automatic import of python-kaldifeat

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+/kaldifeat-1.24.tar.gz
diff --git a/python-kaldifeat.spec b/python-kaldifeat.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-kaldifeat
+Version:	1.24
+Release:	1
+Summary:	please add a summary manually as the author left a blank one
+License:	Apache licensed, as found in the LICENSE file
+URL:		https://github.com/csukuangfj/kaldifeat
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/82/44/5cbc09302a2f34ce46c4ddf96cfdd44a53d488e5507107e08fffe0385e58/kaldifeat-1.24.tar.gz
+BuildArch:	noarch
+
+
+%description
+# kaldifeat
+
+<div align="center">
+<img src="/doc/source/images/os-green.svg">
+<img src="/doc/source/images/python_ge_3.6-blue.svg">
+<img src="/doc/source/images/pytorch_ge_1.5.0-green.svg">
+<img src="/doc/source/images/cuda_ge_10.1-orange.svg">
+</div>
+
+[![Documentation Status](https://github.com/csukuangfj/kaldifeat/actions/workflows/build-doc.yml/badge.svg)](https://csukuangfj.github.io/kaldifeat/)
+
+**Documentation**: <https://csukuangfj.github.io/kaldifeat>
+
+**Note**: If you are looking for a version that does not depend on PyTorch,
+please see <https://github.com/csukuangfj/kaldi-native-fbank>
+
+<sub>
+<table>
+<tr>
+<th>Comments</th>
+<th>Options</th>
+<th>Feature Computer</th>
+<th>Usage</th>
+</tr>
+
+<tr>
+<td>FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.Fbank</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device('cuda', 0)
+opts.frame_opts.window_type = 'povey'
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.OnlineFbank</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_fbank.py">
+./kaldifeat/python/tests/test_fbank.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.Mfcc</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.MfccOptions();
+opts.num_ceps = 13
+mfcc = kaldifeat.Mfcc(opts)
+features = mfcc(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.OnlineMfcc</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_mfcc.py">
+./kaldifeat/python/tests/test_mfcc.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.Plp</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.PlpOptions();
+opts.mel_opts.num_bins = 23
+plp = kaldifeat.Plp(opts)
+features = plp(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.OnlinePlp</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_plp.py">
+./kaldifeat/python/tests/test_plp.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>Spectorgram</td>
+<td><code>kaldifeat.SpectrogramOptions</code></td>
+<td><code>kaldifeat.Spectrogram</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.SpectrogramOptions();
+print(opts)
+spectrogram = kaldifeat.Spectrogram(opts)
+features = spectrogram(wave)
+</pre>
+</td>
+</tr>
+</table>
+</sub>
+
+
+Feature extraction compatible with `Kaldi` using PyTorch, supporting
+CUDA, batch processing, chunk processing, and autograd.
+
+The following kaldi-compatible commandline tools are implemented:
+
+  - `compute-fbank-feats`
+  - `compute-mfcc-feats`
+  - `compute-plp-feats`
+  - `compute-spectrogram-feats`
+
+(**NOTE**: We will implement other types of features, e.g., Pitch, ivector, etc, soon.)
+
+**HINT**: It supports also streaming feature extractors for Fbank, MFCC, and Plp.
+
+# Usage
+
+Let us first generate a test wave using sox:
+
+```bash
+# generate a wave of 1.2 seconds, containing a sine-wave
+# swept from 300 Hz to 3300 Hz
+sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
+```
+
+**HINT**: Download [test.wav][test_wav].
+
+[test_wav]: kaldifeat/python/tests/test_data/test.wav
+
+## Fbank
+
+```python
+import torchaudio
+
+import kaldifeat
+
+filename = "./test.wav"
+wave, samp_freq = torchaudio.load(filename)
+
+wave = wave.squeeze()
+
+opts = kaldifeat.FbankOptions()
+opts.frame_opts.dither = 0
+# Yes, it has same options like `Kaldi`
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+```
+
+To compute features that are compatible with `Kaldi`, wave samples have to be
+scaled to the range `[-32768, 32768]`. **WARNING**: You don't have to do this if
+you don't care about the compatibility with `Kaldi`.
