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	---
	language: "en"
	thumbnail:
	tags:
	- automatic-speech-recognition
	- CTC
	- Attention
	- pytorch
	- speechbrain
	license: "apache-2.0"
	datasets:
	- librispeech
	metrics:
	- wer
	- cer
	---

	<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
	<br/><br/>

	# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech

	This repository provides all the necessary tools to perform automatic speech
	recognition from an end-to-end system pretrained on LibriSpeech (EN) within
	SpeechBrain. For a better experience we encourage you to learn more about
	[SpeechBrain](https://speechbrain.github.io).
	The performance of the model is the following:

	\| Release \| Test WER \| GPUs \|
	\|:-------------:\|:--------------:\| :--------:\|
	\| 20-05-22 \| 3.09 \| 1xV100 32GB \|

	## Pipeline description

	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 10M 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 we encourage you to read our tutorials and learn more about
	[SpeechBrain](https://speechbrain.github.io).

	### Transcribing your own audio files (in English)

	```python
	from speechbrain.inference.ASR import EncoderDecoderASR

	asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="pretrained_models/asr-crdnn-rnnlm-librispeech")
	asr_model.transcribe_file('speechbrain/asr-crdnn-rnnlm-librispeech/example.wav')

	```

	### Inference on GPU
	To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.

	## Parallel Inference on a Batch
	Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.



	### Training
	The model was trained with SpeechBrain (Commit hash: '2abd9f01').
	To train it from scratch follow these steps:
	1. Clone SpeechBrain:
	```bash
	git clone https://github.com/speechbrain/speechbrain/
	```
	2. Install it:
	```bash
	cd speechbrain
	pip install -r requirements.txt
	pip install -e .
	```

	3. Run Training:
	```bash
	cd recipes/LibriSpeech/ASR/seq2seq/
	python train.py hparams/train_BPE_1000.yaml --data_folder=your_data_folder
	```

	You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1SAndjcThdkO-YQF8kvwPOXlQ6LMT71vt?usp=sharing).

	### Limitations
	The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.


	# About SpeechBrain
	- Website: https://speechbrain.github.io/
	- Code: https://github.com/speechbrain/speechbrain/
	- HuggingFace: https://huggingface.co/speechbrain/


	# Citing SpeechBrain
	Please, cite SpeechBrain if you use it for your research or business.

	```bibtex
	@misc{speechbrain,
	title={{SpeechBrain}: A General-Purpose Speech Toolkit},
	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},
	year={2021},
	eprint={2106.04624},
	archivePrefix={arXiv},
	primaryClass={eess.AS},
	note={arXiv:2106.04624}
	}
	```