Update README.md

README.md

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-# wav2vec 2.0 with CTC/Attention trained on CommonVoice Italian (No LM)
+# Work-in-Progress
 
-This repository provides all the necessary tools to perform automatic speech
-recognition from an end-to-end system pretrained on CommonVoice (Italian 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:
-
-| Release | Test WER | GPUs |
-|:--------------:|:--------------:| :--------:|
-| 03-06-21 | 9.86 | 2xV100 32GB |
-
-## Pipeline description
-
-This ASR system is composed of 2 different but linked blocks:
-- Tokenizer (unigram) that transforms words into subword units and trained with
-the train transcriptions (train.tsv) of CommonVoice (EN).
-- Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli)) is combined with two DNN layers and finetuned on CommonVoice En. 
-The obtained final acoustic representation is given to the CTC and attention decoders.
-
-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.
-
-## Install SpeechBrain
-
-First of all, please install tranformers and SpeechBrain with the following command:
-
-```
-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 (in Italian)
-
-```python
-from speechbrain.pretrained import EncoderDecoderASR
-
-asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-it", savedir="pretrained_models/asr-wav2vec2-commonvoice-it")
-asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-it/example-it.wav")
-
-```
-### 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.
-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
-pip install -r requirements.txt
-pip install -e .
-```
-
-3. Run Training:
-```bash
-cd recipes/CommonVoice/ASR/seq2seq
-python train_with_wav2vec.py hparams/train_it_with_wav2vec.yaml --data_folder=your_data_folder
-```
-
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing).
 
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.