+
+The following is an example:
+
+```python
+wave *= 32768
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+print(features[:3])
+```
+
+The output is:
+
+```
+tensor([[15.0074, 21.1730, 25.5286, 24.4644, 16.6994, 13.8480, 11.2087, 11.7952,
+         10.3911, 10.4491, 10.3012,  9.8743,  9.6997,  9.3751,  9.3476,  9.3559,
+          9.1074,  9.0032,  9.0312,  8.8399,  9.0822,  8.7442,  8.4023],
+        [13.8785, 20.5647, 25.4956, 24.6966, 16.9541, 13.9163, 11.3364, 11.8449,
+         10.2565, 10.5871, 10.3484,  9.7474,  9.6123,  9.3964,  9.0695,  9.1177,
+          8.9136,  8.8425,  8.5920,  8.8315,  8.6226,  8.8605,  8.9763],
+        [13.9475, 19.9410, 25.4494, 24.9051, 17.0004, 13.9207, 11.6667, 11.8217,
+         10.3411, 10.7258, 10.0983,  9.8109,  9.6762,  9.4218,  9.1246,  8.7744,
+          9.0863,  8.7488,  8.4695,  8.6710,  8.7728,  8.7405,  8.9824]])
+```
+
+You can compute the fbank feature for the same wave with `Kaldi` using the following commands:
+
+```bash
+echo "1 test.wav" > test.scp
+compute-fbank-feats --dither=0 scp:test.scp ark,t:test.txt
+head -n4 test.txt
+```
+
+The output is:
+
+```
+1  [
+  15.00744 21.17303 25.52861 24.46438 16.69938 13.84804 11.2087 11.79517 10.3911 10.44909 10.30123 9.874329 9.699727 9.37509 9.347578 9.355928 9.107419 9.00323 9.031268 8.839916 9.082197 8.744139 8.40221
+  13.87853 20.56466 25.49562 24.69662 16.9541 13.91633 11.33638 11.84495 10.25656 10.58718 10.34841 9.747416 9.612316 9.39642 9.06955 9.117751 8.913527 8.842571 8.59212 8.831518 8.622513 8.86048 8.976251
+  13.94753 19.94101 25.4494 24.90511 17.00044 13.92074 11.66673 11.82172 10.34108 10.72575 10.09829 9.810879 9.676199 9.421767 9.124647 8.774353 9.086291 8.74897 8.469534 8.670973 8.772754 8.740549 8.982433
+```
+
+You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
+
+
+**HINT**: Download [test.scp][test_scp] and [test.txt][test_txt].
+
+[test_scp]: kaldifeat/python/tests/test_data/test.scp
+[test_txt]: kaldifeat/python/tests/test_data/test.txt
+
+
+To use GPU, you can use:
+
+```python
+import torch
+
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device("cuda", 0)
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave.to(opts.device))
+```
+
+## MFCC, PLP, Spectrogram
+
+To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
+with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively. The same goes
+for `PLP` and `Spectrogram`.
+
+Please refer to
+
+  - [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
+  - [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
+  - [kaldifeat/python/tests/test_plp.py](kaldifeat/python/tests/test_plp.py)
+  - [kaldifeat/python/tests/test_spectrogram.py](kaldifeat/python/tests/test_spectrogram.py)
+  - [kaldifeat/python/tests/test_frame_extraction_options.py](kaldifeat/python/tests/test_frame_extraction_options.py)
+  - [kaldifeat/python/tests/test_mel_bank_options.py](kaldifeat/python/tests/test_mel_bank_options.py)
+  - [kaldifeat/python/tests/test_fbank_options.py](kaldifeat/python/tests/test_fbank_options.py)
+  - [kaldifeat/python/tests/test_mfcc_options.py](kaldifeat/python/tests/test_mfcc_options.py)
+  - [kaldifeat/python/tests/test_spectrogram_options.py](kaldifeat/python/tests/test_spectrogram_options.py)
+  - [kaldifeat/python/tests/test_plp_options.py](kaldifeat/python/tests/test_plp_options.py)
+
+for more examples.
