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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ language:
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+ - ar
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+ thumbnail: null
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+ pipeline_tag: automatic-speech-recognition
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+ tags:
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+ - whisper
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+ - pytorch
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+ - speechbrain
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+ - Transformer
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+ - hf-asr-leaderboard
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  license: apache-2.0
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+ datasets:
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+ - commonvoice
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+ metrics:
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+ - wer
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+ - cer
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+ model-index:
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+ - name: asr-whisper-medium-commonvoice-ar
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+ results:
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+ - task:
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+ name: Automatic Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: CommonVoice 10.0 (Arabic)
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+ type: mozilla-foundation/common_voice_14_0
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+ config: ar
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+ split: test
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+ args:
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+ language: ar
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: '14.82'
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  ---
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+
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+ <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=medium" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
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+ <br/><br/>
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+
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+ # whisper medium fine-tuned on CommonVoice-14.0 Arabic
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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 whisper model fine-tuned on CommonVoice (Arabic 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 CER | Test WER | GPUs |
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+ |:-------------:|:--------------:|:--------------:| :--------:|
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+ | 1-08-23 | 4.95 | 14.82 | 1xV100 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 whisper encoder-decoder blocks:
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+ - The pretrained whisper-medium encoder is frozen.
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+ - The pretrained Whisper tokenizer is used.
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+ - A pretrained Whisper-medium decoder ([openai/whisper-medium](https://huggingface.co/openai/whisper-medium)) is finetuned on CommonVoice ar.
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+ The obtained final acoustic representation is given to the greedy decoder.
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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 Arabic)
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+
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+ ```python
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+
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+ from speechbrain.pretrained import WhisperASR
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+
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+ asr_model = WhisperASR.from_hparams(source="speechbrain/asr-whisper-medium-commonvoice-ar", savedir="pretrained_models/asr-whisper-medium-commonvoice-ar")
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+ asr_model.transcribe_file("speechbrain/asr-whisper-lmedium-commonvoice-ar/example-ar.mp3")
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+
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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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+ ### 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/transformer/
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+ python train_with_whisper.py hparams/train_ar_hf_whisper.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/11PKCsyIE703mmDv6n6n_UnD0bUgMPbg_?usp=share_link).
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+
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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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+
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+ #### Referencing SpeechBrain
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+
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+ ```
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+ @misc{SB2021,
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+ 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 },
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+ title = {SpeechBrain},
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+ year = {2021},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
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+ }
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+ ```
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+
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+ #### About SpeechBrain
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+ 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.
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+
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+ Website: https://speechbrain.github.io/
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+
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+ GitHub: https://github.com/speechbrain/speechbrain