diff --git a/train/mel_processing.py b/train/mel_processing.py index 93e17c9..0c1867b 100644 --- a/train/mel_processing.py +++ b/train/mel_processing.py @@ -1,18 +1,8 @@ -import math -import os -import random import torch -from torch import nn -import torch.nn.functional as F import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read from librosa.filters import mel as librosa_mel_fn + MAX_WAV_VALUE = 32768.0 @@ -35,13 +25,11 @@ def dynamic_range_decompression_torch(x, C=1): def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output + return dynamic_range_compression_torch(magnitudes) def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output + return dynamic_range_decompression_torch(magnitudes) # Reusable banks @@ -116,12 +104,14 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): ) # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec + melspec = torch.matmul(mel_basis[fmax_dtype_device], spec) + melspec = spectral_normalize_torch(melspec) + return melspec -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): +def mel_spectrogram_torch( + y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False +): """Convert waveform into Mel-frequency Log-amplitude spectrogram. Args: @@ -135,4 +125,4 @@ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame) melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax) - return melspec \ No newline at end of file + return melspec