Hev832 commited on
Commit
ced832c
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1 Parent(s): 1f9caee

Update hevrvc.py

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Files changed (1) hide show
  1. hevrvc.py +159 -16
hevrvc.py CHANGED
@@ -7,6 +7,119 @@ import faiss
7
  from sklearn.cluster import MiniBatchKMeans
8
  import traceback
9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
10
  def calculate_audio_duration(file_path):
11
  duration_seconds = len(AudioSegment.from_file(file_path)) / 1000.0
12
  return duration_seconds
@@ -107,22 +220,52 @@ def train_index(exp_dir1, version19):
107
  return "\n".join(infos)
108
 
109
  with gr.Blocks() as demo:
110
- with gr.Tab("CREATE TRANING FILES - This will process the data, extract the features and create your index file for you!"):
111
- with gr.Row():
112
- model_name = gr.Textbox(label="Model Name", value="My-Voice")
113
- dataset_folder = gr.Textbox(label="Dataset Folder", value="/content/dataset")
114
- youtube_link = gr.Textbox(label="YouTube Link (optional)")
115
- with gr.Row():
116
- start_button = gr.Button("Create Training Files")
117
- f0method = gr.Dropdown(["pm", "harvest", "rmvpe", "rmvpe_gpu"], label="F0 Method", value="rmvpe_gpu")
118
- extract_button = gr.Button("Extract Features")
119
- train_button = gr.Button("Train Index")
120
-
121
- output = gr.Textbox(label="Output")
122
-
123
- start_button.click(create_training_files, inputs=[model_name, dataset_folder, youtube_link], outputs=output)
124
- extract_button.click(extract_features, inputs=[model_name, f0method], outputs=output)
125
- train_button.click(train_index, inputs=[model_name, "v2"], outputs=output)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
126
 
127
  demo.launch()
128
 
 
7
  from sklearn.cluster import MiniBatchKMeans
8
  import traceback
9
 
10
+ import gradio as gr
11
+ import os
12
+ from random import shuffle
13
+ import json
14
+ import pathlib
15
+ from subprocess import Popen, PIPE, STDOUT
16
+
17
+ # Define the function for training
18
+ def click_train(
19
+ exp_dir1,
20
+ sr2,
21
+ if_f0_3,
22
+ spk_id5,
23
+ save_epoch10,
24
+ total_epoch11,
25
+ batch_size12,
26
+ if_save_latest13,
27
+ pretrained_G14,
28
+ pretrained_D15,
29
+ gpus16,
30
+ if_cache_gpu17,
31
+ if_save_every_weights18,
32
+ version19,
33
+ ):
34
+ now_dir = os.getcwd()
35
+ exp_dir = f"{now_dir}/logs/{exp_dir1}"
36
+ os.makedirs(exp_dir, exist_ok=True)
37
+ gt_wavs_dir = f"{exp_dir}/0_gt_wavs"
38
+ feature_dir = (
39
+ f"{exp_dir}/3_feature256" if version19 == "v1" else f"{exp_dir}/3_feature768"
40
+ )
41
+
42
+ if if_f0_3:
43
+ f0_dir = f"{exp_dir}/2a_f0"
44
+ f0nsf_dir = f"{exp_dir}/2b-f0nsf"
45
+ names = (
46
+ set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
47
+ & set([name.split(".")[0] for name in os.listdir(feature_dir)])
48
+ & set([name.split(".")[0] for name in os.listdir(f0_dir)])
49
+ & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
50
+ )
51
+ else:
52
+ names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
53
+ [name.split(".")[0] for name in os.listdir(feature_dir)]
54
+ )
55
+
56
+ opt = []
57
+ for name in names:
58
+ if if_f0_3:
59
+ opt.append(
60
+ f"{gt_wavs_dir.replace('\\', '\\\\')}/{name}.wav|{feature_dir.replace('\\', '\\\\')}/{name}.npy|{f0_dir.replace('\\', '\\\\')}/{name}.wav.npy|{f0nsf_dir.replace('\\', '\\\\')}/{name}.wav.npy|{spk_id5}"
61
+ )
62
+ else:
63
+ opt.append(
64
+ f"{gt_wavs_dir.replace('\\', '\\\\')}/{name}.wav|{feature_dir.replace('\\', '\\\\')}/{name}.npy|{spk_id5}"
65
+ )
66
+
67
+ fea_dim = 256 if version19 == "v1" else 768
68
+ if if_f0_3:
69
+ for _ in range(2):
70
+ opt.append(
71
+ f"{now_dir}/logs/mute/0_gt_wavs/mute{sr2}.wav|{now_dir}/logs/mute/3_feature{fea_dim}/mute.npy|{now_dir}/logs/mute/2a_f0/mute.wav.npy|{now_dir}/logs/mute/2b-f0nsf/mute.wav.npy|{spk_id5}"
72
+ )
73
+ else:
74
+ for _ in range(2):
75
+ opt.append(
76
+ f"{now_dir}/logs/mute/0_gt_wavs/mute{sr2}.wav|{now_dir}/logs/mute/3_feature{fea_dim}/mute.npy|{spk_id5}"
77
+ )
78
+
79
+ shuffle(opt)
80
+ with open(f"{exp_dir}/filelist.txt", "w") as f:
81
+ f.write("\n".join(opt))
82
+
83
+ print("Write filelist done")
84
+ print("Use gpus:", str(gpus16))
85
+ if pretrained_G14 == "":
86
+ print("No pretrained Generator")
87
+ if pretrained_D15 == "":
88
+ print("No pretrained Discriminator")
89
+
90
+ if version19 == "v1" or sr2 == "40k":
91
+ config_path = f"configs/v1/{sr2}.json"
92
+ else:
93
+ config_path = f"configs/v2/{sr2}.json"
94
+
95
+ config_save_path = os.path.join(exp_dir, "config.json")
96
+ if not pathlib.Path(config_save_path).exists():
97
+ with open(config_save_path, "w", encoding="utf-8") as f:
98
+ with open(config_path, "r") as config_file:
99
+ config_data = json.load(config_file)
100
+ json.dump(
101
+ config_data,
102
+ f,
103
+ ensure_ascii=False,
104
+ indent=4,
105
+ sort_keys=True,
106
+ )
107
+ f.write("\n")
108
+
109
+ cmd = (
110
+ f'python infer/modules/train/train.py -e "{exp_dir1}" -sr {sr2} -f0 {1 if if_f0_3 else 0} -bs {batch_size12} -g {gpus16} -te {total_epoch11} -se {save_epoch10} {"-pg " + pretrained_G14 if pretrained_G14 != "" else ""} {"-pd " + pretrained_D15 if pretrained_D15 != "" else ""} -l {1 if if_save_latest13 else 0} -c {1 if if_cache_gpu17 else 0} -sw {1 if if_save_every_weights18 else 0} -v {version19}'
111
+ )
112
+
113
+ p = Popen(cmd, shell=True, cwd=now_dir, stdout=PIPE, stderr=STDOUT, bufsize=1, universal_newlines=True)
114
+
115
+ for line in p.stdout:
116
+ print(line.strip())
117
+
118
+ p.wait()
119
+ return "After the training is completed, you can view the console training log or train.log under the experiment folder"
120
+
121
+
122
+
123
  def calculate_audio_duration(file_path):
124
  duration_seconds = len(AudioSegment.from_file(file_path)) / 1000.0
125
  return duration_seconds
 
