220 |
+
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
|
221 |
+
)
|
222 |
+
elif method == "harvest":
|
223 |
+
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
|
224 |
+
if filter_radius > 2:
|
225 |
+
f0 = signal.medfilt(f0, 3)
|
226 |
+
f0 = f0[1:] # Get rid of first frame.
|
227 |
+
elif method == "dio": # Potentially buggy?
|
228 |
+
f0, t = pyworld.dio(
|
229 |
+
x.astype(np.double),
|
230 |
+
fs=self.sr,
|
231 |
+
f0_ceil=f0_max,
|
232 |
+
f0_floor=f0_min,
|
233 |
+
frame_period=10,
|
234 |
+
)
|
235 |
+
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
236 |
+
f0 = signal.medfilt(f0, 3)
|
237 |
+
f0 = f0[1:]
|
238 |
+
# elif method == "pyin": Not Working just yet
|
239 |
+
# f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
|
240 |
+
# Push method to the stack
|
241 |
+
f0_computation_stack.append(f0)
|
242 |
+
|
243 |
+
for fc in f0_computation_stack:
|
244 |
+
print(len(fc))
|
245 |
+
|
246 |
+
print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
|
247 |
+
f0_median_hybrid = None
|
248 |
+
if len(f0_computation_stack) == 1:
|
249 |
+
f0_median_hybrid = f0_computation_stack[0]
|
250 |
+
else:
|
251 |
+
f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
|
252 |
+
return f0_median_hybrid
|
253 |
+
|
254 |
+
def get_f0(
|
255 |
+
self,
|
256 |
+
input_audio_path,
|
257 |
+
x,
|
258 |
+
p_len,
|
259 |
+
f0_up_key,
|
260 |
+
f0_method,
|
261 |
+
filter_radius,
|
262 |
+
crepe_hop_length,
|
263 |
+
inp_f0=None,
|
264 |
+
):
|
265 |
+
global input_audio_path2wav
|
266 |
+
time_step = self.window / self.sr * 1000
|
267 |
+
f0_min = 50
|
268 |
+
f0_max = 1100
|
269 |
+
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
270 |
+
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
271 |
+
if f0_method == "pm":
|
272 |
+
f0 = (
|
273 |
+
parselmouth.Sound(x, self.sr)
|
274 |
+
.to_pitch_ac(
|
275 |
+
time_step=time_step / 1000,
|
276 |
+
voicing_threshold=0.6,
|
277 |
+
pitch_floor=f0_min,
|
278 |
+
pitch_ceiling=f0_max,
|
279 |
+
)
|
280 |
+
.selected_array["frequency"]
|
281 |
+
)
|
282 |
+
pad_size = (p_len - len(f0) + 1) // 2
|
283 |
+
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
284 |
+
f0 = np.pad(
|
285 |
+
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
286 |
+
)
|
287 |
+
elif f0_method == "harvest":
|
288 |
+
input_audio_path2wav[input_audio_path] = x.astype(np.double)
|
289 |
+
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
|
290 |
+
if filter_radius > 2:
|
291 |
+
f0 = signal.medfilt(f0, 3)
|
292 |
+
elif f0_method == "dio": # Potentially Buggy?
|
293 |
+
f0, t = pyworld.dio(
|
294 |
+
x.astype(np.double),
|
295 |
+
fs=self.sr,
|
296 |
+
f0_ceil=f0_max,
|
297 |
+
f0_floor=f0_min,
|
298 |
+
frame_period=10,
|
299 |
+
)
|
300 |
+
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
301 |
+
f0 = signal.medfilt(f0, 3)
|
302 |
+
elif f0_method == "crepe":
|
303 |
+
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
|
304 |
+
elif f0_method == "crepe-tiny":
|
305 |
+
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
|
306 |
+
elif f0_method == "mangio-crepe":
|
307 |
+
f0 = self.get_f0_crepe_computation(
|
308 |
+
x, f0_min, f0_max, p_len, crepe_hop_length
|
309 |
+
)
|
310 |
+
elif f0_method == "mangio-crepe-tiny":
|
311 |
+
f0 = self.get_f0_crepe_computation(
|
312 |
+
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
|
313 |
+
)
|
314 |
+
elif f0_method == "rmvpe":
|
315 |
+
if hasattr(self, "model_rmvpe") == False:
|
316 |
+
from rmvpe import RMVPE
|
317 |
+
|
318 |
+
print("loading rmvpe model")
|
319 |
+
self.model_rmvpe = RMVPE(
|
320 |
+
"rmvpe.pt", is_half=self.is_half, device=self.device
|
321 |
+
)
|
322 |
+
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
323 |
+
|
324 |
+
elif "hybrid" in f0_method:
|
325 |
+
# Perform hybrid median pitch estimation
|
326 |
+
input_audio_path2wav[input_audio_path] = x.astype(np.double)
|
327 |
+
f0 = self.get_f0_hybrid_computation(
|
328 |
+
f0_method,
|
329 |
+
input_audio_path,
|
330 |
+
x,
|
331 |
+
f0_min,
|
332 |
+
f0_max,
|
333 |
+
p_len,
|
334 |
+
filter_radius,
|
335 |
+
crepe_hop_length,
|
336 |
+
time_step,
|
337 |
+
)
|
338 |
+
|
339 |
+
f0 *= pow(2, f0_up_key / 12)
|
340 |
+
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
341 |
+
tf0 = self.sr // self.window # 每秒f0点数
|
342 |
+
if inp_f0 is not None:
|
343 |
+
delta_t = np.round(
|
344 |
+
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
|
345 |
+
).astype("int16")
|
346 |
+
replace_f0 = np.interp(
|
347 |
+
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
|
348 |
+
)
|
349 |
+
shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
|
350 |
+
f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
|
351 |
+
:shape
|
352 |
+
]
|
353 |
+
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
354 |
+
f0bak = f0.copy()
|
355 |
+
f0_mel = 1127 * np.log(1 + f0 / 700)
|
356 |
+
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
|
357 |
+
f0_mel_max - f0_mel_min
|
358 |
+
) + 1
|
359 |
+
f0_mel[f0_mel <= 1] = 1
|
360 |
+
f0_mel[f0_mel > 255] = 255
|
361 |
+
f0_coarse = np.rint(f0_mel).astype(np.int)
|
362 |
+
|
363 |
+
return f0_coarse, f0bak # 1-0
|
364 |
+
|
365 |
+
def vc(
|
366 |
+
self,
|
367 |
+
model,
|
368 |
+
net_g,
|
369 |
+
sid,
|
370 |
+
audio0,
|
371 |
+
pitch,
|
372 |
+
pitchf,
|
373 |
+
times,
|
374 |
+
index,
|
375 |
+
big_npy,
|
376 |
+
index_rate,
|
377 |
+
version,
|
378 |
+
protect,
|
379 |
+
): # ,file_index,file_big_npy
|
380 |
+
feats = torch.from_numpy(audio0)
|
381 |
+
if self.is_half:
|
382 |
+
feats = feats.half()
|
383 |
+
else:
|
384 |
+
feats = feats.float()
|
385 |
+
if feats.dim() == 2: # double channels
|
386 |
+
feats = feats.mean(-1)
|
387 |
+
assert feats.dim() == 1, feats.dim()
|
388 |
+
feats = feats.view(1, -1)
|
389 |
+
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
390 |
+
|
391 |
+
inputs = {
|
392 |
+
"source": feats.to(self.device),
|
393 |
+
"padding_mask": padding_mask,
|
394 |
+
"output_layer": 9 if version == "v1" else 12,
|
395 |
+
}
|
396 |
+
t0 = ttime()
|
397 |
+
with torch.no_grad():
|
398 |
+
logits = model.extract_features(**inputs)
|
399 |
+
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
|
400 |
+
if protect < 0.5 and pitch != None and pitchf != None:
|
401 |
+
feats0 = feats.clone()
|
402 |
+
if (
|
403 |
+
isinstance(index, type(None)) == False
|
404 |
+
and isinstance(big_npy, type(None)) == False
|
405 |
+
and index_rate != 0
|
406 |
+
):
|
407 |
+
npy = feats[0].cpu().numpy()
|
408 |
+
if self.is_half:
|
409 |
+
npy = npy.astype("float32")
|
410 |
+
|
411 |
+
# _, I = index.search(npy, 1)
|
412 |
+
# npy = big_npy[I.squeeze()]
|
413 |
+
|
414 |
+
score, ix = index.search(npy, k=8)
|
415 |
+
weight = np.square(1 / score)
|
416 |
+
weight /= weight.sum(axis=1, keepdims=True)
|
417 |
+
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
418 |
+
|
419 |
+
if self.is_half:
|
420 |
+
npy = npy.astype("float16")