+
+**HINT**: In the examples, you can find that
+
+- ``kaldifeat`` supports batch processing as well as chunk processing
+- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
+
+# Usage in other projects
+
+## icefall
+
+[icefall](https://github.com/k2-fsa/icefall) uses kaldifeat to extract features for a pre-trained model.
+
+See <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/pretrained.py>.
+
+## k2
+
+[k2](https://github.com/k2-fsa/k2) uses kaldifeat's C++ API.
+
+See <https://github.com/k2-fsa/k2/blob/v2.0-pre/k2/torch/csrc/features.cu>.
+
+## lhotse
+
+[lhotse](https://github.com/lhotse-speech/lhotse) uses kaldifeat to extract features on GPU.
+
+See <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/kaldifeat.py>.
+
+## sherpa
+
+[sherpa](https://github.com/k2-fsa/sherpa) uses kaldifeat for streaming feature
+extraction.
+
+See <https://github.com/k2-fsa/sherpa/blob/master/sherpa/bin/pruned_stateless_emformer_rnnt2/decode.py>
+
+# Installation
+
+Refer to
+<https://csukuangfj.github.io/kaldifeat>
+for installation.
+
+%package -n python3-kaldifeat
+Summary:	please add a summary manually as the author left a blank one
+Provides:	python-kaldifeat
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-kaldifeat
+# kaldifeat
+
+<div align="center">
+<img src="/doc/source/images/os-green.svg">
+<img src="/doc/source/images/python_ge_3.6-blue.svg">
+<img src="/doc/source/images/pytorch_ge_1.5.0-green.svg">
+<img src="/doc/source/images/cuda_ge_10.1-orange.svg">
+</div>
+
+[![Documentation Status](https://github.com/csukuangfj/kaldifeat/actions/workflows/build-doc.yml/badge.svg)](https://csukuangfj.github.io/kaldifeat/)
+
+**Documentation**: <https://csukuangfj.github.io/kaldifeat>
+
+**Note**: If you are looking for a version that does not depend on PyTorch,
+please see <https://github.com/csukuangfj/kaldi-native-fbank>
+
+<sub>
+<table>
+<tr>
+<th>Comments</th>
+<th>Options</th>
+<th>Feature Computer</th>
+<th>Usage</th>
+</tr>
+
+<tr>
+<td>FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.Fbank</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device('cuda', 0)
+opts.frame_opts.window_type = 'povey'
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.OnlineFbank</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_fbank.py">
+./kaldifeat/python/tests/test_fbank.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.Mfcc</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.MfccOptions();
+opts.num_ceps = 13
+mfcc = kaldifeat.Mfcc(opts)
+features = mfcc(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.OnlineMfcc</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_mfcc.py">
+./kaldifeat/python/tests/test_mfcc.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.Plp</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.PlpOptions();
+opts.mel_opts.num_bins = 23
+plp = kaldifeat.Plp(opts)
+features = plp(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.OnlinePlp</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_plp.py">
+./kaldifeat/python/tests/test_plp.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>Spectorgram</td>
+<td><code>kaldifeat.SpectrogramOptions</code></td>
+<td><code>kaldifeat.Spectrogram</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.SpectrogramOptions();
+print(opts)
+spectrogram = kaldifeat.Spectrogram(opts)
+features = spectrogram(wave)
+</pre>
+</td>
+</tr>
+</table>
+</sub>
+
+
+Feature extraction compatible with `Kaldi` using PyTorch, supporting
+CUDA, batch processing, chunk processing, and autograd.
+
+The following kaldi-compatible commandline tools are implemented:
+
+  - `compute-fbank-feats`
+  - `compute-mfcc-feats`
+  - `compute-plp-feats`
+  - `compute-spectrogram-feats`
+
+(**NOTE**: We will implement other types of features, e.g., Pitch, ivector, etc, soon.)
