%global _empty_manifest_terminate_build 0
Name:		python-torch-stoi
Version:	0.1.2
Release:	1
Summary:	Computes Short Term Objective Intelligibility in PyTorch
License:	MIT
URL:		https://github.com/mpariente/pytorch_stoi
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4a/bb/0a3122124f18d1091274af1fe59bc77218143f4fc35fa48914e89a7431e9/torch_stoi-0.1.2.tar.gz
BuildArch:	noarch


%description
## PyTorch implementation of STOI
[![Build Status][travis-badge]][travis]
[![PyPI Status](https://badge.fury.io/py/torch-stoi.svg)](https://badge.fury.io/py/torch-stoi)


Implementation of the classical and extended Short 
Term Objective Intelligibility in PyTorch.
See also [Cees Taal's website](http://www.ceestaal.nl/code/) and 
the [python implementation](https://github.com/mpariente/pystoi)

### Install
```bash
pip install torch_stoi
```

## Important warning
**This implementation is intended to be used as a loss function only.**  
It doesn't replicate the exact behavior of the original metrics
but the results should be close enough that it can be used 
as a loss function. See the Notes in the
 [`NegSTOILoss`](./torch_stoi/stoi.py) class.
 
Quantitative comparison coming soon hopefully :rocket:

### Usage
```python
import torch
from torch import nn
from torch_stoi import NegSTOILoss

sample_rate = 16000
loss_func = NegSTOILoss(sample_rate=sample_rate)
# Your nnet and optimizer definition here
nnet = nn.Module()

noisy_speech = torch.randn(2, 16000)
clean_speech = torch.randn(2, 16000)
# Estimate clean speech
est_speech = nnet(noisy_speech)
# Compute loss and backward (then step etc...)
loss_batch = loss_func(est_speech, clean_speech)
loss_batch.mean().backward()
```

### Comparing NumPy and PyTorch versions : the static test
Values obtained with the NumPy version are compared to 
the PyTorch version in the following graphs.  
##### 8kHz
Classic STOI measure

<img src="./plots/8kHzwithVAD.png" width="400"/> <img src="./plots/8kHzwoVAD.png" width="400"/>

Extended STOI measure

<img src="./plots/8kHzExtendedwithVAD.png" width="400"/> <img src="./plots/8kHzExtendedwoVAD.png" width="400">

##### 16kHz
Classic STOI measure

<img src="./plots/16kHzwithVAD.png" width="400"> <img src="./plots/16kHzwoVAD.png" width="400">

Extended STOI measure

<img src="./plots/16kHzExtendedwithVAD.png" width="400"> <img src="./plots/16kHzExtendedwoVAD.png" width="400">


16kHz signals used to compare both versions contained a lot
of silence, which explains why the match is very bad without 
VAD. 

### Comparing NumPy and PyTorch versions : Training a DNN
Coming in the near future

### References
* [1] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'A Short-Time
  Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech',
  ICASSP 2010, Texas, Dallas.
* [2] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'An Algorithm for
  Intelligibility Prediction of Time-Frequency Weighted Noisy Speech',
  IEEE Transactions on Audio, Speech, and Language Processing, 2011.
* [3] J. Jensen and C. H. Taal, 'An Algorithm for Predicting the
  Intelligibility of Speech Masked by Modulated Noise Maskers',
  IEEE Transactions on Audio, Speech and Language Processing, 2016.


[travis]: https://travis-ci.com/mpariente/pytorch_stoi
[travis-badge]: https://travis-ci.com/mpariente/pytorch_stoi.svg?branch=master

%package -n python3-torch-stoi
Summary:	Computes Short Term Objective Intelligibility in PyTorch
Provides:	python-torch-stoi
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-torch-stoi
## PyTorch implementation of STOI
[![Build Status][travis-badge]][travis]
[![PyPI Status](https://badge.fury.io/py/torch-stoi.svg)](https://badge.fury.io/py/torch-stoi)


Implementation of the classical and extended Short 
Term Objective Intelligibility in PyTorch.
See also [Cees Taal's website](http://www.ceestaal.nl/code/) and 
the [python implementation](https://github.com/mpariente/pystoi)

### Install
```bash
pip install torch_stoi
```

## Important warning
**This implementation is intended to be used as a loss function only.**  
It doesn't replicate the exact behavior of the original metrics
but the results should be close enough that it can be used 
as a loss function. See the Notes in the
 [`NegSTOILoss`](./torch_stoi/stoi.py) class.
 
