automatic import of python-torch-stoi

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+%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