Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/csukuangfj/kaldifeat
Kaldi-compatible online & offline feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd - Provide C++ & Python API
https://github.com/csukuangfj/kaldifeat
cpp fbank features-extraction kaldi mfcc online-feature-extractor plp python pytorch streaming-feature-extractor
Last synced: 5 days ago
JSON representation
Kaldi-compatible online & offline feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd - Provide C++ & Python API
- Host: GitHub
- URL: https://github.com/csukuangfj/kaldifeat
- Owner: csukuangfj
- License: other
- Created: 2021-02-25T07:45:43.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-12-14T10:54:26.000Z (about 1 month ago)
- Last Synced: 2024-12-22T01:04:32.397Z (about 1 month ago)
- Topics: cpp, fbank, features-extraction, kaldi, mfcc, online-feature-extractor, plp, python, pytorch, streaming-feature-extractor
- Language: C++
- Homepage: https://csukuangfj.github.io/kaldifeat
- Size: 10.3 MB
- Stars: 192
- Watchers: 8
- Forks: 36
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# kaldifeat
[](https://csukuangfj.github.io/kaldifeat/)
**Documentation**:
**Note**: If you are looking for a version that does not depend on PyTorch,
please see
# Installation
Refer to
for installation.
> Never use `pip install kaldifeat`
> Never use `pip install kaldifeat`
> Never use `pip install kaldifeat`
Comments
Options
Feature Computer
Usage
Fbank for Whisper
kaldifeat.WhisperFbankOptions
kaldifeat.WhisperFbank
opts = kaldifeat.WhisperFbankOptions()
opts.device = torch.device('cuda', 0)
fbank = kaldifeat.WhisperFbank(opts)
features = fbank(wave)
See #82
Fbank for Whisper-V3
kaldifeat.WhisperFbankOptions
kaldifeat.WhisperFbank
opts = kaldifeat.WhisperFbankOptions()
opts.num_mels = 128
opts.device = torch.device('cuda', 0)
fbank = kaldifeat.WhisperFbank(opts)
features = fbank(wave)
FBANK
kaldifeat.FbankOptions
kaldifeat.Fbank
opts = kaldifeat.FbankOptions()
opts.device = torch.device('cuda', 0)
opts.frame_opts.window_type = 'povey'
fbank = kaldifeat.Fbank(opts)
features = fbank(wave)
Streaming FBANK
kaldifeat.FbankOptions
kaldifeat.OnlineFbank
See
./kaldifeat/python/tests/test_fbank.py
MFCC
kaldifeat.MfccOptions
kaldifeat.Mfcc
opts = kaldifeat.MfccOptions();
opts.num_ceps = 13
mfcc = kaldifeat.Mfcc(opts)
features = mfcc(wave)
Streaming MFCC
kaldifeat.MfccOptions
kaldifeat.OnlineMfcc
See
./kaldifeat/python/tests/test_mfcc.py
PLP
kaldifeat.PlpOptions
kaldifeat.Plp
opts = kaldifeat.PlpOptions();
opts.mel_opts.num_bins = 23
plp = kaldifeat.Plp(opts)
features = plp(wave)
Streaming PLP
kaldifeat.PlpOptions
kaldifeat.OnlinePlp
See
./kaldifeat/python/tests/test_plp.py
Spectorgram
kaldifeat.SpectrogramOptions
kaldifeat.Spectrogram
opts = kaldifeat.SpectrogramOptions();
print(opts)
spectrogram = kaldifeat.Spectrogram(opts)
features = spectrogram(wave)
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 .
## k2
[k2](https://github.com/k2-fsa/k2) uses kaldifeat's C++ API.
See .
## lhotse
[lhotse](https://github.com/lhotse-speech/lhotse) uses kaldifeat to extract features on GPU.
See .
## sherpa
[sherpa](https://github.com/k2-fsa/sherpa) uses kaldifeat for streaming feature
extraction.
See