Instructions to use OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Add language/base_model metadata to model card
Browse files
README.md
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---
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license: cc-by-4.0
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library_name: onnx-asr
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tags:
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- automatic-speech-recognition
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- onnx
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# nvidia-eo-conformer-ctc-large-onnx
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[onnx-asr](https://github.com/istupakov/onnx-asr) library and the
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[ovos-stt-plugin-onnx-asr](https://github.com/OpenVoiceOS/ovos-stt-plugin-onnx-asr)
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OpenVoiceOS STT plugin.
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Converted from [nvidia/stt_eo_conformer_ctc_large](https://huggingface.co/nvidia/stt_eo_conformer_ctc_large)
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Decoder: CTC (`model.onnx`) — `model_type: nemo-conformer-ctc`, features_size 80,
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subsampling_factor 4.
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```python
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import onnx_asr
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model = onnx_asr.load_model("OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx")
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print(model.recognize("audio.wav"))
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```
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Or via OpenVoiceOS config:
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```json
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{"stt": {"module": "ovos-stt-plugin-onnx-asr",
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"ovos-stt-plugin-onnx-asr": {"model": "OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx"}}}
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```
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---
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license: cc-by-4.0
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library_name: onnx-asr
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base_model:
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- nvidia/stt_eo_conformer_ctc_large
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tags:
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- automatic-speech-recognition
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- onnx
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# nvidia-eo-conformer-ctc-large-onnx
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ASR model exported to ONNX for the
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[onnx-asr](https://github.com/istupakov/onnx-asr) library and the
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[ovos-stt-plugin-onnx-asr](https://github.com/OpenVoiceOS/ovos-stt-plugin-onnx-asr)
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OpenVoiceOS STT plugin. Ships both fp32 and int8 weights.
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Language: **Multilingual**. Converted from [nvidia/stt_eo_conformer_ctc_large](https://huggingface.co/nvidia/stt_eo_conformer_ctc_large).
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```python
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import onnx_asr
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model = onnx_asr.load_model("OpenVoiceOS/nvidia-eo-conformer-ctc-large-onnx")
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print(model.recognize("audio.wav"))
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```
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