Instructions to use erayyapagci/balbal-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use erayyapagci/balbal-v1 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("erayyapagci/balbal-v1") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Model card: efficiency summary, size/speed charts, bootstrap CIs, commercial API table, CPU thread scaling
ad021ca verified Download assets/mubench-vs-apis.webp from erayyapagci/balbal-v1: direct link, hf CLI and curl.
- Browser
- Download file 65.8 kB
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https://huggingface.co/erayyapagci/balbal-v1/resolve/main/assets/mubench-vs-apis.webp
- Command line
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hf download hf://erayyapagci/balbal-v1/assets/mubench-vs-apis.webp
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curl -L -o mubench-vs-apis.webp https://huggingface.co/erayyapagci/balbal-v1/resolve/main/assets/mubench-vs-apis.webp
65.8 kB
