Downloads · 30 days
154
55% of all-time downloads
OpenVoiceOS/primeline-parakeet-onnx
primeline-parakeet-onnx is a automatic speech recognition model from OpenVoiceOS. Use it when you need speech turned into text. It is set up for onnx. The card lists the license as cc-by-4.0.
ONNX export of primeline/parakeet-primeline, a 600M-parameter German ASR model built on the NVIDIA FastConformer encoder with a Token-and-Duration Transducer (TDT) decoder. The export runs on ONNX Runtime through onnx…
Downloads · 30 days
154
55% of all-time downloads
All-time downloads
280
Public
Repo size
3.2 GB
Likes
0
Public
Click a slice to open those files.
.data2.5 GB · 77%
From the Hugging Face model README
ONNX export of primeline/parakeet-primeline,
a 600M-parameter German ASR model built on the NVIDIA FastConformer encoder with a
Token-and-Duration Transducer (TDT) decoder. The export runs on ONNX Runtime through
onnx-asr, so transcription needs neither PyTorch
nor the NeMo toolkit. Output keeps punctuation and capitalisation.
import onnx_asr
model = onnx_asr.load_model("OpenVoiceOS/primeline-parakeet-onnx")
print(model.recognize("audio.wav"))
The int8 weights load with quantization="int8".
In OpenVoiceOS, select it through
ovos-stt-plugin-onnx-asr:
{
"stt": {
"module": "ovos-stt-plugin-onnx-asr",
"ovos-stt-plugin-onnx-asr": {
"model": "OpenVoiceOS/primeline-parakeet-onnx"
}
}
}
| File | Size | Notes |
|---|---|---|
encoder-model.onnx + encoder-model.onnx.data | 2.5 GB | FP32 encoder, weights in one external-data file |
decoder_joint-model.onnx | 73 MB | FP32 decoder + joint network |
encoder-model.int8.onnx | 654 MB | INT8 dynamic, per-channel |
decoder_joint-model.int8.onnx | 18 MB | INT8 dynamic, per-channel |
vocab.txt | SentencePiece tokens plus the <blk> CTC blank | |
config.json | nemo-conformer-tdt, 128 mel features, subsampling factor 8 |
Exported with NeMo ASRModel.restore_from(...) followed by model.export(), which writes the
encoder and the fused decoder-joint graph separately. INT8 variants come from ONNX Runtime
dynamic quantization with per-channel weights.
Transcribed five German sentences rendered by gTTS. Both the FP32 and the INT8 variant scored 0% word error rate and reproduced the punctuation and capitalisation of the source text. This is a smoke test on synthetic speech, not a benchmark — for accuracy figures on real corpora see the WER table on the base model card.
CC-BY-4.0, inherited from the base model.