diodiogod
40f0e873be
- Bundle VoiceFixer locally to avoid cache downloads - Create voice_fixer_downloader for TTS/voicefixer/ model management - Implement Voice Fixer node with 3 restoration modes (original, high-freq removal, train mode) - Register node in nodes.py with proper error handling - Patch bundled VoiceFixer hardcoded paths to use TTS/voicefixer/ directory - Disable auto-download in restorer/__init__.py, handled by node instead - Support lazy loading and CUDA acceleration - All models download to organized TTS/ folder, never to cache
317 lines
7.8 KiB
Python
317 lines
7.8 KiB
Python
import torch
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import numpy as np
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import os
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from voicefixer_bundled.tools.path import root_path
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class Config:
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@classmethod
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def refresh(cls, sr):
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if sr == 44100:
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Config.ckpt = os.path.join(
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os.path.expanduser("~"),
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".cache/voicefixer/synthesis_module/44100/model.ckpt-1490000_trimed.pt",
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)
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Config.cond_channels = 512
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Config.m_channels = 768
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Config.resstack_depth = [8, 8, 8, 8]
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Config.channels = 1024
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Config.cin_channels = 128
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Config.upsample_scales = [7, 7, 3, 3]
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Config.num_mels = 128
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Config.n_fft = 2048
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Config.hop_length = 441
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Config.sample_rate = 44100
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Config.fmax = 22000
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Config.mel_win = 128
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Config.local_condition_dim = 128
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else:
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raise RuntimeError(
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"Error: Vocoder currently only support 44100 samplerate."
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)
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ckpt = os.path.join(
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os.path.expanduser("~"),
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".cache/voicefixer/synthesis_module/44100/model.ckpt-1490000_trimed.pt",
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)
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m_channels = 384
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bits = 10
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opt = "Ralamb"
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cond_channels = 256
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clip = 0.5
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num_bands = 1
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cin_channels = 128
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upsample_scales = [7, 7, 3, 3]
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filterbands = "test/filterbanks_4bands.dat"
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##For inference
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tag = ""
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min_db = -115
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num_mels = 128
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n_fft = 2048
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hop_length = 441
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win_size = None
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sample_rate = 44100
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frame_shift_ms = None
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trim_fft_size = 512
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trim_hop_size = 128
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trim_top_db = 23
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signal_normalization = True
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allow_clipping_in_normalization = True
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symmetric_mels = True
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max_abs_value = 4.0
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preemphasis = 0.85
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min_level_db = -100
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ref_level_db = 20
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fmin = 50
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fmax = 22000
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power = 1.5
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griffin_lim_iters = 60
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rescale = False
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rescaling_max = 0.95
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trim_silence = False
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clip_mels_length = True
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max_mel_frames = 2000
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mel_win = 128
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batch_size = 24
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g_learning_rate = 0.001
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d_learning_rate = 0.001
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warmup_steps = 100000
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decay_learning_rate = 0.5
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exponential_moving_average = True
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ema_decay = 0.99
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reset_opt = False
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reset_g_opt = False
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reset_d_opt = False
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local_condition_dim = 128
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lambda_update_G = 1
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multiscale_D = 3
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lambda_adv = 4.0
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lambda_fm_loss = 0.0
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lambda_sc_loss = 5.0
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lambda_mag_loss = 5.0
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lambda_mel_loss = 50.0
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use_mle_loss = False
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lambda_mle_loss = 5.0
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lambda_freq_loss = 2.0
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lambda_energy_loss = 100.0
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lambda_t_loss = 200.0
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lambda_phase_loss = 100.0
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lambda_f0_loss = 1.0
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use_elu = False
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de_preem = False # train
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up_org = False
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use_one = True
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use_small_D = False
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use_condnet = True
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use_depreem = False # inference
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use_msd = False
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model_type = "tfgan" # or bytewave, frame level vocoder using istft
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use_hjcud = False
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no_skip = False
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out_channels = 1
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use_postnet = False # wn in postnet
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use_wn = False # wn in resstack
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up_type = "transpose"
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use_smooth = False
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use_drop = False
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use_shift_scale = False
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use_gcnn = False
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resstack_depth = [6, 6, 6, 6]
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kernel_size = [3, 3, 3, 3]
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channels = 512
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use_f0_loss = False
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use_sine = False
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use_cond_rnn = False
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use_rnn = False
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f0_step = 120
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use_lowfreq_loss = False
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lambda_lowfreq_loss = 1.0
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use_film = False
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use_mb_mr_gan = False
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use_mssl = False
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use_ml_gan = False
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use_mb_gan = True
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use_mpd = False
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use_spec_gan = True
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use_rwd = False
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use_mr_gan = True
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use_pqmf_rwd = False
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no_sine = False
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use_frame_mask = False
