Create README.md

README.md

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+---
+language: "en"
+thumbnail:
+tags:
+- ASR
+- CTC
+- Attention
+- Transformers
+- pytorch
+license: "apache-2.0"
+datasets:
+- librispeech
+metrics:
+- wer
+- cer
+---
+
+# Transformer for AISHELL (Mandarin Chinese)
+
+This repository provides all the necessary tools to perform automatic speech
+recognition from an end-to-end system pretrained on AISHELL (Mandarin Chinese)
+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 | Dev CER | Test CER | GPUs | Full Results |
+|:-------------:|:--------------:|:--------------:|:--------:|:--------:|
+| 05-03-21 | 5.60 | 6.04 | 2xV100 32GB | [Google Drive](https://drive.google.com/drive/folders/1zlTBib0XEwWeyhaXDXnkqtPsIBI18Uzs?usp=sharing)|
+
+
+
+## Pipeline description
+
+This ASR system is composed of 2 different but linked blocks:
+1. Tokenizer (unigram) that transforms words into subword units and trained with
+the train transcriptions of LibriSpeech.
+2. Acoustic model made of a transformer encoder and a joint decoder with CTC +
+transformer. Hence, the decoding also incorporates the CTC probabilities.
+
+To Train this system from scratch, [see our SpeechBrain recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/AISHELL-1).
+
+## Intended uses & limitations
+
+This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
+for the Mandarin Chinese language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
+detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is
+installed.
+
+## 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.pretrained import TransformerASR
+
+asr_model = TransformerASR.from_hparams(source="speechbrain/asr-transformer-aishell", savedir="pretrained_models/asr-transformer-aishell")
+asr_model.transcribe_file("speechbrain/asr-transformer-aishell/example.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}},
+  }
+```