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import torch
import torch.utils.data
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data, sampling_rate
def _dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def _spectral_normalize_torch(magnitudes):
output = _dynamic_range_compression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def mel_spectrogram(
y,
n_fft,
num_mels,
sampling_rate,
hop_size,
win_size,
fmin,
fmax,
center=False,
output_energy=False,
):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global mel_basis, hann_window
if fmax not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
mel_spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
mel_spec = _spectral_normalize_torch(mel_spec)
if output_energy:
energy = torch.norm(spec, dim=1)
return mel_spec, energy
else:
return mel_spec
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