+
+**HINT**: It supports also streaming feature extractors for Fbank, MFCC, and Plp.
+
+# Usage
+
+Let us first generate a test wave using sox:
+
+```bash
+# generate a wave of 1.2 seconds, containing a sine-wave
+# swept from 300 Hz to 3300 Hz
+sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
+```
+
+**HINT**: Download [test.wav][test_wav].
+
+[test_wav]: kaldifeat/python/tests/test_data/test.wav
+
+## Fbank
+
+```python
+import torchaudio
+
+import kaldifeat
+
+filename = "./test.wav"
+wave, samp_freq = torchaudio.load(filename)
+
+wave = wave.squeeze()
+
+opts = kaldifeat.FbankOptions()
+opts.frame_opts.dither = 0
+# Yes, it has same options like `Kaldi`
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+```
+
+To compute features that are compatible with `Kaldi`, wave samples have to be
+scaled to the range `[-32768, 32768]`. **WARNING**: You don't have to do this if
+you don't care about the compatibility with `Kaldi`.
+
+The following is an example:
+
+```python
+wave *= 32768
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+print(features[:3])
+```
+
+The output is:
+
+```
+tensor([[15.0074, 21.1730, 25.5286, 24.4644, 16.6994, 13.8480, 11.2087, 11.7952,
+         10.3911, 10.4491, 10.3012,  9.8743,  9.6997,  9.3751,  9.3476,  9.3559,
+          9.1074,  9.0032,  9.0312,  8.8399,  9.0822,  8.7442,  8.4023],
+        [13.8785, 20.5647, 25.4956, 24.6966, 16.9541, 13.9163, 11.3364, 11.8449,
+         10.2565, 10.5871, 10.3484,  9.7474,  9.6123,  9.3964,  9.0695,  9.1177,
+          8.9136,  8.8425,  8.5920,  8.8315,  8.6226,  8.8605,  8.9763],
+        [13.9475, 19.9410, 25.4494, 24.9051, 17.0004, 13.9207, 11.6667, 11.8217,
+         10.3411, 10.7258, 10.0983,  9.8109,  9.6762,  9.4218,  9.1246,  8.7744,
+          9.0863,  8.7488,  8.4695,  8.6710,  8.7728,  8.7405,  8.9824]])
+```
+
+You can compute the fbank feature for the same wave with `Kaldi` using the following commands:
+
+```bash
+echo "1 test.wav" > test.scp
+compute-fbank-feats --dither=0 scp:test.scp ark,t:test.txt
+head -n4 test.txt
+```
+
+The output is:
+
+```
+1  [
+  15.00744 21.17303 25.52861 24.46438 16.69938 13.84804 11.2087 11.79517 10.3911 10.44909 10.30123 9.874329 9.699727 9.37509 9.347578 9.355928 9.107419 9.00323 9.031268 8.839916 9.082197 8.744139 8.40221
+  13.87853 20.56466 25.49562 24.69662 16.9541 13.91633 11.33638 11.84495 10.25656 10.58718 10.34841 9.747416 9.612316 9.39642 9.06955 9.117751 8.913527 8.842571 8.59212 8.831518 8.622513 8.86048 8.976251
+  13.94753 19.94101 25.4494 24.90511 17.00044 13.92074 11.66673 11.82172 10.34108 10.72575 10.09829 9.810879 9.676199 9.421767 9.124647 8.774353 9.086291 8.74897 8.469534 8.670973 8.772754 8.740549 8.982433
+```
+
+You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
+
+
+**HINT**: Download [test.scp][test_scp] and [test.txt][test_txt].
+
+[test_scp]: kaldifeat/python/tests/test_data/test.scp
+[test_txt]: kaldifeat/python/tests/test_data/test.txt
+
+
+To use GPU, you can use:
+
+```python
+import torch
+
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device("cuda", 0)
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave.to(opts.device))
+```
+
+## MFCC, PLP, Spectrogram
+
+To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
+with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively. The same goes
+for `PLP` and `Spectrogram`.
