Create README.md

README.md

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