Quantitative comparison coming soon hopefully :rocket:

### Usage
```python
import torch
from torch import nn
from torch_stoi import NegSTOILoss

sample_rate = 16000
loss_func = NegSTOILoss(sample_rate=sample_rate)
# Your nnet and optimizer definition here
nnet = nn.Module()

noisy_speech = torch.randn(2, 16000)
clean_speech = torch.randn(2, 16000)
# Estimate clean speech
est_speech = nnet(noisy_speech)
# Compute loss and backward (then step etc...)
loss_batch = loss_func(est_speech, clean_speech)
loss_batch.mean().backward()
```

### Comparing NumPy and PyTorch versions : the static test
Values obtained with the NumPy version are compared to 
the PyTorch version in the following graphs.  
##### 8kHz
Classic STOI measure

<img src="./plots/8kHzwithVAD.png" width="400"/> <img src="./plots/8kHzwoVAD.png" width="400"/>

Extended STOI measure

<img src="./plots/8kHzExtendedwithVAD.png" width="400"/> <img src="./plots/8kHzExtendedwoVAD.png" width="400">

##### 16kHz
Classic STOI measure

<img src="./plots/16kHzwithVAD.png" width="400"> <img src="./plots/16kHzwoVAD.png" width="400">

Extended STOI measure

<img src="./plots/16kHzExtendedwithVAD.png" width="400"> <img src="./plots/16kHzExtendedwoVAD.png" width="400">


16kHz signals used to compare both versions contained a lot
of silence, which explains why the match is very bad without 
VAD. 

### Comparing NumPy and PyTorch versions : Training a DNN
Coming in the near future

### References
* [1] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'A Short-Time
  Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech',
  ICASSP 2010, Texas, Dallas.
* [2] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'An Algorithm for
  Intelligibility Prediction of Time-Frequency Weighted Noisy Speech',
  IEEE Transactions on Audio, Speech, and Language Processing, 2011.
* [3] J. Jensen and C. H. Taal, 'An Algorithm for Predicting the
  Intelligibility of Speech Masked by Modulated Noise Maskers',
  IEEE Transactions on Audio, Speech and Language Processing, 2016.


[travis]: https://travis-ci.com/mpariente/pytorch_stoi
[travis-badge]: https://travis-ci.com/mpariente/pytorch_stoi.svg?branch=master

%package help
Summary:	Development documents and examples for torch-stoi
Provides:	python3-torch-stoi-doc
%description help
## PyTorch implementation of STOI
[![Build Status][travis-badge]][travis]
[![PyPI Status](https://badge.fury.io/py/torch-stoi.svg)](https://badge.fury.io/py/torch-stoi)


Implementation of the classical and extended Short 
Term Objective Intelligibility in PyTorch.
See also [Cees Taal's website](http://www.ceestaal.nl/code/) and 
the [python implementation](https://github.com/mpariente/pystoi)

### Install
```bash
pip install torch_stoi
```

## Important warning
**This implementation is intended to be used as a loss function only.**  
It doesn't replicate the exact behavior of the original metrics
but the results should be close enough that it can be used 
as a loss function. See the Notes in the
 [`NegSTOILoss`](./torch_stoi/stoi.py) class.
 
Quantitative comparison coming soon hopefully :rocket:

### Usage
```python
import torch
from torch import nn
from torch_stoi import NegSTOILoss

sample_rate = 16000
loss_func = NegSTOILoss(sample_rate=sample_rate)
# Your nnet and optimizer definition here
nnet = nn.Module()

noisy_speech = torch.randn(2, 16000)
clean_speech = torch.randn(2, 16000)
# Estimate clean speech
est_speech = nnet(noisy_speech)
# Compute loss and backward (then step etc...)
loss_batch = loss_func(est_speech, clean_speech)
loss_batch.mean().backward()
```

### Comparing NumPy and PyTorch versions : the static test
Values obtained with the NumPy version are compared to 
the PyTorch version in the following graphs.  
##### 8kHz
Classic STOI measure

<img src="./plots/8kHzwithVAD.png" width="400"/> <img src="./plots/8kHzwoVAD.png" width="400"/>

Extended STOI measure

<img src="./plots/8kHzExtendedwithVAD.png" width="400"/> <img src="./plots/8kHzExtendedwoVAD.png" width="400">

##### 16kHz
Classic STOI measure

<img src="./plots/16kHzwithVAD.png" width="400"> <img src="./plots/16kHzwoVAD.png" width="400">

Extended STOI measure

<img src="./plots/16kHzExtendedwithVAD.png" width="400"> <img src="./plots/16kHzExtendedwoVAD.png" width="400">


16kHz signals used to compare both versions contained a lot
of silence, which explains why the match is very bad without 
VAD. 

### Comparing NumPy and PyTorch versions : Training a DNN
Coming in the near future

### References
* [1] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'A Short-Time
  Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech',
  ICASSP 2010, Texas, Dallas.
* [2] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'An Algorithm for
  Intelligibility Prediction of Time-Frequency Weighted Noisy Speech',
  IEEE Transactions on Audio, Speech, and Language Processing, 2011.
* [3] J. Jensen and C. H. Taal, 'An Algorithm for Predicting the
  Intelligibility of Speech Masked by Modulated Noise Maskers',
  IEEE Transactions on Audio, Speech and Language Processing, 2016.


[travis]: https://travis-ci.com/mpariente/pytorch_stoi
[travis-badge]: https://travis-ci.com/mpariente/pytorch_stoi.svg?branch=master

%prep
%autosetup -n torch-stoi-0.1.2

%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-stoi -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
- Package Spec generated