+
+Please refer to
+
+  - [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
+  - [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
+  - [kaldifeat/python/tests/test_plp.py](kaldifeat/python/tests/test_plp.py)
+  - [kaldifeat/python/tests/test_spectrogram.py](kaldifeat/python/tests/test_spectrogram.py)
+  - [kaldifeat/python/tests/test_frame_extraction_options.py](kaldifeat/python/tests/test_frame_extraction_options.py)
+  - [kaldifeat/python/tests/test_mel_bank_options.py](kaldifeat/python/tests/test_mel_bank_options.py)
+  - [kaldifeat/python/tests/test_fbank_options.py](kaldifeat/python/tests/test_fbank_options.py)
+  - [kaldifeat/python/tests/test_mfcc_options.py](kaldifeat/python/tests/test_mfcc_options.py)
+  - [kaldifeat/python/tests/test_spectrogram_options.py](kaldifeat/python/tests/test_spectrogram_options.py)
+  - [kaldifeat/python/tests/test_plp_options.py](kaldifeat/python/tests/test_plp_options.py)
+
+for more examples.
+
+**HINT**: In the examples, you can find that
+
+- ``kaldifeat`` supports batch processing as well as chunk processing
+- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
+
+# Usage in other projects
+
+## icefall
+
+[icefall](https://github.com/k2-fsa/icefall) uses kaldifeat to extract features for a pre-trained model.
+
+See <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/pretrained.py>.
+
+## k2
+
+[k2](https://github.com/k2-fsa/k2) uses kaldifeat's C++ API.
+
+See <https://github.com/k2-fsa/k2/blob/v2.0-pre/k2/torch/csrc/features.cu>.
+
+## lhotse
+
+[lhotse](https://github.com/lhotse-speech/lhotse) uses kaldifeat to extract features on GPU.
+
+See <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/kaldifeat.py>.
+
+## sherpa
+
+[sherpa](https://github.com/k2-fsa/sherpa) uses kaldifeat for streaming feature
+extraction.
+
+See <https://github.com/k2-fsa/sherpa/blob/master/sherpa/bin/pruned_stateless_emformer_rnnt2/decode.py>
+
+# Installation
+
+Refer to
+<https://csukuangfj.github.io/kaldifeat>
+for installation.
+
+%package help
+Summary:	Development documents and examples for kaldifeat
+Provides:	python3-kaldifeat-doc
+%description help
+# kaldifeat
+
+<div align="center">
+<img src="/doc/source/images/os-green.svg">
+<img src="/doc/source/images/python_ge_3.6-blue.svg">
+<img src="/doc/source/images/pytorch_ge_1.5.0-green.svg">
+<img src="/doc/source/images/cuda_ge_10.1-orange.svg">
+</div>
+
+[![Documentation Status](https://github.com/csukuangfj/kaldifeat/actions/workflows/build-doc.yml/badge.svg)](https://csukuangfj.github.io/kaldifeat/)
+
+**Documentation**: <https://csukuangfj.github.io/kaldifeat>
+
+**Note**: If you are looking for a version that does not depend on PyTorch,
+please see <https://github.com/csukuangfj/kaldi-native-fbank>
+
+<sub>
+<table>
+<tr>
+<th>Comments</th>
+<th>Options</th>
+<th>Feature Computer</th>
+<th>Usage</th>
+</tr>
+
+<tr>
+<td>FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.Fbank</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device('cuda', 0)
+opts.frame_opts.window_type = 'povey'
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming FBANK</td>
+<td><code>kaldifeat.FbankOptions</code></td>
+<td><code>kaldifeat.OnlineFbank</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_fbank.py">
+./kaldifeat/python/tests/test_fbank.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.Mfcc</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.MfccOptions();
+opts.num_ceps = 13
+mfcc = kaldifeat.Mfcc(opts)
+features = mfcc(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming MFCC</td>
+<td><code>kaldifeat.MfccOptions</code></td>
+<td><code>kaldifeat.OnlineMfcc</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_mfcc.py">
+./kaldifeat/python/tests/test_mfcc.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.Plp</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.PlpOptions();
+opts.mel_opts.num_bins = 23