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lambda_var_loss = 0.0
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discriminator_train_start_steps = 40000 # 80k
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aux_d_train_start_steps = 40000 # 100k
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rescale_out = 0.40
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use_dist = True
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dist_backend = "nccl"
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dist_url = "tcp://localhost:12345"
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world_size = 1
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mel_weight_torch = torch.tensor(
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[
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19.40951426,
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19.94047336,
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20.4859038,
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21.04629067,
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21.62194148,
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22.21335214,
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22.8210215,
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23.44529231,
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24.08660962,
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24.74541882,
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25.42234287,
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26.11770576,
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26.83212784,
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27.56615283,
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28.32007747,
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29.0947679,
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29.89060111,
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30.70832636,
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31.54828121,
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32.41121487,
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33.29780773,
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34.20865341,
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35.14437675,
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36.1056621,
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37.09332763,
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38.10795802,
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39.15039691,
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40.22119881,
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41.32154931,
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42.45172373,
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43.61293329,
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44.80609379,
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46.031602,
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47.29070223,
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48.58427549,
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49.91327905,
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51.27863232,
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52.68119708,
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54.1222372,
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55.60274206,
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57.12364703,
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58.68617876,
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60.29148652,
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61.94081306,
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63.63501986,
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65.37562658,
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67.16408954,
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69.00109084,
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70.88850318,
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72.82736101,
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74.81985537,
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76.86654792,
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78.96885475,
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81.12900906,
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83.34840929,
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85.62810662,
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87.97005418,
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90.37689804,
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92.84887686,
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95.38872881,
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97.99777002,
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100.67862715,
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103.43232942,
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106.26140638,
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109.16827015,
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112.15470471,
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115.22184756,
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118.37439245,
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121.6122689,
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124.93877158,
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128.35661454,
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131.86761321,
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135.47417938,
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139.18059494,
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142.98713744,
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146.89771854,
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150.91684347,
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155.0446638,
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159.28614648,
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163.64270198,
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168.12035831,
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172.71749158,
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177.44220154,
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182.29556933,
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187.28286676,
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192.40502126,
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197.6682721,
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203.07516896,
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208.63088733,
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214.33770931,
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220.19910108,
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226.22363072,
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232.41087124,
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238.76803591,
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245.30079083,
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252.01064464,
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258.90261676,
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265.98474,
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273.26010248,
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280.73496362,
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288.41440094,
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296.30489752,
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304.41180337,
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312.7377183,
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321.28877878,
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330.07870237,
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339.10812951,
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348.38276173,
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357.91393924,
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367.70513992,
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377.76413924,
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388.09467408,
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398.70920178,
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409.61813793,
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420.81980127,
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432.33215467,
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444.16083117,
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456.30919947,
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468.78589276,
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481.61325588,
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494.78824596,
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508.31969844,
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522.2238331,
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536.51163441,
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551.18859414,
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566.26142988,
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581.75006061,
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597.66210737,
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]
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)
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x_orig = np.linspace(1, mel_weight_torch.shape[0], num=mel_weight_torch.shape[0])
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x_orig_torch = torch.linspace(
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1, mel_weight_torch.shape[0], steps=mel_weight_torch.shape[0]
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)
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@classmethod
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def get_mel_weight(cls, percent=1, a=18.8927416350036, b=0.0269863588184314):
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b = percent * b
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def func(a, b, x):
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return a * np.exp(b * x)
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return func(a, b, Config.x_orig)
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@classmethod
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def get_mel_weight_torch(cls, percent=1, a=18.8927416350036, b=0.0269863588184314):
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b = percent * b
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def func(a, b, x):
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return a * torch.exp(b * x)
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return func(a, b, Config.x_orig_torch)
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