More rich documentation on pipelines
Author: MonolithFoundation
Pull request: no
Labels: name: Feature request

### Feature request

For instance, in the case of pipelines, there are merely some community examples such as automatic-speech-recognition. There is no indication of who constitutes it or how the Voice Activity Detection (VAD) is involved. As a result, users are perplexed.

### Motivation

More detailed descriptions on pipline details.

### Your contribution

currently not

Comments:
@MonolithFoundation @stevhliu, I would like to contribute to improving the ASR pipeline documentation. Based on the feature request, I propose the following changes to address the lack of clarity, particularly regarding the role of Voice Activity Detection (VAD):

- Clarify the Role of VAD: Add a section to explain how VAD filters out non-speech segments of the audio to improve the accuracy and efficiency of the ASR system.

- Provide Example Code: Include a simple Python code snippet to demonstrate how VAD, feature extraction, and the ASR model work together in the pipeline.

(Optional):
- Visualization: Add a diagram to illustrate the flow of data through the pipeline, showing the interaction between the audio input, VAD, feature extraction, and ASR model, leading to the final transcription.

- FAQ Section: Include common user questions, such as how to adjust VAD sensitivity and how to customize the pipeline for different languages or accents.

Please let me know if you have any suggestions for further improvements!

@Uvi-12, thank you very much for the guidance on improvement. It is sufficient for newcomers to understand.

> @Uvi-12, thank you very much for the guidance on improvement. It is sufficient for newcomers to understand.

Thank you, I will work on the issue. Let me know if there are any additional points I should consider while working on this!

@MonolithFoundation This is the [ASR Pipeline Code](https://github.com/huggingface/transformers/blob/a9ccdfd8e3fbaf19ad6f174a99e8d39e706950b3/src/transformers/pipelines/automatic_speech_recognition.py#L127) , I wanted to ask for a few things-

- Shall we add VAD in the Argument section? Or should we have a completely different section?
- And please help me with the example code and let me know where to place it?

Yes, in my humble opinion:

- We need to specify which VAD model is used and how it compares with some state-of-the-art VAD models.
- It is necessary to determine in which situations VAD should be used in the ASR pipeline. Since if not, using vanilla wav decoding would lead to massive hallucinations in models like Whisper, such as decoding non-human voice audio into words.

@MonolithFoundation, thanks for your input! I agree that specifying the VAD model used and its comparison with state-of-the-art models is crucial. It would also be helpful to clarify when VAD should be used in the ASR pipeline to avoid issues like hallucinations, especially with models like Whisper.

However, I am currently unable to locate the relevant file to contribute to the documentation improvements. If you’re aware of where it is, please feel free to go ahead and contribute or guide me to the file so I can assist with the updates.

Looking forward to your suggestions!

I think here: https://huggingface.co/learn/audio-course/en/chapter2/asr_pipeline

or 

https://huggingface.co/docs/transformers/en/tasks/asr

would be very helpful

@stevhliu, Is the correct file to add the documentation on Voice Activity Detection (VAD) located at https://github.com/huggingface/transformers/blob/main/docs/source/en/tasks/asr.md ?

I'm not too familiar with VAD, but this type of content seems more educational to me, so I think it may be more appropriate to add it to https://huggingface.co/learn/audio-course/en/chapter2/asr_pipeline instead.

Both of them are good.