Exporting audio model to python?

[td0m]

Is it possible to export the audio model to tflite and include a snippet explaining the usage in python?

[HalfdanJ]

This is high on our wishlist. The issue we haven't solved yet is that the pre-processing of the audio data to the format the network expects hasn't been written for python yet. As I understand it, fft processing of audio is handled differently natively in javascript and python, making it tricky.

The model that is used for audio training is https://github.com/tensorflow/tfjs-models/tree/master/speech-commands, and does unfortunately not yet have a python counter part.

Contributions to this is very welcomed!

[td0m]

Thanks for the reply @HalfdanJ, does the speech commands package work on node.js? I tried running it in a non-browser environment and it didnt seem to work.

[HalfdanJ]

I don't believe so

[caisq]

@d0minikt Can you say a little more about your use case? The audio model is tied to WebAudio's frequency analyzer (FFT). This means that in order to use the model in Python, you'll find a way to replicate the audio input parameters and frequency analysis.

[td0m]

@caisq if that's hard to do, is it easier to port the speech-commands package so that it also runs on node.js? I'm sure I'm not the only one with a headless use case.

[lc0]

There are ops for DSP in tensorflow directly[1], but I guess it's hard to maintain these for different platforms like TFLite and TFjs.

Also, most likely you rely on optimized FFT of browser.

1. https://www.tensorflow.org/api_docs/python/tf/signal

[lc0]

Also, any plans to support exporting saved model? Currently I only see export to Tensorflow.js

[nickoala]

As I understand it, fft processing of audio is handled differently natively in javascript and python, making it tricky.

The model that is used for audio training is ...... and does unfortunately not yet have a python counter part.

In case anyone hasn't noticed, the Coral example project Keyphrase detector seems to have the pre-processing code necessary. Not sure it's equivalent to those in Speech Commands, but at least they both compute Mel spectrogram.

I am just saying this, in case it may be helpful to someone.

[caisq]

@nickoala To be clear, I'm pretty sure the preprocessing steps in the Coral example doesn't fit TF.js Speech Commands, because Speech Commands is based on the browser's WebAudio FFT, which is a linear-frequency spectrum, not a Mel one.

[nickoala]

@caisq, but there is a SOFT_FFT option to speechCommands.create() right? This file does seem to compute Mel spectrogram.

[caisq]

@nickoala My apologies: The document is not very clear and some of the code is obsolete. The SOFT_FFT mode does use Mel spectrum. But the default mode of Speech Commands (BROWSER_FFT) uses linear spectrum from WebAudio.

[nickoala]

I see. Thank you for clarification.

[charlielito]

Hey guys, I had the same problem trying to run an audio model in a headless device with python. I could make it work but with node.js, but the trick could work also with python. The little trick was to launch a headless chromium with puppeteer where the javascript run the model and inside the node.js script the predictions are parsed and then you are free to go and do whatever with the predictions.

I made it to turn off/on my room's light. If you want to check out the code and how to do it go to: https://github.com/charlielito/teachable-machines-audio-demo

Any feedback is welcome!