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SOVITS4.0

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PNAC

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  1. PNAC/44k/D_100000.pth +3 -0
  2. PNAC/44k/D_96800.pth +3 -0
  3. PNAC/44k/D_97600.pth +3 -0
  4. PNAC/44k/D_98400.pth +3 -0
  5. PNAC/44k/D_99200.pth +3 -0
  6. PNAC/44k/G_100000.pth +3 -0
  7. PNAC/44k/G_96800.pth +3 -0
  8. PNAC/44k/G_97600.pth +3 -0
  9. PNAC/44k/G_98400.pth +3 -0
  10. PNAC/44k/G_99200.pth +3 -0
  11. PNAC/44k/config.json +95 -0
  12. PNAC/44k/githash +1 -0
  13. PNAC/44k/train.log +0 -0
  14. PNAC/PNAC_bak/config.json +95 -0
  15. PNAC/PNAC_bak/dataset.zip +3 -0
  16. PNAC/PNAC_bak/inference_main.py +101 -0
  17. PNAC/PNAC_bak/train.txt +1664 -0
  18. PNAC/PNAC_bak/val.txt +6 -0
  19. PNAC/config.json（副本）.json +64 -0
  20. PNAC/dataset/PNAC.zip +3 -0
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+ {
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+ "train": {
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+ "log_interval": 200,
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+ "eval_interval": 800,
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+ "seed": 6536180,
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+ "epochs": 10000,
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+ "learning_rate": 0.0001,
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+ "betas": [
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+ 0.8,
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+ 0.99
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+ ],
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+ "eps": 1e-09,
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+ "batch_size": 6,
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+ "fp16_run": true,
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+ "lr_decay": 0.999875,
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+ "segment_size": 10240,
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+ "init_lr_ratio": 1,
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+ "warmup_epochs": 0,
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+ "c_mel": 45,
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+ "c_kl": 1.0,
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+ "use_sr": true,
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+ "max_speclen": 512,
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+ "port": "8001",
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+ "keep_ckpts": 5
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+ },
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+ "data": {
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+ "training_files": "filelists/train.txt",
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+ "validation_files": "filelists/val.txt",
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+ "max_wav_value": 32768.0,
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+ "sampling_rate": 44100,
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+ "filter_length": 2048,
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+ "hop_length": 512,
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+ "win_length": 2048,
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+ "n_mel_channels": 80,
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+ "mel_fmin": 0.0,
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+ "mel_fmax": 22050
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+ },
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+ "model": {
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+ "inter_channels": 192,
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+ "hidden_channels": 192,
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+ "filter_channels": 768,
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+ "n_heads": 2,
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+ "n_layers": 6,
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+ "kernel_size": 3,
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+ "p_dropout": 0.1,
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+ "resblock": "1",
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+ "resblock_kernel_sizes": [
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+ 3,
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+ 7,
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+ 11
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+ ],
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+ "resblock_dilation_sizes": [
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+ [
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+ "upsample_rates": [
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+ 8,
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+ 8,
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "upsample_initial_channel": 512,
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+ "upsample_kernel_sizes": [
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+ 16,
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+ 16,
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+ 4,
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+ 4,
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+ 4
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+ ],
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+ "n_layers_q": 3,
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+ "use_spectral_norm": false,
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+ "gin_channels": 256,
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+ "ssl_dim": 256,
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+ "n_speakers": 200
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+ },
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+ "spk": {
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+ "nahida": 0,
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+ "pecorine": 1,
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+ "ayaka": 2
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+ }
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+ }
PNAC/44k/githash ADDED
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PNAC/44k/train.log ADDED
The diff for this file is too large to render. See raw diff
 