220
  return "\n".join(infos)
221
 
222
  with gr.Blocks() as demo:
223
+ with gr.Tab("Training"):
224
+ with gr.Tab("CREATE TRANING FILES - This will process the data, extract the features and create your index file for you!"):
225
+ with gr.Row():
226
+ model_name = gr.Textbox(label="Model Name", value="My-Voice")
227
+ dataset_folder = gr.Textbox(label="Dataset Folder", value="/content/dataset")
228
+ youtube_link = gr.Textbox(label="YouTube Link (optional)")
229
+ with gr.Row():
230
+ start_button = gr.Button("Create Training Files")
231
+ f0method = gr.Dropdown(["pm", "harvest", "rmvpe", "rmvpe_gpu"], label="F0 Method", value="rmvpe_gpu")
232
+ extract_button = gr.Button("Extract Features")
233
+ train_button = gr.Button("Train Index")
234
+
235
+ output = gr.Textbox(label="Output")
236
+
237
+ start_button.click(create_training_files, inputs=[model_name, dataset_folder, youtube_link], outputs=output)
238
+ extract_button.click(extract_features, inputs=[model_name, f0method], outputs=output)
239
+ train_button.click(train_index, inputs=[model_name, "v2"], outputs=output)
240
+ with gr.Tab("train"):
241
+ exp_dir1 = gr.Textbox(label="Experiment Directory", value="mymodel")
242
+ sr2 = gr.Dropdown(choices=["32k", "40k", "48k"], label="Sample Rate", value="32k")
243
+ if_f0_3 = gr.Checkbox(label="Use F0", value=True)
244
+ spk_id5 = gr.Number(label="Speaker ID", value=0)
245
+ save_epoch10 = gr.Slider(label="Save Frequency", minimum=5, maximum=50, step=5, value=25)
246
+ total_epoch11 = gr.Slider(label="Total Epochs", minimum=10, maximum=2000, step=10, value=500)
247
+ batch_size12 = gr.Slider(label="Batch Size", minimum=1, maximum=20, step=1, value=8)
248
+ if_save_latest13 = gr.Checkbox(label="Save Latest", value=True)
249
+ pretrained_G14 = gr.Textbox(label="Pretrained Generator File", value="/content/pre/assets/pretrained_v2/f0Ov2Super32kG.pth")
250
+ pretrained_D15 = gr.Textbox(label="Pretrained Discriminator File", value="/content/pre/assets/pretrained_v2/f0Ov2Super32kD.pth")
251
+ gpus16 = gr.Number(label="GPUs", value=0)
252
+ if_cache_gpu17 = gr.Checkbox(label="Cache GPU", value=False)
253
+ if_save_every_weights18 = gr.Checkbox(label="Save Every Weights", value=True)
254
+ version19 = gr.Textbox(label="Version", value="v2")
255
+ training_log = gr.Textbox(label="Training Log", interactive=False)
256
+ train_button = gr.Button("Start Training")
257
+
258
+ train_button.click(
259
+ fn=click_train,
260
+ inputs=[
261
+ exp_dir1, sr2, if_f0_3, spk_id5, save_epoch10, total_epoch11, batch_size12,
262
+ if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17,
263
+ if_save_every_weights18, version19
264
+ ],
265
+ outputs=training_log
266
+ )
267
+
268
+
269
 
270
  demo.launch()
271