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| File | Purpose |
|---|---|
model.onnx | Encoder + CTC head, fp32 |
vocab.txt | Token vocabulary (<token> <id> per line, ▁ = space, <blk> = CTC blank) |
config.json | onnx-asr metadata: model_type: nemo-conformer-ctc, features_size: 64, subsampling_factor: 2 |
ConvInteger nodes that ONNX Runtime cannot
execute on CPU. int8 requires static QDQ quantization with calibration data.Note: 64-mel NeMo models need onnx-asr withnemo64preprocessor support — currently the TigreGotico fork or the runtime backfill in ovos-stt-plugin-onnx-asr — until it lands upstream.
pip install onnx-asr[cpu,hub]):1import onnx_asr
2
3model = onnx_asr.load_model("OpenVoiceOS/stt_pl_quartznet15x5_onnx")
4print(model.recognize("speech.wav")) # 16 kHz PCM wav1{
2 "stt": {
3 "module": "ovos-stt-plugin-onnx-asr",
4 "ovos-stt-plugin-onnx-asr": {
5 "model": "OpenVoiceOS/stt_pl_quartznet15x5_onnx"
6 }
7 }
8}model.export()
(see the conversion guide).
The subsampling_factor was measured empirically on the exported graph, and
the export was verified differentially: the ONNX model and the original NeMo
checkpoint produce identical transcriptions on a reference clip.