| import numpy as np
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| import torch
|
| import torch.utils.data
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| from librosa.filters import mel as librosa_mel_fn
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| from scipy.io.wavfile import read
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|
|
| MAX_WAV_VALUE = 32768.0
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|
|
|
|
| def load_wav(full_path):
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| sampling_rate, data = read(full_path)
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| return data, sampling_rate
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|
|
|
|
| def dynamic_range_compression(x, C=1, clip_val=1e-5):
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| return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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|
|
|
|
| def dynamic_range_decompression(x, C=1):
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| return np.exp(x) / C
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|
|
|
|
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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| return torch.log(torch.clamp(x, min=clip_val) * C)
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|
|
|
|
| def dynamic_range_decompression_torch(x, C=1):
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| return torch.exp(x) / C
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|
|
|
|
| def spectral_normalize_torch(magnitudes):
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| output = dynamic_range_compression_torch(magnitudes)
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| return output
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|
|
|
|
| def spectral_de_normalize_torch(magnitudes):
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| output = dynamic_range_decompression_torch(magnitudes)
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| return output
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|
|
|
|
| mel_basis = {}
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| hann_window = {}
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|
|
|
|
| def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
| if torch.min(y) < -1.0:
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| print("min value is ", torch.min(y))
|
| if torch.max(y) > 1.0:
|
| print("max value is ", torch.max(y))
|
|
|
| global mel_basis, hann_window
|
| if f"{str(sampling_rate)}_{str(fmax)}_{str(y.device)}" not in mel_basis:
|
| mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
| mel_basis[str(sampling_rate) + "_" + str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
| hann_window[str(sampling_rate) + "_" + str(y.device)] = torch.hann_window(win_size).to(y.device)
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|
|
| y = torch.nn.functional.pad(
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| y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
|
| )
|
| y = y.squeeze(1)
|
|
|
| spec = torch.view_as_real(
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| torch.stft(
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| y,
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| n_fft,
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| hop_length=hop_size,
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| win_length=win_size,
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| window=hann_window[str(sampling_rate) + "_" + str(y.device)],
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| center=center,
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| pad_mode="reflect",
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| normalized=False,
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| onesided=True,
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| return_complex=True,
|
| )
|
| )
|
|
|
| spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
|
|
|
| spec = torch.matmul(mel_basis[str(sampling_rate) + "_" + str(fmax) + "_" + str(y.device)], spec)
|
| spec = spectral_normalize_torch(spec)
|
|
|
| return spec
|
|
|
