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|
%global _empty_manifest_terminate_build 0
Name: python-torchlibrosa
Version: 0.1.0
Release: 1
Summary: PyTorch implemention of part of librosa functions.
License: MIT License
URL: https://github.com/qiuqiangkong/torchlibrosa
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a4/67/e4c79da3f15777b9bc2b655d47dac553fc31e40360500fef5e66d6877ce8/torchlibrosa-0.1.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-librosa
%description
# TorchLibrosa: PyTorch implementation of Librosa
This codebase provides PyTorch implementation of some librosa functions. If users previously used for training cpu-extracted features from librosa, but want to add GPU acceleration during training and evaluation, TorchLibrosa will provide almost identical features to standard torchlibrosa functions (numerical difference less than 1e-5).
## Install
```bash
$ pip install torchlibrosa
```
## Examples 1
Extract Log mel spectrogram with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# TorchLibrosa feature extractor the same as librosa.feature.melspectrogram()
feature_extractor = torch.nn.Sequential(
tl.Spectrogram(
hop_length=hop_length,
win_length=win_length,
), tl.LogmelFilterBank(
sr=sample_rate,
n_mels=n_mels,
is_log=False, # Default is true
))
batch_feature = feature_extractor(batch_audio) # (batch_size, 1, time_steps, mel_bins)
```
## Examples 2
Extracting spectrogram, then log mel spectrogram, STFT and ISTFT with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# Spectrogram
spectrogram_extractor = tl.Spectrogram(n_fft=win_length, hop_length=hop_length)
sp = spectrogram_extractor.forward(batch_audio) # (batch_size, 1, time_steps, freq_bins)
# Log mel spectrogram
logmel_extractor = tl.LogmelFilterBank(sr=sample_rate, n_fft=win_length, n_mels=n_mels)
logmel = logmel_extractor.forward(sp) # (batch_size, 1, time_steps, mel_bins)
# STFT
stft_extractor = tl.STFT(n_fft=win_length, hop_length=hop_length)
(real, imag) = stft_extractor.forward(batch_audio)
# real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins) #
# ISTFT
istft_extractor = tl.ISTFT(n_fft=win_length, hop_length=hop_length)
y = istft_extractor.forward(real, imag, length=batch_audio.shape[-1]) # (batch_size, samples_num)
```
## Example 3
Check the compability of TorchLibrosa to Librosa. The numerical difference should be less than 1e-5.
```python
python3 torchlibrosa/stft.py --device='cuda' # --device='cpu' | 'cuda'
```
## Contact
Qiuqiang Kong, qiuqiangkong@gmail.com
## Cite
[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.
## External links
Other related repos include:
torchaudio: https://github.com/pytorch/audio
Asteroid-filterbanks: https://github.com/asteroid-team/asteroid-filterbanks
Kapre: https://github.com/keunwoochoi/kapre
%package -n python3-torchlibrosa
Summary: PyTorch implemention of part of librosa functions.
Provides: python-torchlibrosa
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torchlibrosa
# TorchLibrosa: PyTorch implementation of Librosa
This codebase provides PyTorch implementation of some librosa functions. If users previously used for training cpu-extracted features from librosa, but want to add GPU acceleration during training and evaluation, TorchLibrosa will provide almost identical features to standard torchlibrosa functions (numerical difference less than 1e-5).
## Install
```bash
$ pip install torchlibrosa
```
## Examples 1
Extract Log mel spectrogram with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# TorchLibrosa feature extractor the same as librosa.feature.melspectrogram()
feature_extractor = torch.nn.Sequential(
tl.Spectrogram(
hop_length=hop_length,
win_length=win_length,
), tl.LogmelFilterBank(
sr=sample_rate,
n_mels=n_mels,
is_log=False, # Default is true
))
batch_feature = feature_extractor(batch_audio) # (batch_size, 1, time_steps, mel_bins)
```
## Examples 2
Extracting spectrogram, then log mel spectrogram, STFT and ISTFT with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# Spectrogram
spectrogram_extractor = tl.Spectrogram(n_fft=win_length, hop_length=hop_length)
sp = spectrogram_extractor.forward(batch_audio) # (batch_size, 1, time_steps, freq_bins)
# Log mel spectrogram
logmel_extractor = tl.LogmelFilterBank(sr=sample_rate, n_fft=win_length, n_mels=n_mels)
logmel = logmel_extractor.forward(sp) # (batch_size, 1, time_steps, mel_bins)
# STFT
stft_extractor = tl.STFT(n_fft=win_length, hop_length=hop_length)
(real, imag) = stft_extractor.forward(batch_audio)
# real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins) #
# ISTFT
istft_extractor = tl.ISTFT(n_fft=win_length, hop_length=hop_length)
y = istft_extractor.forward(real, imag, length=batch_audio.shape[-1]) # (batch_size, samples_num)
```
## Example 3
Check the compability of TorchLibrosa to Librosa. The numerical difference should be less than 1e-5.
