128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
import torch
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import torch.utils.data
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from librosa.filters import mel as librosa_mel_fn
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import logging
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logger = logging.getLogger(__name__)
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MAX_WAV_VALUE = 32768.0
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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"""
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PARAMS
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------
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C: compression factor
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"""
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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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"""
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PARAMS
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------
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C: compression factor used to compress
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"""
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return torch.exp(x) / C
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def spectral_normalize_torch(magnitudes):
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return dynamic_range_compression_torch(magnitudes)
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def spectral_de_normalize_torch(magnitudes):
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return dynamic_range_decompression_torch(magnitudes)
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# Reusable banks
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mel_basis = {}
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hann_window = {}
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def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
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"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
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Args:
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y :: (B, T) - Audio waveforms
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n_fft
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sampling_rate
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hop_size
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win_size
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center
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Returns:
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:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
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"""
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# Window - Cache if needed
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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(
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dtype=y.dtype, device=y.device
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)
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# Padding
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
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mode="reflect",
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)
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y = y.squeeze(1)
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# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
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spec = 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[wnsize_dtype_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,
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)
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# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
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spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
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return spec
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def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
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# MelBasis - Cache if needed
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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(
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sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
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)
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mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
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dtype=spec.dtype, device=spec.device
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)
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# Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
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melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
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melspec = spectral_normalize_torch(melspec)
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return melspec
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def mel_spectrogram_torch(
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y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
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):
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"""Convert waveform into Mel-frequency Log-amplitude spectrogram.
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Args:
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y :: (B, T) - Waveforms
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Returns:
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melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
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"""
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# Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
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spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
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# Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
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melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
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return melspec
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