
# Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need 
for fine-tuning.

Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) 
by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).


# Usage

To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor).

The `WhisperProcessor` is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the `<|startoftranscript|>` token
2. The second token is the language token (e.g. `<|en|>` for English)
3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation
4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:
```
<|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|>
```
Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:

```python
model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
```

Which forces the model to predict in English under the task of speech recognition.



## Training Data

Common Voice 11.0 Bengali Train 
OpenSLR 53 Bengali Train 


### BibTeX entry and citation info
```bibtex
@misc{radford2022whisper,
  doi = {10.48550/ARXIV.2212.04356},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```


{"language": ["bn"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "whisper-small-bn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "bn", "split": "test", "args": {"language": "bn"}}, "metrics": [{"type": "wer", "value": 35.14, "name": "Test WER"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition audio hf-asr-leaderboard bn arxiv:2212.04356 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #bn #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need 
for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision 
by Alec Radford et al from OpenAI. The original code repository can be found here.


# Usage

To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'.

The 'WhisperProcessor' is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the '<|startoftranscript|>' token
2. The second token is the language token (e.g. '<|en|>' for English)
3. The third token is the "task token". It can take one of two values: '<|transcribe|>' for speech recognition or '<|translate|>' for speech translation
4. In addition, a '<|notimestamps|>' token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:

Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:



Which forces the model to predict in English under the task of speech recognition.



## Training Data

Common Voice 11.0 Bengali Train 
OpenSLR 53 Bengali Train 


### BibTeX entry and citation info



# Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need 
for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision 
by Alec Radford et al from OpenAI. The original code repository can be found here. # Usage

To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'.

The 'WhisperProcessor' is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the '<|startoftranscript|>' token
2. The second token is the language token (e.g. '<|en|>' for English)
3. The third token is the "task token". It can take one of two values: '<|transcribe|>' for speech recognition or '<|translate|>' for speech translation
4. In addition, a '<|notimestamps|>' token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:

Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:



Which forces the model to predict in English under the task of speech recognition. ## Training Data

Common Voice 11.0 Bengali Train 
OpenSLR 53 Bengali Train ### BibTeX entry and citation info
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #bn #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need 
for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision 
by Alec Radford et al from OpenAI. The original code repository can be found here. # Usage

To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'.

The 'WhisperProcessor' is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the '<|startoftranscript|>' token
2. The second token is the language token (e.g. '<|en|>' for English)
3. The third token is the "task token". It can take one of two values: '<|transcribe|>' for speech recognition or '<|translate|>' for speech translation
4. In addition, a '<|notimestamps|>' token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:

Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:



Which forces the model to predict in English under the task of speech recognition. ## Training Data

Common Voice 11.0 Bengali Train 
OpenSLR 53 Bengali Train ### BibTeX entry and citation info
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #bn #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need 
for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision 
by Alec Radford et al from OpenAI. The original code repository can be found here.# Usage

To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'.

The 'WhisperProcessor' is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the '<|startoftranscript|>' token
2. The second token is the language token (e.g. '<|en|>' for English)
3. The third token is the "task token". It can take one of two values: '<|transcribe|>' for speech recognition or '<|translate|>' for speech translation
4. In addition, a '<|notimestamps|>' token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:

Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:



Which forces the model to predict in English under the task of speech recognition.## Training Data

Common Voice 11.0 Bengali Train 
OpenSLR 53 Bengali Train### BibTeX entry and citation info