Instructions to use espnet/owsm_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/owsm_v3 with ESPnet:
from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/owsm_v3" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
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[OWSM](https://arxiv.org/abs/2309.13876) is an Open Whisper-style Speech Model from [CMU WAVLab](https://www.wavlab.org/). It reproduces Whisper-style training using publicly available data and an open-source toolkit [ESPnet](https://github.com/espnet/espnet).
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Our demo is available [here](https://huggingface.co/spaces/pyf98/OWSM_v3_demo).
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OWSM v3 has 889M parameters and is trained on 180k hours of public speech data. It supports various speech-to-text tasks:
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- Speech recognition
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[OWSM](https://arxiv.org/abs/2309.13876) is an Open Whisper-style Speech Model from [CMU WAVLab](https://www.wavlab.org/). It reproduces Whisper-style training using publicly available data and an open-source toolkit [ESPnet](https://github.com/espnet/espnet).
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Our demo is available [here](https://huggingface.co/spaces/pyf98/OWSM_v3_demo). The [project page](https://www.wavlab.org/activities/2024/owsm/) contains various resources.
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OWSM v3 has 889M parameters and is trained on 180k hours of public speech data. It supports various speech-to-text tasks:
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- Speech recognition
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