stt_pl_fastconformer_hybrid_large_pc_onnx

Polish speech-to-text model. ONNX export of nvidia/stt_pl_fastconformer_hybrid_large_pc — an NVIDIA NeMo FastConformer-Hybrid CTC model — for onnx-asr. Runs offline with ONNX Runtime; PyTorch and NeMo are not required.

Part of the OpenVoiceOS STT/ASR ONNX collection.

Files

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: 80, subsampling_factor: 8

There is no int8 variant: these architectures are convolution-dominated, and dynamic quantization produces ConvInteger nodes that ONNX Runtime cannot execute on CPU. int8 requires static QDQ quantization with calibration data.

Usage

With onnx-asr (pip install onnx-asr[cpu,hub]):

import onnx_asr

model = onnx_asr.load_model("OpenVoiceOS/stt_pl_fastconformer_hybrid_large_pc_onnx")
print(model.recognize("speech.wav"))  # 16 kHz PCM wav

With OpenVoiceOS, through ovos-stt-plugin-onnx-asr (mycroft.conf):

{
  "stt": {
    "module": "ovos-stt-plugin-onnx-asr",
    "ovos-stt-plugin-onnx-asr": {
      "model": "OpenVoiceOS/stt_pl_fastconformer_hybrid_large_pc_onnx"
    }
  }
}

Export and verification

Exported from the original checkpoint with NeMo's model.export() (CTC decoder head of the hybrid model; 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.

Accuracy, training data and limitations

See the source model card for benchmark results, training corpora and known limitations. This repo changes the runtime, not the weights.

Related projects

• onnx-asr — ASR inference with ONNX Runtime
• ovos-stt-plugin-onnx-asr — OpenVoiceOS STT plugin
• NVIDIA NeMo — framework the source model was trained with