I am trying to create a speech recognition dataset, especially for Indian Accents. I am taking from colleagues to build this. Daily I send an article link and ask them to record and upload it to google drive.

I have a problem with this approach. All audio recordings of length 5 -7 min. I am using the DeepSpeech model for this and it requires 10-sec audio sentences.

Suggest me any approach if possible to segment audio files into corresponding sentence phrases or to build a better with 5 min length audio files. Suggestions are more than welcome on a better way to create a speech-to-text dataset.


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The typical approach is to just cut the clips into consecutive sections, and run the model on each such section. Sometimes a bit of overlap is used, say 10%. then you have to decide what to do with potential conflicts in these overlaps. A good model is usually robust against silence, otherwise you can try to cut silence in start and end of your 10-second window.

librosa.util.frame is a practical way of doing this in Python.


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