# kaldifeat

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[![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>

# Installation

Refer to
<https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html>
for installation.

> Never use `pip install kaldifeat`

> Never use `pip install kaldifeat`

> Never use `pip install kaldifeat`



<sub>
<table>
<tr>
<th>Comments</th>
<th>Options</th>
<th>Feature Computer</th>
<th>Usage</th>
</tr>

<tr>
<td>Fbank for <a href="https://github.com/openai/whisper">Whisper</a></td>
<td><code>kaldifeat.WhisperFbankOptions</code></td>
<td><code>kaldifeat.WhisperFbank</code></td>
<td>
<pre lang="python">
opts = kaldifeat.WhisperFbankOptions()
opts.device = torch.device('cuda', 0)
fbank = kaldifeat.WhisperFbank(opts)
features = fbank(wave)
</pre>
See <a href="https://github.com/csukuangfj/kaldifeat/pull/82">#82</a>
</td>
</tr>

<tr>
<td>Fbank for <a href="https://github.com/openai/whisper">Whisper-V3</a></td>
<td><code>kaldifeat.WhisperFbankOptions</code></td>
<td><code>kaldifeat.WhisperFbank</code></td>
<td>
<pre lang="python">
opts = kaldifeat.WhisperFbankOptions()
opts.num_mels = 128
opts.device = torch.device('cuda', 0)
fbank = kaldifeat.WhisperFbank(opts)
features = fbank(wave)
</pre>
</td>
</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>