This codebase provides PyTorch implementation of some librosa functions. The functions can run on GPU. For example, users can extract log mel spectrogram on GPU. The numerical difference of this codebase and librosa is less than 1e-6.
$ pip install torchlibrosa
Here are examples of extracting spectrogram, log mel spectrogram, STFT and ISTFT using torchlibrosa.
import torch
import torchlibrosa as tl
# Data
x = torch.zeros(1, 32000) # (batch_size, samples_num)
# Spectrogram
spectrogram_extractor = tl.stft.Spectrogram(n_fft=1024, hop_length=250)
sp = spectrogram_extractor.forward(x) # (batch_size, 1, time_steps, freq_bins)
# Log mel spectrogram
logmel_extractor = tl.stft.LogmelFilterBank(sr=32000, n_fft=1024, n_mels=64)
logmel = logmel_extractor.forward(sp) # (batch_size, 1, time_steps, freq_bins)
# STFT
stft_extractor = tl.stft.STFT(n_fft=1024, hop_length=250)
(real, imag) = stft_extractor.forward(x)
"""real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins)"""
# ISTFT
istft_extractor = tl.stft.ISTFT(n_fft=1024, hop_length=250)
y = istft_extractor.forward(real, imag, x.shape[-1]) # (batch_size, samples_num)
python3 torchlibrosa/stft.py
[1] Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D. Plumbley. "PANNs: Large-scale pretrained audio neural networks for audio pattern recognition." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 2880-2894.
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