Refactor mel module (#132)

* Refactor wave-to-mel

* Add docstring on mel

* Refactor mel module import and variable names

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
 
 
@@ -35,25 +25,38 @@ 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
 mel_basis = {}
 hann_window = {}
 
 
 def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
+
+    Args:
+        y             :: (B, T) - Audio waveforms
+        n_fft
+        sampling_rate
+        hop_size
+        win_size
+        center
+    Returns:
+        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
+    """
+    # Validation
     if torch.min(y) < -1.0:
         print("min value is ", torch.min(y))
     if torch.max(y) > 1.0:
         print("max value is ", torch.max(y))
 
+    # Window - Cache if needed
     global hann_window
     dtype_device = str(y.dtype) + "_" + str(y.device)
     wnsize_dtype_device = str(win_size) + "_" + dtype_device
@@ -62,6 +65,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
             dtype=y.dtype, device=y.device
         )
 
+    # Padding
     y = torch.nn.functional.pad(
         y.unsqueeze(1),
         (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
@@ -69,6 +73,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     )
     y = y.squeeze(1)
 
+    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
     spec = torch.stft(
         y,
         n_fft,
@@ -82,11 +87,13 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
         return_complex=False,
     )
 
+    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
     return spec
 
 
 def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    # MelBasis - Cache if needed
     global mel_basis
     dtype_device = str(spec.dtype) + "_" + str(spec.device)
     fmax_dtype_device = str(fmax) + "_" + dtype_device
@@ -95,66 +102,27 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
         mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
             dtype=spec.dtype, device=spec.device
         )
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
+
+    # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
+    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
 ):
-    if torch.min(y) < -1.0:
-        print("min value is ", torch.min(y))
-    if torch.max(y) > 1.0:
-        print("max value is ", torch.max(y))
+    """Convert waveform into Mel-frequency Log-amplitude spectrogram.
 
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=y.dtype, device=y.device
-        )
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
-            dtype=y.dtype, device=y.device
-        )
+    Args:
+        y       :: (B, T)           - Waveforms
+    Returns:
+        melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
+    """
+    # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
+    spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
 
-    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)
+    # 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)
 
-    # spec = torch.stft(
-    #     y,
-    #     n_fft,
-    #     hop_length=hop_size,
-    #     win_length=win_size,
-    #     window=hann_window[wnsize_dtype_device],
-    #     center=center,
-    #     pad_mode="reflect",
-    #     normalized=False,
-    #     onesided=True,
-    # )
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
+    return melspec