Dineshoct/voctalk-indic-conformer

IndicConformer 600M for Voctalk (Telugu, Hindi)

A repackaged copy of AI4Bharat's IndicConformer-600M-Multi, so the Voctalk dictation app can run it on a laptop without PyTorch.

The weights are AI4Bharat's, unchanged and in full precision. Only the packaging differs:

File	What it is
encoder.onnx, encoder.onnx.data	The Conformer encoder, with its weights in one file instead of about 300
ctc_decoder.onnx	The CTC head, as released
features.json	The constants of the original TorchScript audio preprocessor: window, 80-band mel filterbank, pre-emphasis, log guard and normalisation epsilon
tokens.json	The Telugu and Hindi vocabularies, and which of the shared output columns belong to each

Using it

1. Features: 16 kHz mono audio, pre-emphasis 0.97, reflect padding of 256 samples on each side, then a 512-point STFT with a hop of 160 and the 400-sample window stored in features.json (already centred in 512). Take the power spectrum, apply the filterbank and log(x + log_guard). Normalise each band to zero mean and unit standard deviation over the frames that hold audio (len / 160 + 1 of them), dividing the variance by n − 1.
2. Encoder: feed the features as audio_signal [1, 80, T] with length [1].
3. Decoding: feed outputs to ctc_decoder.onnx as encoder_output. Keep the language's columns from the logits, take the best token per frame, drop repeats and the blank (index 256), and join the pieces, turning ▁ into spaces.

The model can't tell languages apart, so it has to be told which one is being spoken.

Licence and credit

IndicConformer is by AI4Bharat, IIT Madras, released under the MIT License. This copy is under the same licence:

MIT License

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