|
421 |
+
feats = (
|
422 |
+
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
|
423 |
+
+ (1 - index_rate) * feats
|
424 |
+
)
|
425 |
+
|
426 |
+
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
427 |
+
if protect < 0.5 and pitch != None and pitchf != None:
|
428 |
+
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
|
429 |
+
0, 2, 1
|
430 |
+
)
|
431 |
+
t1 = ttime()
|
432 |
+
p_len = audio0.shape[0] // self.window
|
433 |
+
if feats.shape[1] < p_len:
|
434 |
+
p_len = feats.shape[1]
|
435 |
+
if pitch != None and pitchf != None:
|
436 |
+
pitch = pitch[:, :p_len]
|
437 |
+
pitchf = pitchf[:, :p_len]
|
438 |
+
|
439 |
+
if protect < 0.5 and pitch != None and pitchf != None:
|
440 |
+
pitchff = pitchf.clone()
|
441 |
+
pitchff[pitchf > 0] = 1
|
442 |
+
pitchff[pitchf < 1] = protect
|
443 |
+
pitchff = pitchff.unsqueeze(-1)
|
444 |
+
feats = feats * pitchff + feats0 * (1 - pitchff)
|
445 |
+
feats = feats.to(feats0.dtype)
|
446 |
+
p_len = torch.tensor([p_len], device=self.device).long()
|
447 |
+
with torch.no_grad():
|
448 |
+
if pitch != None and pitchf != None:
|
449 |
+
audio1 = (
|
450 |
+
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
|
451 |
+
.data.cpu()
|
452 |
+
.float()
|
453 |
+
.numpy()
|
454 |
+
)
|
455 |
+
else:
|
456 |
+
audio1 = (
|
457 |
+
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
|
458 |
+
)
|
459 |
+
del feats, p_len, padding_mask
|
460 |
+
if torch.cuda.is_available():
|
461 |
+
torch.cuda.empty_cache()
|
462 |
+
t2 = ttime()
|
463 |
+
times[0] += t1 - t0
|
464 |
+
times[2] += t2 - t1
|
465 |
+
return audio1
|
466 |
+
|
467 |
+
def pipeline(
|
468 |
+
self,
|
469 |
+
model,
|
470 |
+
net_g,
|
471 |
+
sid,
|
472 |
+
audio,
|
473 |
+
input_audio_path,
|
474 |
+
times,
|
475 |
+
f0_up_key,
|
476 |
+
f0_method,
|
477 |
+
file_index,
|
478 |
+
# file_big_npy,
|
479 |
+
index_rate,
|
480 |
+
if_f0,
|
481 |
+
filter_radius,
|
482 |
+
tgt_sr,
|
483 |
+
resample_sr,
|
484 |
+
rms_mix_rate,
|
485 |
+
version,
|
486 |
+
protect,
|
487 |
+
crepe_hop_length,
|
488 |
+
f0_file=None,
|
489 |
+
):
|
490 |
+
if (
|
491 |
+
file_index != ""
|
492 |
+
# and file_big_npy != ""
|
493 |
+
# and os.path.exists(file_big_npy) == True
|
494 |
+
and os.path.exists(file_index) == True
|
495 |
+
and index_rate != 0
|
496 |
+
):
|
497 |
+
try:
|
498 |
+
index = faiss.read_index(file_index)
|
499 |
+
# big_npy = np.load(file_big_npy)
|
500 |
+
big_npy = index.reconstruct_n(0, index.ntotal)
|
501 |
+
except:
|
502 |
+
traceback.print_exc()
|
503 |
+
index = big_npy = None
|
504 |
+
else:
|
505 |
+
index = big_npy = None
|
506 |
+
audio = signal.filtfilt(bh, ah, audio)
|
507 |
+
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
|
508 |
+
opt_ts = []
|
509 |
+
if audio_pad.shape[0] > self.t_max:
|
510 |
+
audio_sum = np.zeros_like(audio)
|
511 |
+
for i in range(self.window):
|
512 |
+
audio_sum += audio_pad[i : i - self.window]
|
513 |
+
for t in range(self.t_center, audio.shape[0], self.t_center):
|
514 |
+
opt_ts.append(
|
515 |
+
t
|
516 |
+
- self.t_query
|
517 |
+
+ np.where(
|
518 |
+
np.abs(audio_sum[t - self.t_query : t + self.t_query])
|
519 |
+
== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
|
520 |
+
)[0][0]
|
521 |
+
)
|
522 |
+
s = 0
|
523 |
+
audio_opt = []
|
524 |
+
t = None
|
525 |
+
t1 = ttime()
|
526 |
+
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
|
527 |
+
p_len = audio_pad.shape[0] // self.window
|
528 |
+
inp_f0 = None
|