+plp = kaldifeat.Plp(opts)
+features = plp(wave)
+</pre>
+</td>
+</tr>
+
+<tr>
+<td>Streaming PLP</td>
+<td><code>kaldifeat.PlpOptions</code></td>
+<td><code>kaldifeat.OnlinePlp</code></td>
+<td>
+See <a href="./kaldifeat/python/tests/test_plp.py">
+./kaldifeat/python/tests/test_plp.py
+</a>
+</td>
+</tr>
+
+<tr>
+<td>Spectorgram</td>
+<td><code>kaldifeat.SpectrogramOptions</code></td>
+<td><code>kaldifeat.Spectrogram</code></td>
+<td>
+<pre lang="python">
+opts = kaldifeat.SpectrogramOptions();
+print(opts)
+spectrogram = kaldifeat.Spectrogram(opts)
+features = spectrogram(wave)
+</pre>
+</td>
+</tr>
+</table>
+</sub>
+
+
+Feature extraction compatible with `Kaldi` using PyTorch, supporting
+CUDA, batch processing, chunk processing, and autograd.
+
+The following kaldi-compatible commandline tools are implemented:
+
+  - `compute-fbank-feats`
+  - `compute-mfcc-feats`
+  - `compute-plp-feats`
+  - `compute-spectrogram-feats`
+
+(**NOTE**: We will implement other types of features, e.g., Pitch, ivector, etc, soon.)
+
+**HINT**: It supports also streaming feature extractors for Fbank, MFCC, and Plp.
+
+# Usage
+
+Let us first generate a test wave using sox:
+
+```bash
+# generate a wave of 1.2 seconds, containing a sine-wave
+# swept from 300 Hz to 3300 Hz
+sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
+```
+
+**HINT**: Download [test.wav][test_wav].
+
+[test_wav]: kaldifeat/python/tests/test_data/test.wav
+
+## Fbank
+
+```python
+import torchaudio
+
+import kaldifeat
+
+filename = "./test.wav"
+wave, samp_freq = torchaudio.load(filename)
+
+wave = wave.squeeze()
+
+opts = kaldifeat.FbankOptions()
+opts.frame_opts.dither = 0
+# Yes, it has same options like `Kaldi`
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+```
+
+To compute features that are compatible with `Kaldi`, wave samples have to be
+scaled to the range `[-32768, 32768]`. **WARNING**: You don't have to do this if
+you don't care about the compatibility with `Kaldi`.
+
+The following is an example:
+
+```python
+wave *= 32768
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+print(features[:3])
+```
+
+The output is:
+
+```
+tensor([[15.0074, 21.1730, 25.5286, 24.4644, 16.6994, 13.8480, 11.2087, 11.7952,
+         10.3911, 10.4491, 10.3012,  9.8743,  9.6997,  9.3751,  9.3476,  9.3559,
+          9.1074,  9.0032,  9.0312,  8.8399,  9.0822,  8.7442,  8.4023],
+        [13.8785, 20.5647, 25.4956, 24.6966, 16.9541, 13.9163, 11.3364, 11.8449,
+         10.2565, 10.5871, 10.3484,  9.7474,  9.6123,  9.3964,  9.0695,  9.1177,
+          8.9136,  8.8425,  8.5920,  8.8315,  8.6226,  8.8605,  8.9763],
+        [13.9475, 19.9410, 25.4494, 24.9051, 17.0004, 13.9207, 11.6667, 11.8217,
+         10.3411, 10.7258, 10.0983,  9.8109,  9.6762,  9.4218,  9.1246,  8.7744,
+          9.0863,  8.7488,  8.4695,  8.6710,  8.7728,  8.7405,  8.9824]])
+```
+
+You can compute the fbank feature for the same wave with `Kaldi` using the following commands:
+
+```bash
+echo "1 test.wav" > test.scp
+compute-fbank-feats --dither=0 scp:test.scp ark,t:test.txt
+head -n4 test.txt
+```
+
+The output is:
+
+```
+1  [
+  15.00744 21.17303 25.52861 24.46438 16.69938 13.84804 11.2087 11.79517 10.3911 10.44909 10.30123 9.874329 9.699727 9.37509 9.347578 9.355928 9.107419 9.00323 9.031268 8.839916 9.082197 8.744139 8.40221
+  13.87853 20.56466 25.49562 24.69662 16.9541 13.91633 11.33638 11.84495 10.25656 10.58718 10.34841 9.747416 9.612316 9.39642 9.06955 9.117751 8.913527 8.842571 8.59212 8.831518 8.622513 8.86048 8.976251
+  13.94753 19.94101 25.4494 24.90511 17.00044 13.92074 11.66673 11.82172 10.34108 10.72575 10.09829 9.810879 9.676199 9.421767 9.124647 8.774353 9.086291 8.74897 8.469534 8.670973 8.772754 8.740549 8.982433
+```
+
+You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
+
+
+**HINT**: Download [test.scp][test_scp] and [test.txt][test_txt].
