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  <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>
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  <br/><br/>
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- # wav2vec 2.0 with CTC/Attention trained on CommonVoice Italian (No LM)
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-
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- This repository provides all the necessary tools to perform automatic speech
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- recognition from an end-to-end system pretrained on CommonVoice (Italian Language) within
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- SpeechBrain. For a better experience, we encourage you to learn more about
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- [SpeechBrain](https://speechbrain.github.io).
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-
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- The performance of the model is the following:
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-
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- | Release | Test WER | GPUs |
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- |:--------------:|:--------------:| :--------:|
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- | 03-06-21 | 9.86 | 2xV100 32GB |
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-
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- ## Pipeline description
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-
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- 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
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- the train transcriptions (train.tsv) of CommonVoice (EN).
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- - 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.
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- The obtained final acoustic representation is given to the CTC and attention decoders.
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-
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- The system is trained with recordings sampled at 16kHz (single channel).
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- The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
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-
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- ## Install SpeechBrain
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-
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- First of all, please install tranformers and SpeechBrain with the following command:
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-
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- ```
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- pip install speechbrain transformers
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- ```
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-
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- Please notice that we encourage you to read our tutorials and learn more about
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- [SpeechBrain](https://speechbrain.github.io).
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-
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- ### Transcribing your own audio files (in Italian)
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-
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- ```python
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- from speechbrain.pretrained import EncoderDecoderASR
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-
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- asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-it", savedir="pretrained_models/asr-wav2vec2-commonvoice-it")
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- asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-it/example-it.wav")
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-
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- ```
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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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-
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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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-
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- ### Training
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- The model was trained with SpeechBrain.
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- To train it from scratch follow these steps:
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- 1. Clone SpeechBrain:
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- ```bash
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- git clone https://github.com/speechbrain/speechbrain/
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- ```
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- 2. Install it:
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- ```bash
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- cd speechbrain
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- pip install -r requirements.txt
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- pip install -e .
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- ```
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-
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- 3. Run Training:
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- ```bash
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- cd recipes/CommonVoice/ASR/seq2seq
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- python train_with_wav2vec.py hparams/train_it_with_wav2vec.yaml --data_folder=your_data_folder
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- ```
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-
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- You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?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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  <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>
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  <br/><br/>
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+ # Work in Progress
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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.