sravaani-onnx

SraVaani-1.0 ONNX

ONNX export of ARTPARK-IISc/SraVaani-1.0, a TDT/RNNT ASR model covering 44 Indic languages/dialects, built by SPIRE Lab + ARTPARK, IISc Bangalore (paper).

All credit for the model itself goes to the original authors. This repo only provides an ONNX conversion of the encoder + decoder_joint graphs for use with onnxruntime instead of PyTorch/TorchScript — useful for platforms where PyTorch is heavy or hard to build (e.g. Android/Termux). License follows the original: MIT.

Why this exists

The original model ships as a TorchScript checkpoint (model-asr.fp16.ts) wrapped in a transformers-compatible loader, needing torch + transformers + sentencepiece at runtime — a heavy stack on resource-constrained platforms. This repo provides the same model as two ONNX graphs (encoder.onnx, decoder_joint.onnx) runnable with just onnxruntime — smaller dependency footprint, and in testing, noticeably faster than the PyTorch CPU path (see benchmarks below).

Files

The graphs are fp32 (CPU has no fp16 conv kernel, so export runs the model upcast to fp32 — this roughly doubles encoder.onnx's size vs the original fp16 .ts checkpoint, trading disk space for CPU-inference correctness).

Usage

Decode is a greedy TDT loop: run the encoder once per utterance, then step the decoder_joint once per encoder frame (with duration-based skipping), same as any RNNT/TDT model. See onnx_transcribe.py in the companion GitHub repo for a complete reference implementation (feature extraction + greedy decode + tokenizer, using only onnxruntime, sentencepiece, torch for the STFT frontend, and soundfile).

import onnxruntime as ort

enc_sess = ort.InferenceSession("encoder.onnx", providers=["CPUExecutionProvider"])
dj_sess = ort.InferenceSession("decoder_joint.onnx", providers=["CPUExecutionProvider"])
# feats: [1, 128, T] mel-spectrogram, feature_lengths: [1]
enc_out, enc_lengths = enc_sess.run(None, {"feats": feats, "feature_lengths": feature_lengths})
# then greedy-decode by stepping dj_sess once per encoder frame - see
# onnx_transcribe.py for the full loop

Benchmarks (Android phone, CPU only, no GPU/NPU)

Tested on a mid-range Android phone (OnePlus CPH2467, arm64) via Termux, on a 4.4s audio clip:

Note: NNAPI hardware acceleration was slower than plain CPU for this model in testing — the decoder_joint's many small per-timestep calls incur more NNAPI dispatch overhead than they save. Recommend CPUExecutionProvider.

Transcripts from both the original PyTorch model and this ONNX export were verified byte-identical on real speech, and numerically match to within 2.5e-5 max absolute difference on raw encoder output (float32 rounding noise, not a real discrepancy).

Export process

See export_onnx.py in the companion GitHub repo to reproduce this conversion yourself. Note: export on a normal desktop PC, not Android/Termux — Termux's PyTorch build has a libc++/libcxxabi defect that corrupts TorchScript graph attribute reading during ONNX export (unrelated to this model; a general Android/Termux PyTorch packaging issue). Plain desktop PyTorch doesn't have this problem.