model card

README.md

@@ -0,0 +1,80 @@
+---
+language: "en"
+thumbnail:
+tags:
+- ASR
+- CTC
+- Attention
+- Tranformer
+- pytorch
+license: "apache-2.0"
+datasets:
+- librispeech
+metrics:
+- wer
+- cer
+---
+
+# 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 given ASR model performance are:
+
+| Release | Test clean WER | Test other WER | GPUs |
+|:-------------:|:--------------:|:--------------:|:--------:|
+| 05-03-21 | 2.90 | 8.51 | 1xV100 16GB |
+
+## Pipeline description
+
+This ASR system is composed with 3 different but linked blocks:
+1. Tokenizer (unigram) that transforms words into subword units and trained with
+the train transcriptions of LibriSpeech.
+2. Neural language model (Transformer LM) trained on the full 10M words dataset.
+3. 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 with projection layers is connected
+to a final DNN to obtain the final acoustic representation that is given to
+the CTC and attention decoders.
+
+## Intended uses & limitations
+
+This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model
+for the english language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
+detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is
+installed.
+
+## Install SpeechBrain
+
+First of all, please install SpeechBrain with the following command:
+
+```
+pip install \\we hide ! SpeechBrain is still private :p
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Transcribing your own audio files
+
+```python
+from speechbrain.pretrained import EncoderDecoderASR
+
+asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-transformerlm-librispeech")
+asr_model.transcribe_file("path_to_your_file.wav")
+
+```
+
+#### Referencing SpeechBrain
+
+```
+@misc{SB2021,
+    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
+    title = {SpeechBrain},
+    year = {2021},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\url{https://github.com/speechbrain/speechbrain}},
+  }
+```