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@@ -0,0 +1 @@ +/kaldifeat-1.24.tar.gz diff --git a/python-kaldifeat.spec b/python-kaldifeat.spec new file mode 100644 index 0000000..2c96d44 --- /dev/null +++ b/python-kaldifeat.spec @@ -0,0 +1,936 @@ +%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> + +[](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> + +[](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> + +[](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 @@ -0,0 +1 @@ +66dbc99675639a1320496ec4a9250f07 kaldifeat-1.24.tar.gz |