Update README.md

README.md

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 # CRDNN with CTC/Attention trained on CommonVoice 7.0 German (No LM)
 This repository provides all the necessary tools to perform automatic speech
 recognition from an end-to-end system pretrained on CommonVoice (German Language) 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:
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 | Release | Test CER | Test WER | GPUs |
 |:-------------:|:--------------:|:--------------:| :--------:|
-| 28-10-21 | 4.93 | 15.37 | 1xV100 16GB |
+| 28.10.21 | 4.93 | 15.37 | 1xV100 16GB |
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 ## Credits
 The model is provided by [vitas.ai](vitas.ai).
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 ## Pipeline description
 This ASR system is composed of 2 different but linked blocks:
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 - Tokenizer (unigram) that transforms words into subword units and trained with
 the train transcriptions (train.tsv) of CommonVoice (DE).
 - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
 N blocks of convolutional neural networks with normalization 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.
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 ## Install SpeechBrain
 First of all, please install SpeechBrain with the following command:
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 ```
 pip install speechbrain
 ```
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 Please notice that we encourage you to read our tutorials and learn more about
 [SpeechBrain](https://speechbrain.github.io).
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 ### Transcribing your own audio files (in German)
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 ```python
 from speechbrain.pretrained import EncoderDecoderASR
 asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-de", savedir="pretrained_models/asr-crdnn-commonvoice-de")
 asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-de/example-de.wav")
 ```
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 ### Inference on GPU
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 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
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 ## Parallel Inference on a Batch
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 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.
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 ### Training
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 The model was trained with SpeechBrain (986a2175).
 To train it from scratch follows these steps:
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 1. Clone SpeechBrain:
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 ```bash
 git clone https://github.com/speechbrain/speechbrain/
 ```
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 2. Install it:
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 ```
 cd speechbrain
 pip install -r requirements.txt
 pip install -e .
 ```
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 3. Run Training:
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 ```
 cd recipes/CommonVoice/ASR/seq2seq
 python train.py hparams/train_de.yaml --data_folder=your_data_folder
 ```
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 You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/13i7rdgVX7-qZ94Rtj6OdUgU-S6BbKKvw?usp=sharing)
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 ### Limitations
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 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
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 # **About SpeechBrain**
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 - Website: https://speechbrain.github.io/
 - Code: https://github.com/speechbrain/speechbrain/
 - HuggingFace: https://huggingface.co/speechbrain/
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 # **Citing SpeechBrain**
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 Please, cite SpeechBrain if you use it for your research or business.
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 ```bibtex
 @misc{speechbrain,
   title={{SpeechBrain}: A General-Purpose Speech Toolkit},