```python
python3 torchlibrosa/stft.py --device='cuda' # --device='cpu' | 'cuda'
```
## Contact
Qiuqiang Kong, qiuqiangkong@gmail.com
## Cite
[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.
## External links
Other related repos include:
torchaudio: https://github.com/pytorch/audio
Asteroid-filterbanks: https://github.com/asteroid-team/asteroid-filterbanks
Kapre: https://github.com/keunwoochoi/kapre
%package help
Summary: Development documents and examples for torchlibrosa
Provides: python3-torchlibrosa-doc
%description help
# TorchLibrosa: PyTorch implementation of Librosa
This codebase provides PyTorch implementation of some librosa functions. If users previously used for training cpu-extracted features from librosa, but want to add GPU acceleration during training and evaluation, TorchLibrosa will provide almost identical features to standard torchlibrosa functions (numerical difference less than 1e-5).
## Install
```bash
$ pip install torchlibrosa
```
## Examples 1
Extract Log mel spectrogram with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# TorchLibrosa feature extractor the same as librosa.feature.melspectrogram()
feature_extractor = torch.nn.Sequential(
tl.Spectrogram(
hop_length=hop_length,
win_length=win_length,
), tl.LogmelFilterBank(
sr=sample_rate,
n_mels=n_mels,
is_log=False, # Default is true
))
batch_feature = feature_extractor(batch_audio) # (batch_size, 1, time_steps, mel_bins)
```
## Examples 2
Extracting spectrogram, then log mel spectrogram, STFT and ISTFT with TorchLibrosa.
```python
import torch
import torchlibrosa as tl
batch_size = 16
sample_rate = 22050
win_length = 2048
hop_length = 512
n_mels = 128
batch_audio = torch.empty(batch_size, sample_rate).uniform_(-1, 1) # (batch_size, sample_rate)
# Spectrogram
spectrogram_extractor = tl.Spectrogram(n_fft=win_length, hop_length=hop_length)
sp = spectrogram_extractor.forward(batch_audio) # (batch_size, 1, time_steps, freq_bins)
# Log mel spectrogram
logmel_extractor = tl.LogmelFilterBank(sr=sample_rate, n_fft=win_length, n_mels=n_mels)
logmel = logmel_extractor.forward(sp) # (batch_size, 1, time_steps, mel_bins)
# STFT
stft_extractor = tl.STFT(n_fft=win_length, hop_length=hop_length)
(real, imag) = stft_extractor.forward(batch_audio)
# real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins) #
# ISTFT
istft_extractor = tl.ISTFT(n_fft=win_length, hop_length=hop_length)
y = istft_extractor.forward(real, imag, length=batch_audio.shape[-1]) # (batch_size, samples_num)
```
## Example 3
Check the compability of TorchLibrosa to Librosa. The numerical difference should be less than 1e-5.
```python
python3 torchlibrosa/stft.py --device='cuda' # --device='cpu' | 'cuda'
```
## Contact
Qiuqiang Kong, qiuqiangkong@gmail.com
## Cite
[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.
## External links
Other related repos include:
torchaudio: https://github.com/pytorch/audio
Asteroid-filterbanks: https://github.com/asteroid-team/asteroid-filterbanks
Kapre: https://github.com/keunwoochoi/kapre
%prep
%autosetup -n torchlibrosa-0.1.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-torchlibrosa -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
- Package Spec generated
|
