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|
%global _empty_manifest_terminate_build 0
Name: python-torch-stft
Version: 0.1.4
Release: 1
Summary: An STFT/iSTFT for PyTorch
License: BSD License
URL: https://github.com/pseeth/torch-stft
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c3/bd/ea6bc20ccaf1008c62c3b235eeb76649190e155ad43c0a06ed6bda2afd5f/torch_stft-0.1.4.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-librosa
%description
[](https://travis-ci.com/pseeth/pytorch-stft)
# STFT/iSTFT in PyTorch
An STFT/iSTFT written up in PyTorch using 1D Convolutions. Requirements are a recent version PyTorch, numpy, and librosa (for loading audio in test_stft.py). Thanks to Shrikant Venkataramani for sharing code this was based off of and Rafael Valle for catching bugs and adding the proper windowing logic. Uses Python 3.
## Installation
Install easily with pip:
```
pip install torch_stft
```
## Usage
```
import torch
from torch_stft import STFT
import numpy as np
import librosa
import matplotlib.pyplot as plt
audio = librosa.load(librosa.util.example_audio_file(), duration=10.0, offset=30)[0]
device = 'cpu'
filter_length = 1024
hop_length = 256
win_length = 1024 # doesn't need to be specified. if not specified, it's the same as filter_length
window = 'hann'
audio = torch.FloatTensor(audio)
audio = audio.unsqueeze(0)
audio = audio.to(device)
stft = STFT(
filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
window=window
).to(device)
magnitude, phase = stft.transform(audio)
output = stft.inverse(magnitude, phase)
output = output.cpu().data.numpy()[..., :]
audio = audio.cpu().data.numpy()[..., :]
print(np.mean((output - audio) ** 2)) # on order of 1e-16
```
Output of [`compare_stft.py`](compare_stft.py):
## Tests
Test it by just cloning this repo and running
```
pip install -r requirements.txt
python -m pytest .
```
Unfortunately, since it's implemented with 1D Convolutions, some filter_length/hop_length
combinations can result in out of memory errors on your GPU when run on sufficiently large input.
## Contributing
Pull requests welcome.
%package -n python3-torch-stft
Summary: An STFT/iSTFT for PyTorch
Provides: python-torch-stft
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torch-stft
[](https://travis-ci.com/pseeth/pytorch-stft)
# STFT/iSTFT in PyTorch
An STFT/iSTFT written up in PyTorch using 1D Convolutions. Requirements are a recent version PyTorch, numpy, and librosa (for loading audio in test_stft.py). Thanks to Shrikant Venkataramani for sharing code this was based off of and Rafael Valle for catching bugs and adding the proper windowing logic. Uses Python 3.
## Installation
Install easily with pip:
```
pip install torch_stft
```
## Usage
```
import torch
from torch_stft import STFT
import numpy as np
import librosa
import matplotlib.pyplot as plt
audio = librosa.load(librosa.util.example_audio_file(), duration=10.0, offset=30)[0]
device = 'cpu'
filter_length = 1024
hop_length = 256
win_length = 1024 # doesn't need to be specified. if not specified, it's the same as filter_length
window = 'hann'
audio = torch.FloatTensor(audio)
audio = audio.unsqueeze(0)
audio = audio.to(device)
stft = STFT(
filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
window=window
).to(device)
magnitude, phase = stft.transform(audio)
output = stft.inverse(magnitude, phase)
output = output.cpu().data.numpy()[..., :]
audio = audio.cpu().data.numpy()[..., :]
print(np.mean((output - audio) ** 2)) # on order of 1e-16
```
Output of [`compare_stft.py`](compare_stft.py):
## Tests
Test it by just cloning this repo and running
```
pip install -r requirements.txt
python -m pytest .
```
Unfortunately, since it's implemented with 1D Convolutions, some filter_length/hop_length
combinations can result in out of memory errors on your GPU when run on sufficiently large input.
## Contributing
Pull requests welcome.
%package help
Summary: Development documents and examples for torch-stft
Provides: python3-torch-stft-doc
%description help
[](https://travis-ci.com/pseeth/pytorch-stft)
# STFT/iSTFT in PyTorch
An STFT/iSTFT written up in PyTorch using 1D Convolutions. Requirements are a recent version PyTorch, numpy, and librosa (for loading audio in test_stft.py). Thanks to Shrikant Venkataramani for sharing code this was based off of and Rafael Valle for catching bugs and adding the proper windowing logic. Uses Python 3.
## Installation
Install easily with pip:
```
pip install torch_stft
```
## Usage
```
import torch
from torch_stft import STFT
import numpy as np
import librosa
import matplotlib.pyplot as plt
audio = librosa.load(librosa.util.example_audio_file(), duration=10.0, offset=30)[0]
device = 'cpu'
filter_length = 1024
hop_length = 256
win_length = 1024 # doesn't need to be specified. if not specified, it's the same as filter_length
window = 'hann'
audio = torch.FloatTensor(audio)
audio = audio.unsqueeze(0)
audio = audio.to(device)
stft = STFT(
filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
window=window
).to(device)
magnitude, phase = stft.transform(audio)
output = stft.inverse(magnitude, phase)
output = output.cpu().data.numpy()[..., :]
audio = audio.cpu().data.numpy()[..., :]
print(np.mean((output - audio) ** 2)) # on order of 1e-16
```
Output of [`compare_stft.py`](compare_stft.py):
## Tests
Test it by just cloning this repo and running
```
pip install -r requirements.txt
python -m pytest .
```
Unfortunately, since it's implemented with 1D Convolutions, some filter_length/hop_length
combinations can result in out of memory errors on your GPU when run on sufficiently large input.
## Contributing
Pull requests welcome.
%prep
%autosetup -n torch-stft-0.1.4
%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-torch-stft -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.4-1
- Package Spec generated
|
