asr

automatic-speech-recognition

🎧 Automatic Speech Recognition: DeepSpeech & Seq2Seq (TensorFlow)

Showing:

Popularity

Downloads/wk

0

GitHub Stars

208

Maintenance

Last Commit

1yr ago

Contributors

5

Package

Dependencies

6

License

GNU

Categories

Readme

Automatic Speech Recognition

The project aim is to distill the Automatic Speech Recognition research. At the beginning, you can load a ready-to-use pipeline with a pre-trained model. Benefit from the eager TensorFlow 2.0 and freely monitor model weights, activations or gradients.

import automatic_speech_recognition as asr

file = 'to/test/sample.wav'  # sample rate 16 kHz, and 16 bit depth
sample = asr.utils.read_audio(file)
pipeline = asr.load('deepspeech2', lang='en')
pipeline.model.summary()     # TensorFlow model
sentences = pipeline.predict([sample])

We support english (thanks to Open Seq2Seq). The evaluation results of the English benchmark LibriSpeech dev-clean are in the table. To reference, the DeepSpeech (Mozilla) achieves around 7.5% WER, whereas the state-of-the-art (RWTH Aachen University) equals 2.3% WER (recent evaluation results can be found here). Both of them, use the external language model to boost results. By comparison, humans achieve 5.83% WER here (LibriSpeech dev-clean)

Model NameDecoderWER-dev
deepspeech2greedy6.71

Shortly it turns out that you need to adjust pipeline a little bit. Take a look at the CTC Pipeline. The pipeline is responsible for connecting a neural network model with all non-differential transformations (features extraction or prediction decoding). Pipeline components are independent. You can adjust them to your needs e.g. use more sophisticated feature extraction, different data augmentation, or add the language model decoder (static n-grams or huge transformers). You can do much more like distribute the training using the Strategy, or experiment with mixed precision policy.


import numpy as np
import tensorflow as tf
import automatic_speech_recognition as asr

dataset = asr.dataset.Audio.from_csv('train.csv', batch_size=32)
dev_dataset = asr.dataset.Audio.from_csv('dev.csv', batch_size=32)
alphabet = asr.text.Alphabet(lang='en')
features_extractor = asr.features.FilterBanks(
    features_num=160,
    winlen=0.02,
    winstep=0.01,
    winfunc=np.hanning
)
model = asr.model.get_deepspeech2(
    input_dim=160,
    output_dim=29,
    rnn_units=800,
    is_mixed_precision=False
)
optimizer = tf.optimizers.Adam(
    lr=1e-4,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-8
)
decoder = asr.decoder.GreedyDecoder()
pipeline = asr.pipeline.CTCPipeline(
    alphabet, features_extractor, model, optimizer, decoder
)
pipeline.fit(dataset, dev_dataset, epochs=25)
pipeline.save('/checkpoint')

test_dataset = asr.dataset.Audio.from_csv('test.csv')
wer, cer = asr.evaluate.calculate_error_rates(pipeline, test_dataset)
print(f'WER: {wer}   CER: {cer}')

Installation

You can use pip:

pip install automatic-speech-recognition

Otherwise clone the code and create a new environment via conda:

git clone https://github.com/rolczynski/Automatic-Speech-Recognition.git
conda env create -f=environment.yml     # or use: environment-gpu.yml
conda activate Automatic-Speech-Recognition

References

The fundamental repositories:

Moreover, you can explore the GitHub using key phrases like ASR, DeepSpeech, or Speech-To-Text. The list wer_are_we, an attempt at tracking states of the art, can be helpful too.

Rate & Review

Great Documentation0
Easy to Use0
Performant0
Highly Customizable0
Bleeding Edge0
Responsive Maintainers0
Poor Documentation0
Hard to Use0
Slow0
Buggy0
Abandoned0
Unwelcoming Community0
100
No reviews found
Be the first to rate

Alternatives

No alternatives found

Tutorials

No tutorials found
Add a tutorial