+
+[test_scp]: kaldifeat/python/tests/test_data/test.scp
+[test_txt]: kaldifeat/python/tests/test_data/test.txt
+
+
+To use GPU, you can use:
+
+```python
+import torch
+
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device("cuda", 0)
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave.to(opts.device))
+```
+
+## MFCC, PLP, Spectrogram
+
+To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
+with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively. The same goes
+for `PLP` and `Spectrogram`.
+
+Please refer to
+
+  - [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
+  - [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
+  - [kaldifeat/python/tests/test_plp.py](kaldifeat/python/tests/test_plp.py)
+  - [kaldifeat/python/tests/test_spectrogram.py](kaldifeat/python/tests/test_spectrogram.py)
+  - [kaldifeat/python/tests/test_frame_extraction_options.py](kaldifeat/python/tests/test_frame_extraction_options.py)
+  - [kaldifeat/python/tests/test_mel_bank_options.py](kaldifeat/python/tests/test_mel_bank_options.py)
+  - [kaldifeat/python/tests/test_fbank_options.py](kaldifeat/python/tests/test_fbank_options.py)
+  - [kaldifeat/python/tests/test_mfcc_options.py](kaldifeat/python/tests/test_mfcc_options.py)
+  - [kaldifeat/python/tests/test_spectrogram_options.py](kaldifeat/python/tests/test_spectrogram_options.py)
+  - [kaldifeat/python/tests/test_plp_options.py](kaldifeat/python/tests/test_plp_options.py)
+
+for more examples.
+
+**HINT**: In the examples, you can find that
+
+- ``kaldifeat`` supports batch processing as well as chunk processing
+- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
+
+# Usage in other projects
+
+## icefall
+
+[icefall](https://github.com/k2-fsa/icefall) uses kaldifeat to extract features for a pre-trained model.
+
+See <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/pretrained.py>.
+
+## k2
+
+[k2](https://github.com/k2-fsa/k2) uses kaldifeat's C++ API.
+
+See <https://github.com/k2-fsa/k2/blob/v2.0-pre/k2/torch/csrc/features.cu>.
+
+## lhotse
+
+[lhotse](https://github.com/lhotse-speech/lhotse) uses kaldifeat to extract features on GPU.
+
+See <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/kaldifeat.py>.
+
+## sherpa
+
+[sherpa](https://github.com/k2-fsa/sherpa) uses kaldifeat for streaming feature
+extraction.
+
+See <https://github.com/k2-fsa/sherpa/blob/master/sherpa/bin/pruned_stateless_emformer_rnnt2/decode.py>
+
+# Installation
+
+Refer to
+<https://csukuangfj.github.io/kaldifeat>
+for installation.
+
+%prep
+%autosetup -n kaldifeat-1.24
+
+%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-kaldifeat -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 1.24-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..2cfcf66
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+66dbc99675639a1320496ec4a9250f07  kaldifeat-1.24.tar.gz