PNAC/PNAC_bak/config.json ADDED
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+ {
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+ "train": {
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+ "log_interval": 200,
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+ "eval_interval": 800,
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+ "seed": 6536180,
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+ "epochs": 10000,
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+ "learning_rate": 0.0001,
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+ "betas": [
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+ 0.8,
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+ 0.99
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+ ],
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+ "eps": 1e-09,
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+ "batch_size": 6,
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+ "fp16_run": true,
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+ "lr_decay": 0.999875,
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+ "segment_size": 10240,
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+ "init_lr_ratio": 1,
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+ "warmup_epochs": 0,
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+ "c_mel": 45,
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+ "c_kl": 1.0,
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+ "use_sr": true,
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+ "max_speclen": 512,
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+ "port": "8001",
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+ "keep_ckpts": 5
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+ },
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+ "data": {
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+ "training_files": "filelists/train.txt",
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+ "validation_files": "filelists/val.txt",
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+ "max_wav_value": 32768.0,
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+ "sampling_rate": 44100,
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+ "filter_length": 2048,
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+ "hop_length": 512,
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+ "win_length": 2048,
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+ "n_mel_channels": 80,
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+ "mel_fmin": 0.0,
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+ "mel_fmax": 22050
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+ },
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+ "model": {
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+ "inter_channels": 192,
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+ "hidden_channels": 192,
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+ "filter_channels": 768,
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+ "n_heads": 2,
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+ "n_layers": 6,
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+ "kernel_size": 3,
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+ "p_dropout": 0.1,
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+ "resblock": "1",
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+ "resblock_kernel_sizes": [
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+ 3,
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+ 7,
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+ 11
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+ ],
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+ "resblock_dilation_sizes": [
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+ [
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+ 1,
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+ 3,
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+ 5
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+ ],
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+ [
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+ ],
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+ [
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+ 1,
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+ 5
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+ ]
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+ ],
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+ "upsample_rates": [
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+ 8,
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+ 8,
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "upsample_initial_channel": 512,
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+ "upsample_kernel_sizes": [
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+ 16,
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+ 16,
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+ 4,
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+ 4,
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+ 4
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+ ],
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+ "n_layers_q": 3,
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+ "use_spectral_norm": false,
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+ "gin_channels": 256,
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+ "ssl_dim": 256,
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+ "n_speakers": 200
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+ },
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+ "spk": {
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+ "nahida": 0,
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+ "pecorine": 1,
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+ "ayaka": 2
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+ }
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+ }
PNAC/PNAC_bak/dataset.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:19a221159161de9244ac33ff3e9a80d4c4e07d82d2943ae8c343592f47faa6a9
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+ size 1203781740
PNAC/PNAC_bak/inference_main.py ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import io
2
+ import logging
3
+ import time
4
+ from pathlib import Path
5
+
6
+ import librosa
7
+ import matplotlib.pyplot as plt
8
+ import numpy as np
9
+ import soundfile
10
+
11
+ from inference import infer_tool
12
+ from inference import slicer
13
+ from inference.infer_tool import Svc
14
+
15
+ logging.getLogger('numba').setLevel(logging.WARNING)
16
+ chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
17
+
18
+
19
+
20
+ def main():
21
+ import argparse
22
+
23
+ parser = argparse.ArgumentParser(description='sovits4 inference')
24
+
25
+ # 一定要设置的部分
26
+ parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
27
+ parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
28
+ parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
29
+ parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）')
30
+ parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
31
+
32
+ # 可选项部分
33
+ parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,
34
+ help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
35
+ parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
36
+ parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则填0即可')
37
+
38
+ # 不用动的部分
39
+ parser.add_argument('-sd', '--slice_db', type=int, default=-50, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
40
+ parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu')
41
+ parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别，会影响咬字和音质，较为玄学')
42
+ parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现')
43
+ parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
44
+
45
+ args = parser.parse_args()
46
+
47
+ svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path)
48
+ infer_tool.mkdir(["raw/raw", "results"])
49
+ clean_names = args.clean_names
50
+ trans = args.trans
51
+ spk_list = args.spk_list
52
+ slice_db = args.slice_db
53
+ wav_format = args.wav_format
54
+ auto_predict_f0 = args.auto_predict_f0
55
+ cluster_infer_ratio = args.cluster_infer_ratio
56
+ noice_scale = args.noice_scale
57
+ pad_seconds = args.pad_seconds
58
+
59
+ infer_tool.fill_a_to_b(trans, clean_names)
60
+ for clean_name, tran in zip(clean_names, trans):
61
+ raw_audio_path = f"raw/raw/{clean_name}"
62
+ if "." not in raw_audio_path:
63
+ raw_audio_path += ".wav"
64
+ infer_tool.format_wav(raw_audio_path)
65
+ wav_path = Path(raw_audio_path).with_suffix('.wav')
66
+ chunks = slicer.cut(wav_path, db_thresh=slice_db)
67
+ audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
68
+
69
+ for spk in spk_list:
70
+ audio = []
71
+ for (slice_tag, data) in audio_data:
72
+ print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
73
+
74
+ length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
75
+ if slice_tag:
76
+ print('jump empty segment')
77
+ _audio = np.zeros(length)
78
+ else:
79
+ # padd
80
+ pad_len = int(audio_sr * pad_seconds)
81
+ data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
82
+ raw_path = io.BytesIO()
83
+ soundfile.write(raw_path, data, audio_sr, format="wav")
84
+ raw_path.seek(0)
85
+ out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
86
+ cluster_infer_ratio=cluster_infer_ratio,
87
+ auto_predict_f0=auto_predict_f0,
88
+ noice_scale=noice_scale
89
+ )
90
+ _audio = out_audio.cpu().numpy()
91
+ pad_len = int(svc_model.target_sample * pad_seconds)
92
+ _audio = _audio[pad_len:-pad_len]
93
+
94
+ audio.extend(list(infer_tool.pad_array(_audio, length)))
95
+ key = "auto" if auto_predict_f0 else f"{tran}key"
96
+ cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
97
+ res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
98
+ soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
99
+
100
+ if __name__ == '__main__':
101
+ main()
PNAC/PNAC_bak/train.txt ADDED
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