|
--- |
|
language: |
|
- bn |
|
tags: |
|
- audio |
|
- automatic-speech-recognition |
|
- hf-asr-leaderboard |
|
|
|
|
|
|
|
|
|
|
|
model-index: |
|
- name: whisper-small-bn |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice 11.0 |
|
type: mozilla-foundation/common_voice_11_0 |
|
config: bn |
|
split: test |
|
args: |
|
language: bn |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 35.14 |
|
pipeline_tag: automatic-speech-recognition |
|
license: apache-2.0 |
|
--- |
|
|
|
# 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} |
|
} |
|
``` |
|
|
|
|
