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--- |
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language: "ru" |
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thumbnail: |
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tags: |
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- automatic-speech-recognition |
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- CTC |
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- Attention |
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- pytorch |
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- speechbrain |
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license: "apache-2.0" |
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datasets: |
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- buriy-audiobooks-2-val |
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metrics: |
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- wer |
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- cer |
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--- |
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| Release | Test WER | GPUs | |
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|:-------------:|:--------------:| :--------:| |
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| 22-05-11 | - | 1xK80 24GB | |
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## Pipeline description |
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(by SpeechBrain text) |
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This ASR system is composed with 3 different but linked blocks: |
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- Tokenizer (unigram) that transforms words into subword units and trained with |
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the train transcriptions of LibriSpeech. |
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- Neural language model (RNNLM) trained on the full (380K) words dataset. |
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- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of |
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N blocks of convolutional neural networks with normalisation and pooling on the |
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frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain |
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the final acoustic representation that is given to the CTC and attention decoders. |
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The system is trained with recordings sampled at 16kHz (single channel). |
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The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that SpeechBrain encourage you to read tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Transcribing your own audio files (in Russian) |
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```python |
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from speechbrain.pretrained import EncoderDecoderASR |
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asr_model = EncoderDecoderASR.from_hparams(source="AndyGo/speech-brain-asr-crdnn-rnnlm-buriy-audiobooks-2-val", savedir="pretrained_models/speech-brain-asr-crdnn-rnnlm-buriy-audiobooks-2-val") |
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asr_model.transcribe_file('speech-brain-asr-crdnn-rnnlm-buriy-audiobooks-2-val/example.wav') |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
