update readme

README.md

@@ -2,9 +2,10 @@
 language:
 - en
 thumbnail: null
-pipeline_tag: automatic-speech-recognition
 tags:
+- automatic-speech-recognition
 - CTC
+- Attention
 - pytorch
 - speechbrain
 license: apache-2.0
@@ -12,7 +13,7 @@ datasets:
 - switchboard
 metrics:
 - wer
-- ser
+- cer
 
 ---
 
@@ -21,26 +22,23 @@ metrics:
 
 # wav2vec 2.0 with CTC/Attention trained on Switchboard (No LM)
 
-This repository provides all the necessary tools to perform automatic speech
-recognition from an end-to-end system pretrained on the Switchboard corpus within
-SpeechBrain. For a better experience, we encourage you to learn more about
-[SpeechBrain](https://speechbrain.github.io).
+This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on the Switchboard (EN) corpus 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  | Swbd SER | Callhome SER | Eval2000 SER | Swbd WER | Callhome WER | Eval2000 WER | GPUs        |
+| Release  | Swbd CER | Callhome CER | Eval2000 CER | Swbd WER | Callhome WER | Eval2000 WER | GPUs        |
 |:--------:|:--------:|:------------:|:------------:|:--------:|:------------:|:------------:|:-----------:|
-| 17-09-22 |  48.60   |  55.76       |   52.96      |  8 .76   |  14.67       |     11.78    | 4xA100 40GB |
+| 17-09-22 |  5.24   |  9.69       |   7.44      |  8 .76   |  14.67       |     11.78    | 4xA100 40GB |
 
-## Pipeline description
+## Pipeline Description
 
 This ASR system is composed of 2 different but linked blocks:
-- Tokenizer (unigram) that transforms words into subword units trained on the Switchboard training transcripts and the Fisher corpus.
-- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on Switchboard
-The obtained final acoustic representation is given to the CTC greedy decoder.
+- Tokenizer (unigram) that transforms words into subword units trained on the Switchboard training transcriptions and the Fisher corpus. 
+- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with a feature encoder consisting of three DNN layers and finetuned on Switchboard. The obtained final acoustic representation is given to a greedy CTC decoder.
 
 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.
+The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling `transcribe_file` if needed.
 
 ## Install SpeechBrain
 
@@ -53,7 +51,7 @@ pip install speechbrain transformers
 Please notice that we encourage you to read our tutorials and learn more about
 [SpeechBrain](https://speechbrain.github.io).
 
-### Transcribing your own audio files
+## Transcribing Your Own Audio Files
 
 ```python
 from speechbrain.pretrained import EncoderASR
@@ -62,17 +60,21 @@ asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-switchboard
 asr_model.transcribe_file('path/to/audiofile')
 
 ```
-### Inference on GPU
+
+## Inference on GPU
+
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
-### Training
-The model was trained with SpeechBrain (Commit hash: '70904d0').
+## Training
+
+The model was trained with SpeechBrain (commit hash: `70904d0`).
 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
@@ -82,32 +84,42 @@ pip install -e .
 
 3. Run Training:
 ```bash
-cd recipes/Switchboard/ASR/CTC/
+cd recipes/Switchboard/ASR/CTC
 python train_with_wav2vec.py hparams/train_with_wav2vec.yaml --data_folder=your_data_folder
 ```
 
-### Limitations
-The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+## Limitations
 
-#### Referencing SpeechBrain
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. 
 
-```
-@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}},
-  }
-```
+## Credits
+
+This model was trained with resources provided by the [THN Center for AI](https://www.th-nuernberg.de/en/kiz). 
+
+# About SpeechBrain
 
-#### About SpeechBrain
-SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
+SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. 
+Competitive or state-of-the-art performance is obtained in various domains.
 
-Website: https://speechbrain.github.io/
+- Website: https://speechbrain.github.io/
+- GitHub: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/
 
-GitHub: https://github.com/speechbrain/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}
+}
+```