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Update README.md

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@@ -1,6 +1,6 @@
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  ---
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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:
@@ -16,22 +16,22 @@ 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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  <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>
@@ -40,7 +40,7 @@ model-index:
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  # whisper medium fine-tuned on CommonVoice-14.0 Arabic
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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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@@ -48,14 +48,14 @@ The performance of the model is the following:
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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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  ## Pipeline description
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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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  The system is trained with recordings sampled at 16kHz (single channel).
@@ -72,14 +72,14 @@ pip install speechbrain transformers
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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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- ### Transcribing your own audio files (in Arabic)
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  ```python
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  from speechbrain.pretrained import WhisperASR
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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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  ```
@@ -103,7 +103,7 @@ pip install -e .
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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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  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/11PKCsyIE703mmDv6n6n_UnD0bUgMPbg_?usp=share_link).
@@ -129,4 +129,4 @@ SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to b
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  Website: https://speechbrain.github.io/
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- GitHub: https://github.com/speechbrain/speechbrain
 
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  ---
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  language:
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+ - it
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  thumbnail: null
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  pipeline_tag: automatic-speech-recognition
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  tags:
 
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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-it
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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 (Italian)
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  type: mozilla-foundation/common_voice_14_0
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+ config: it
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  split: test
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  args:
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+ language: it
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: '8.26'
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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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  # whisper medium fine-tuned on CommonVoice-14.0 Arabic
41
 
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  This repository provides all the necessary tools to perform automatic speech
43
+ recognition from an end-to-end whisper model fine-tuned on CommonVoice (Italian Language) within
44
  SpeechBrain. For a better experience, we encourage you to learn more about
45
  [SpeechBrain](https://speechbrain.github.io).
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  | Release | Test CER | Test WER | GPUs |
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  |:-------------:|:--------------:|:--------------:| :--------:|
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+ | 1-08-23 | 2.42 | 8.26 | 1xV100 32GB |
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  ## Pipeline description
54
 
55
  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 it.
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  The obtained final acoustic representation is given to the greedy decoder.
60
 
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  The system is trained with recordings sampled at 16kHz (single channel).
 
72
  Please notice that we encourage you to read our tutorials and learn more about
73
  [SpeechBrain](https://speechbrain.github.io).
74
 
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+ ### Transcribing your own audio files (in Italian)
76
 
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  ```python
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  from speechbrain.pretrained import WhisperASR
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+ asr_model = WhisperASR.from_hparams(source="speechbrain/asr-whisper-medium-commonvoice-it", savedir="pretrained_models/asr-whisper-medium-commonvoice-it")
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+ asr_model.transcribe_file("speechbrain/asr-whisper-lmedium-commonvoice-it/example-it.mp3")
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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_it_hf_whisper.yaml --data_folder=your_data_folder
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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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  Website: https://speechbrain.github.io/
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+ GitHub: https://github.com/speechbrain/speechbrain