pyAudioAnalysis VS SpeechRecognition

I actually came up with an el cheapo version of what I want to accomplish that isn't perfect but without any research can implement it and it may actually prove useful to language learners. PM me if you're interested in hearing it and critiquing it. I can share here that I'm using this guy's multiple repos though: https://github.com/tyiannak/pyAudioAnalysis

Update: From one researcher to another. I was referred to a Python Audio AI project . Once I determine exactly which module to use I should be smooth sailing. I'll send more updates soon.

Hey folks, I'm looking into clustering audio files with features extracted by pyAudioAnalysis. However, every feature (I'm interested in MFCC, spectral centroid and spread, and BPM) is extracted for each frame of the song (by default 0.05s, excluding BPM that relates to the whole) so tracks with different lengths produce arrays with different shapes.

To learn more about pyAudioAnalysis here you go.

Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy? This is where PyAudio Analysis comes in. PyAudio Analysis is an open source Python project by Theodoros Giannakopoulos, a Principle researcher of multimodal machine learning at the Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL). The package aims to simplify the feature extraction and classification process by providing a number of helpful tools at can sift through the signal and create relevant features. These features can then be used to train models for classification tasks.

This might be relevant, which has a set of feature extraction methods implemented: https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction

A library for audio feature extraction, regression, classification, segmentation\ (2 comments)

Start and Stop Listening Example

There is a great library that has support not only with OpenAIs whisper but many others that also work offline. https://github.com/Uberi/speech_recognition

This is the library: https://github.com/Uberi/speech_recognition

It is python remote plugin that is a tin wrapper around speech_recognition package.

I am less familiar with speech recognition myself. I have implemented something similar many years ago, back when Google had a REST API that allowed you to upload audio and they would respond with the recognized words/sentence. I think they still have the same API available, though. They limited how much you could send, but for voice commands it was pretty solid. However, SpeechRecognition looks like a library worth trying out for this, as that seems like it could do offline processing depending on the underlying library. They also have some examples to look at.

SpeechRecognition

I found this one here

https://github.com/Uberi/speech_recognition/blob/master/reference/library-reference.rst this might help. I guess your best bet is to rtfm.