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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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after 9 epochs training - valid %WER - 4.09e+02 |
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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/speechbrain-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('speechbrain-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. |
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## About SpeechBrain |
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Website: https://speechbrain.github.io/ |
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Code: https://github.com/speechbrain/speechbrain/ |
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HuggingFace: https://huggingface.co/speechbrain/ |
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## Citing SpeechBrain |
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Please, cite SpeechBrain if you use it for your research or business. |
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@misc{speechbrain, |
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title={{SpeechBrain}: A General-Purpose Speech Toolkit}, |
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author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, |
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year={2021}, |
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eprint={2106.04624}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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note={arXiv:2106.04624} |
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} |
