update readme

README.md

@@ -14,7 +14,8 @@ datasets:
 - switchboard
 metrics:
 - wer
-- ser
+- 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>
@@ -23,28 +24,26 @@ metrics:
 # Transformer for Switchboard (with Transformer LM)
 
 This repository provides all the necessary tools to perform automatic speech
-recognition from an end-to-end system pretrained on Switchboard 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:
+recognition from an end-to-end system pretrained on Switchboard (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  | Swbd SER | Callhome SER | Eval2000 SER | Swbd WER | Callhome WER | Eval2000 WER | GPUs        |
-|:--------:|:--------:|:------------:|:------------:|:--------:|:------------:|:------------:|:-----------:|
-| 17-09-22 |  49.30   |  56.89       |  54.20       |  9.80     |  17.89       |  13.94       | 1xA100 40GB |
+| Release  | Swbd WER | Callhome WER | Eval2000 WER | GPUs        |
+|:--------:|:--------:|:------------:|:------------:|:-----------:|
+| 17-09-22 | 9.80     |  17.89       |  13.94       | 1xA100 40GB |
 
 
-## Pipeline description
+## Pipeline Description
 
 This ASR system is composed of 3 different but linked blocks:
-- Tokenizer (unigram) that transforms words into subword units and trained with
-the train transcriptions of LibriSpeech.
-- Neural language model (Transformer LM) trained on the Switchboard training set and the Fisher corpus.
+- Tokenizer (unigram) that transforms words into subword units trained on the Switchboard training transcriptions and the Fisher corpus.
+- Neural language model (Transformer LM) trained on the Switchboard training transcriptions and the Fisher corpus.
 - Acoustic model made of a transformer encoder and a joint decoder with CTC +
 transformer. Hence, the decoding also incorporates the CTC probabilities.
 
 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
 
@@ -57,26 +56,32 @@ 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)
+## Transcribing Your Own Audio Files
 
 ```python
 from speechbrain.pretrained import EncoderDecoderASR
 asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-transformer-switchboard", savedir="pretrained_models/asr-transformer-switchboard")
 asr_model.transcribe_file("path/to/your/audiofile")
 ```
-### Inference on GPU
+
+## 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: '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
@@ -90,20 +95,26 @@ cd recipes/Switchboard/ASR/transformer
 python train.py hparams/transformer.yaml --data_folder=your_data_folder
 ```
 
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1ZudxqMWb8VNCJKvY2Ws5oNY3WI1To0I7?usp=sharing).
+## Limitations
 
-### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
 
-# **About SpeechBrain**
+## Credits
+
+This model was trained with resources provided by the [THN Center for AI](https://www.th-nuernberg.de/en/kiz). 
+
+# 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.
+
 - Website: https://speechbrain.github.io/
-- Code: https://github.com/speechbrain/speechbrain/
+- GitHub: https://github.com/speechbrain/speechbrain/
 - HuggingFace: https://huggingface.co/speechbrain/
 
+# Citing SpeechBrain
 
-# **Citing SpeechBrain**
-Please, cite SpeechBrain if you use it for your research or business.
-
+Please cite SpeechBrain if you use it for your research or business.
 
 ```bibtex
 @misc{speechbrain,
@@ -116,7 +127,3 @@ Please, cite SpeechBrain if you use it for your research or business.
   note={arXiv:2106.04624}
 }
 ```
-
-#### Credits
-
-This model was trained with resources provided by the [KIZ](https://www.th-nuernberg.de/en/facilities/competence-centers/center-for-artificial-intelligence-kiz/) Cluster at TH Nürnberg.