529 |
+
if hasattr(f0_file, "name") == True:
|
530 |
+
try:
|
531 |
+
with open(f0_file.name, "r") as f:
|
532 |
+
lines = f.read().strip("\n").split("\n")
|
533 |
+
inp_f0 = []
|
534 |
+
for line in lines:
|
535 |
+
inp_f0.append([float(i) for i in line.split(",")])
|
536 |
+
inp_f0 = np.array(inp_f0, dtype="float32")
|
537 |
+
except:
|
538 |
+
traceback.print_exc()
|
539 |
+
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
540 |
+
pitch, pitchf = None, None
|
541 |
+
if if_f0 == 1:
|
542 |
+
pitch, pitchf = self.get_f0(
|
543 |
+
input_audio_path,
|
544 |
+
audio_pad,
|
545 |
+
p_len,
|
546 |
+
f0_up_key,
|
547 |
+
f0_method,
|
548 |
+
filter_radius,
|
549 |
+
crepe_hop_length,
|
550 |
+
inp_f0,
|
551 |
+
)
|
552 |
+
pitch = pitch[:p_len]
|
553 |
+
pitchf = pitchf[:p_len]
|
554 |
+
if self.device == "mps":
|
555 |
+
pitchf = pitchf.astype(np.float32)
|
556 |
+
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
557 |
+
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
|
558 |
+
t2 = ttime()
|
559 |
+
times[1] += t2 - t1
|
560 |
+
for t in opt_ts:
|
561 |
+
t = t // self.window * self.window
|
562 |
+
if if_f0 == 1:
|
563 |
+
audio_opt.append(
|
564 |
+
self.vc(
|
565 |
+
model,
|
566 |
+
net_g,
|
567 |
+
sid,
|
568 |
+
audio_pad[s : t + self.t_pad2 + self.window],
|
569 |
+
pitch[:, s // self.window : (t + self.t_pad2) // self.window],
|
570 |
+
pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
|
571 |
+
times,
|
572 |
+
index,
|
573 |
+
big_npy,
|
574 |
+
index_rate,
|
575 |
+
version,
|
576 |
+
protect,
|
577 |
+
)[self.t_pad_tgt : -self.t_pad_tgt]
|
578 |
+
)
|
579 |
+
else:
|
580 |
+
audio_opt.append(
|
581 |
+
self.vc(
|
582 |
+
model,
|
583 |
+
net_g,
|
584 |
+
sid,
|
585 |
+
audio_pad[s : t + self.t_pad2 + self.window],
|
586 |
+
None,
|
587 |
+
None,
|
588 |
+
times,
|
589 |
+
index,
|
590 |
+
big_npy,
|
591 |
+
index_rate,
|
592 |
+
version,
|
593 |
+
protect,
|
594 |
+
)[self.t_pad_tgt : -self.t_pad_tgt]
|
595 |
+
)
|
596 |
+
s = t
|
597 |
+
if if_f0 == 1:
|
598 |
+
audio_opt.append(
|
599 |
+
self.vc(
|
600 |
+
model,
|
601 |
+
net_g,
|
602 |
+
sid,
|
603 |
+
audio_pad[t:],
|
604 |
+
pitch[:, t // self.window :] if t is not None else pitch,
|
605 |
+
pitchf[:, t // self.window :] if t is not None else pitchf,
|
606 |
+
times,
|
607 |
+
index,
|
608 |
+
big_npy,
|
609 |
+
index_rate,
|
610 |
+
version,
|
611 |
+
protect,
|
612 |
+
)[self.t_pad_tgt : -self.t_pad_tgt]
|
613 |
+
)
|
614 |
+
else:
|
615 |
+
audio_opt.append(
|
616 |
+
self.vc(
|
617 |
+
model,
|
618 |
+
net_g,
|
619 |
+
sid,
|
620 |
+
audio_pad[t:],
|
621 |
+
None,
|
622 |
+
None,
|
623 |
+
times,
|
624 |
+
index,
|
625 |
+
big_npy,
|
626 |
+
index_rate,
|
627 |
+
version,
|
628 |
+
protect,
|
629 |
+
)[self.t_pad_tgt : -self.t_pad_tgt]
|
630 |
+
)
|
631 |
+
audio_opt = np.concatenate(audio_opt)
|
632 |
+
if rms_mix_rate != 1:
|
633 |
+
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
|
634 |
+
if resample_sr >= 16000 and tgt_sr != resample_sr:
|
635 |
+
audio_opt = librosa.resample(
|
636 |
+
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
|
637 |
+
)
|
638 |
+
audio_max = np.abs(audio_opt).max() / 0.99
|
639 |
+
max_int16 = 32768
|
640 |
+
if audio_max > 1:
|
641 |
+
max_int16 /= audio_max
|
642 |
+
audio_opt = (audio_opt * max_int16).astype(np.int16)
|
643 |
+
del pitch, pitchf, sid
|
644 |
+
if torch.cuda.is_available():
|
645 |
+
torch.cuda.empty_cache()
|
646 |
+
return audio_opt
|