99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
import numpy as np
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import torch
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import torch.utils.data
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from scipy.io.wavfile import read
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from librosa.filters import mel as librosa_mel_fn
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MAX_WAV_VALUE = 32768.0
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mel_basis = {}
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hann_window = {}
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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 load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
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def spectrogram(y, n_fft, hop_size, win_size, center=False):
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if torch.min(y) < -1.:
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print('min value is ', torch.min(y))
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if torch.max(y) > 1.:
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print('max value is ', torch.max(y))
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global hann_window
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dtype_device = str(y.dtype) + '_' + str(y.device)
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wnsize_dtype_device = str(win_size) + '_' + dtype_device
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if wnsize_dtype_device not in hann_window:
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hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
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y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
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y = y.squeeze(1)
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spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
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return spec
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def spec_to_mel(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
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global mel_basis
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dtype_device = str(spec.dtype) + '_' + str(spec.device)
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fmax_dtype_device = str(fmax) + '_' + dtype_device
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if fmax_dtype_device not in mel_basis:
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mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
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mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
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spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
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spec = _spectral_normalize_torch(spec)
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return spec
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def mel_spectrogram(
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y,
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n_fft,
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num_mels,
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sampling_rate,
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hop_size,
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win_size,
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fmin,
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fmax,
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center=False,
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output_energy=False,
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):
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if torch.min(y) < -1.:
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print('min value is ', torch.min(y))
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if torch.max(y) > 1.:
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print('max value is ', torch.max(y))
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global mel_basis, hann_window
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if fmax not in mel_basis:
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mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
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mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
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hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
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y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
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y = y.squeeze(1)
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spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
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spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-6))
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mel_spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
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mel_spec = _spectral_normalize_torch(mel_spec)
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if output_energy:
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energy = torch.norm(spec, dim=1)
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return mel_spec, energy
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else:
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return mel_spec
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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 _spectral_normalize_torch(magnitudes):
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output = _dynamic_range_compression_torch(magnitudes)
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return output
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