https://github.com/csukuangfj/kaldi-native-fbank

Kaldi-compatible online fbank extractor without external dependencies
https://github.com/csukuangfj/kaldi-native-fbank

cpp fbank kaldi-compatible online-fbank python

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Kaldi-compatible online fbank extractor without external dependencies

• Host: GitHub
• URL: https://github.com/csukuangfj/kaldi-native-fbank
• Owner: csukuangfj
• License: apache-2.0
• Created: 2022-08-23T12:53:35.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-10-24T12:14:01.000Z (3 months ago)
• Last Synced: 2024-10-25T06:43:35.315Z (3 months ago)
• Topics: cpp, fbank, kaldi-compatible, online-fbank, python
• Language: C++
• Homepage:
• Size: 151 KB
• Stars: 75
• Watchers: 4
• Forks: 21
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Introduction

Kaldi-compatible online fbank feature extractor without external dependencies.

Tested on the following architectures and operating systems:

  - Linux

  - macOS

  - Windows

  - Android

  - x86

  - arm

  - aarch64

# Usage

See the following CMake-based speech recognition (i.e., text-to-speech) projects

for its usage:

- 

  - Specifically, please have a look at 

- 

They use `kaldi-native-fbank` to compute fbank features for **real-time**

speech recognition.

# Python APIs

First, please install `kaldi-native-fbank` by

```bash

git clone https://github.com/csukuangfj/kaldi-native-fbank

cd kaldi-native-fbank

python3 setup.py install

```

or use

```bash

pip install kaldi-native-fbank

```

To check that you have installed `kaldi-native-fbank` successfully, please use

```

python3 -c "import kaldi_native_fbank; print(kaldi_native_fbank.__version__)"

```

which should print the version you have installed.

Please refer to

  - 

  - 

for usages.

For easier reference, we post the above file below:

```python3

#!/usr/bin/env python3

import sys

try:

    import kaldifeat

except:

    print("Please install kaldifeat first")

    sys.exit(0)

import kaldi_native_fbank as knf

import torch

def main():

    sampling_rate = 16000

    samples = torch.randn(16000 * 10)

    opts = kaldifeat.FbankOptions()

    opts.frame_opts.dither = 0

    opts.mel_opts.num_bins = 80

    opts.frame_opts.snip_edges = False

    opts.mel_opts.debug_mel = False

    online_fbank = kaldifeat.OnlineFbank(opts)

    online_fbank.accept_waveform(sampling_rate, samples)

    opts = knf.FbankOptions()

    opts.frame_opts.dither = 0

    opts.mel_opts.num_bins = 80

    opts.frame_opts.snip_edges = False

    opts.mel_opts.debug_mel = False

    fbank = knf.OnlineFbank(opts)

    fbank.accept_waveform(sampling_rate, samples.tolist())

    assert online_fbank.num_frames_ready == fbank.num_frames_ready

    for i in range(fbank.num_frames_ready):

        f1 = online_fbank.get_frame(i)

        f2 = torch.from_numpy(fbank.get_frame(i))

        assert torch.allclose(f1, f2, atol=1e-3), (i, (f1 - f2).abs().max())

if __name__ == "__main__":

    torch.manual_seed(20220825)

    main()

    print("success")

```