Increase convenience for colab training
This commit is contained in:
parent
d40ae694fe
commit
c092d25716
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@ -1,106 +0,0 @@
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{
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"train": {
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"log_interval": 50,
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"eval_interval": 1000,
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"seed": 1234,
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"port": 8001,
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"epochs": 10000,
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"learning_rate": 0.0002,
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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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"accumulation_steps": 1,
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"fp16_run": false,
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"lr_decay": 0.998,
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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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"keep_ckpts":4
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},
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"data": {
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"data_dir": "dataset",
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"dataset_type": "SingDataset",
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"collate_type": "SingCollate",
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"training_filelist": "filelists/train.txt",
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"validation_filelist": "filelists/val.txt",
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"max_wav_value": 32768.0,
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"sampling_rate": 44100,
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"n_fft": 2048,
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"fmin": 0,
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"fmax": 22050,
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"hop_length": 512,
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"win_size": 2048,
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"acoustic_dim": 80,
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"c_dim": 256,
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"min_level_db": -115,
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"ref_level_db": 20,
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"min_db": -115,
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"max_abs_value": 4.0,
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"n_speakers": 200
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},
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"model": {
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"hidden_channels": 192,
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"spk_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 4,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"prior_hidden_channels": 192,
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"prior_filter_channels": 768,
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"prior_n_heads": 2,
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"prior_n_layers": 4,
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"prior_kernel_size": 3,
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"prior_p_dropout": 0.1,
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"resblock": "1",
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"use_spectral_norm": false,
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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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1,
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3,
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5
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],
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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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"upsample_rates": [
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8,
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8,
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4,
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2
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],
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"upsample_initial_channel": 256,
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"upsample_kernel_sizes": [
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16,
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16,
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8,
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4
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],
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"n_harmonic": 64,
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"n_bands": 65
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},
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"spk": {
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"jishuang": 0,
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"huiyu": 1,
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"nen": 2,
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"paimon": 3,
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"yunhao": 4
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}
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}
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@ -0,0 +1,106 @@
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{
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"train": {
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"log_interval": 50,
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"eval_interval": 1000,
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"seed": 1234,
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"port": 8001,
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"epochs": 10000,
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"learning_rate": 0.0002,
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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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"accumulation_steps": 1,
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"fp16_run": false,
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"lr_decay": 0.998,
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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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"keep_ckpts":4
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},
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"data": {
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"data_dir": "dataset",
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"dataset_type": "SingDataset",
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"collate_type": "SingCollate",
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"training_filelist": "filelists/train.txt",
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"validation_filelist": "filelists/val.txt",
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"max_wav_value": 32768.0,
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"sampling_rate": 44100,
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"n_fft": 2048,
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"fmin": 0,
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"fmax": 22050,
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"hop_length": 512,
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"win_size": 2048,
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"acoustic_dim": 80,
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"c_dim": 256,
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"min_level_db": -115,
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"ref_level_db": 20,
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"min_db": -115,
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"max_abs_value": 4.0,
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"n_speakers": 200
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},
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"model": {
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"hidden_channels": 192,
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"spk_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 4,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"prior_hidden_channels": 192,
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"prior_filter_channels": 768,
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"prior_n_heads": 2,
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"prior_n_layers": 4,
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"prior_kernel_size": 3,
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"prior_p_dropout": 0.1,
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"resblock": "1",
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"use_spectral_norm": false,
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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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1,
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3,
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5
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],
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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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"upsample_rates": [
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8,
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8,
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4,
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2
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],
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"upsample_initial_channel": 256,
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"upsample_kernel_sizes": [
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16,
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16,
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8,
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4
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],
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"n_harmonic": 64,
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"n_bands": 65
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},
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"spk": {
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"jishuang": 0,
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"huiyu": 1,
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"nen": 2,
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"paimon": 3,
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"yunhao": 4
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}
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}
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@ -1,83 +1,83 @@
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import os
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import argparse
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import re
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from tqdm import tqdm
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from random import shuffle
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import json
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import wave
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config_template = json.load(open("configs/config.json"))
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pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
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def get_wav_duration(file_path):
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with wave.open(file_path, 'rb') as wav_file:
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# 获取音频帧数
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n_frames = wav_file.getnframes()
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# 获取采样率
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framerate = wav_file.getframerate()
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# 计算时长(秒)
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duration = n_frames / float(framerate)
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return duration
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
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parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
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parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
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parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
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args = parser.parse_args()
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train = []
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val = []
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test = []
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idx = 0
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spk_dict = {}
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spk_id = 0
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for speaker in tqdm(os.listdir(args.source_dir)):
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spk_dict[speaker] = spk_id
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spk_id += 1
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wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
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new_wavs = []
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for file in wavs:
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if not file.endswith("wav"):
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continue
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if not pattern.match(file):
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print(f"warning:文件名{file}中包含非字母数字下划线,可能会导致错误。(也可能不会)")
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if get_wav_duration(file) < 0.3:
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print("skip too short audio:", file)
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continue
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new_wavs.append(file)
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wavs = new_wavs
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shuffle(wavs)
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train += wavs[2:-2]
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val += wavs[:2]
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test += wavs[-2:]
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shuffle(train)
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shuffle(val)
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shuffle(test)
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print("Writing", args.train_list)
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with open(args.train_list, "w") as f:
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for fname in tqdm(train):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.val_list)
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with open(args.val_list, "w") as f:
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for fname in tqdm(val):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.test_list)
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with open(args.test_list, "w") as f:
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for fname in tqdm(test):
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wavpath = fname
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f.write(wavpath + "\n")
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config_template["spk"] = spk_dict
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print("Writing configs/config.json")
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with open("configs/config.json", "w") as f:
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json.dump(config_template, f, indent=2)
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import os
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import argparse
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import re
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from tqdm import tqdm
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from random import shuffle
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import json
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import wave
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config_template = json.load(open("configs_template/config_template.json"))
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pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
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def get_wav_duration(file_path):
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with wave.open(file_path, 'rb') as wav_file:
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# 获取音频帧数
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n_frames = wav_file.getnframes()
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# 获取采样率
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framerate = wav_file.getframerate()
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# 计算时长(秒)
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duration = n_frames / float(framerate)
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return duration
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
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parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
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parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
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parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
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args = parser.parse_args()
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train = []
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val = []
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test = []
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idx = 0
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spk_dict = {}
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spk_id = 0
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for speaker in tqdm(os.listdir(args.source_dir)):
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spk_dict[speaker] = spk_id
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spk_id += 1
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wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
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new_wavs = []
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for file in wavs:
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if not file.endswith("wav"):
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continue
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if not pattern.match(file):
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print(f"warning:文件名{file}中包含非字母数字下划线,可能会导致错误。(也可能不会)")
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if get_wav_duration(file) < 0.3:
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print("skip too short audio:", file)
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continue
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new_wavs.append(file)
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wavs = new_wavs
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shuffle(wavs)
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train += wavs[2:-2]
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val += wavs[:2]
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test += wavs[-2:]
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shuffle(train)
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shuffle(val)
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shuffle(test)
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print("Writing", args.train_list)
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with open(args.train_list, "w") as f:
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for fname in tqdm(train):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.val_list)
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with open(args.val_list, "w") as f:
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for fname in tqdm(val):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.test_list)
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with open(args.test_list, "w") as f:
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for fname in tqdm(test):
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wavpath = fname
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f.write(wavpath + "\n")
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config_template["spk"] = spk_dict
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print("Writing configs/config.json")
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with open("configs/config.json", "w") as f:
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json.dump(config_template, f, indent=2)
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