Compare spectrograms of torchaudio and librosa

compare_mel_filterbank.py

import torch
import torchaudio
import librosa

import matplotlib.pyplot as plt
from torchaudio.functional import create_fb_matrix

n_fft = 2048
n_mels = 128
sample_rate = 6000

torchaudio_mel = create_fb_matrix(
    int(n_fft // 2 + 1),
    n_mels=n_mels,
    f_min=0.,
    f_max=sample_rate/2.,
    sample_rate=sample_rate,
    norm='slaney'
)

librosa_mel = librosa.filters.mel(
    sample_rate,
    n_fft,
    n_mels=n_mels,
    fmin=0.,
    fmax=sample_rate/2.,
    norm='slaney',
    htk=True,
).T

mse = ((torchaudio_mel - librosa_mel) ** 2).mean()

print(f'MSE:\t{mse}')

fig, axs = plt.subplots(1, 2, figsize=(10, 10))
fig.suptitle('mel-filter bank')

axs[0].set_title('torchaudio[slaney]')
axs[0].imshow(torchaudio_mel, aspect='auto')
axs[0].set_ylabel('frequency bin')
axs[0].set_xlabel('mel bin')

axs[1].set_title('librosa[htk + slaney]')
axs[1].imshow(librosa_mel, aspect='auto')
axs[0].set_ylabel('frequency bin')
axs[1].set_xlabel('mel bin')

plt.show()

compare_melspectrogram.py

import librosa

import matplotlib.pyplot as plt
import torch
from torchaudio.transforms import MelSpectrogram

n_fft = 2048
win_len = None
hop_len = 512
n_mels = 128
sample_rate = 6000

path = 'test/torchaudio_unittest/assets/steam-train-whistle-daniel_simon.wav'

waveform, sample_rate = librosa.load(path, sr=sample_rate)
waveform = torch.Tensor(waveform)

torchaudio_melspec = MelSpectrogram(
    sample_rate=sample_rate,
    n_fft=n_fft,
    win_length=win_len,
    hop_length=hop_len,
    center=True,
    pad_mode="reflect",
    power=2.0,
    norm='slaney',
    onesided=True,
    n_mels=n_mels,
)(waveform)
librosa_melspec = librosa.feature.melspectrogram(
    waveform.numpy(),
    sr=sample_rate,
    n_fft=n_fft,
    hop_length=hop_len,
    win_length=win_len,
    center=True,
    pad_mode="reflect",
    power=2.0,
    n_mels=n_mels,
    norm='slaney',
    htk=True,
)

mse = ((torchaudio_melspec - librosa_melspec) ** 2).mean()

print(f'MSE:\t{mse}')

fig, axs = plt.subplots(1, 2, figsize=(20, 5))
fig.suptitle('Mel Spectrogram')

axs[0].set_title('torchaudio')
axs[0].set_ylabel('mel bin')
axs[0].set_xlabel('frame')
axs[0].imshow(librosa.power_to_db(torchaudio_melspec), aspect='auto')

axs[1].set_title('librosa')
axs[1].set_ylabel('mel bin')
axs[1].set_xlabel('frame')
axs[1].imshow(librosa.power_to_db(librosa_melspec), aspect='auto')

plt.show()

compare_spectrogram.py

import librosa.core.spectrum

import matplotlib.pyplot as plt
import torch
from torchaudio.transforms import Spectrogram

n_fft = 2048
win_len = None
hop_len = 512
sample_rate = 6000

path = 'test/torchaudio_unittest/assets/steam-train-whistle-daniel_simon.wav'

waveform, _ = librosa.load(path, sr=sample_rate)
waveform = torch.Tensor(waveform)

torchaudio_spec = Spectrogram(
    n_fft=n_fft,
    win_length=win_len,
    hop_length=hop_len,
    center=True,
    pad_mode="reflect",
    power=2.0,
)(waveform)
librosa_spec, _ = librosa.core.spectrum._spectrogram(
    waveform.numpy(),
    n_fft=n_fft,
    hop_length=hop_len,
    win_length=win_len,
    center=True,
    pad_mode="reflect",
    power=2.0,
)

mse = ((torchaudio_spec - librosa_spec) ** 2).mean()

print(f'MSE:\t{mse}')

fig, axs = plt.subplots(1, 2, figsize=(20, 5))
fig.suptitle('mel-Kernel')

axs[0].set_title('torchaudio')
axs[0].set_ylabel('mel bin')
axs[0].set_xlabel('frame')
axs[0].imshow(librosa.power_to_db(torchaudio_spec), aspect='auto')

axs[1].set_title('librosa')
axs[1].set_ylabel('mel bin')
axs[1].set_xlabel('frame')
axs[1].imshow(librosa.power_to_db(librosa_spec), aspect='auto')

plt.show()

Are there any noticeable differences you've observed?

@jerpint

Not among the set of compatible parameters. Please refer to https://pytorch.org/audio/0.10.0/tutorials/audio_feature_extractions_tutorial.html#mel-filter-bank for a better explanation.