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
136 lines
4.0 KiB
Python
136 lines
4.0 KiB
Python
from voicefixer_bundled.vocoder.config import Config
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from voicefixer_bundled.tools.pytorch_util import try_tensor_cuda, check_cuda_availability
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import torch
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import librosa
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import numpy as np
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def tr_normalize(S):
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if Config.allow_clipping_in_normalization:
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if Config.symmetric_mels:
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return torch.clip(
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(2 * Config.max_abs_value) * ((S - Config.min_db) / (-Config.min_db))
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- Config.max_abs_value,
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-Config.max_abs_value,
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Config.max_abs_value,
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)
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else:
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return torch.clip(
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Config.max_abs_value * ((S - Config.min_db) / (-Config.min_db)),
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0,
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Config.max_abs_value,
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)
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assert S.max() <= 0 and S.min() - Config.min_db >= 0
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if Config.symmetric_mels:
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return (2 * Config.max_abs_value) * (
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(S - Config.min_db) / (-Config.min_db)
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) - Config.max_abs_value
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else:
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return Config.max_abs_value * ((S - Config.min_db) / (-Config.min_db))
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def tr_amp_to_db(x):
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min_level = torch.exp(Config.min_level_db / 20 * torch.log(torch.tensor(10.0)))
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min_level = min_level.type_as(x)
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return 20 * torch.log10(torch.maximum(min_level, x))
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def normalize(S):
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if Config.allow_clipping_in_normalization:
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if Config.symmetric_mels:
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return np.clip(
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(2 * Config.max_abs_value) * ((S - Config.min_db) / (-Config.min_db))
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- Config.max_abs_value,
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-Config.max_abs_value,
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Config.max_abs_value,
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)
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else:
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return np.clip(
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Config.max_abs_value * ((S - Config.min_db) / (-Config.min_db)),
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0,
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Config.max_abs_value,
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)
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assert S.max() <= 0 and S.min() - Config.min_db >= 0
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if Config.symmetric_mels:
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return (2 * Config.max_abs_value) * (
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(S - Config.min_db) / (-Config.min_db)
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) - Config.max_abs_value
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else:
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return Config.max_abs_value * ((S - Config.min_db) / (-Config.min_db))
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def amp_to_db(x):
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min_level = np.exp(Config.min_level_db / 20 * np.log(10))
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return 20 * np.log10(np.maximum(min_level, x))
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def tr_pre(npy):
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# conditions = torch.FloatTensor(npy).type_as(npy) # to(device)
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conditions = npy.transpose(1, 2)
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l = conditions.size(-1)
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pad_tail = l % 2 + 4
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zeros = (
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torch.zeros([conditions.size()[0], Config.num_mels, pad_tail]).type_as(
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conditions
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)
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+ -4.0
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)
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return torch.cat([conditions, zeros], dim=-1)
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def pre(npy):
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conditions = npy
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## padding tail
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if type(conditions) == np.ndarray:
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conditions = torch.FloatTensor(conditions).unsqueeze(0)
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else:
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conditions = torch.FloatTensor(conditions.float()).unsqueeze(0)
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conditions = conditions.transpose(1, 2)
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l = conditions.size(-1)
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pad_tail = l % 2 + 4
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zeros = torch.zeros([1, Config.num_mels, pad_tail]) + -4.0
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return torch.cat([conditions, zeros], dim=-1)
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def load_try(state, model):
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model_dict = model.state_dict()
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try:
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model_dict.update(state)
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model.load_state_dict(model_dict)
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except RuntimeError as e:
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print(str(e))
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model_dict = model.state_dict()
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for k, v in state.items():
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model_dict[k] = v
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model.load_state_dict(model_dict)
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def load_checkpoint(checkpoint_path, device):
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checkpoint = torch.load(checkpoint_path, map_location=device)
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return checkpoint
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def build_mel_basis():
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return librosa.filters.mel(
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sr=Config.sample_rate,
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n_fft=Config.n_fft,
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htk=True,
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n_mels=Config.num_mels,
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fmin=0,
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fmax=int(Config.sample_rate // 2)
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)
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def linear_to_mel(spectogram):
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_mel_basis = build_mel_basis()
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return np.dot(_mel_basis, spectogram)
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if __name__ == "__main__":
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data = torch.randn((3, 5, 100))
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b = normalize(amp_to_db(data.numpy()))
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a = tr_normalize(tr_amp_to_db(data)).numpy()
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print(a - b)
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