Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bgstud/whisper-small-libirClean-vs-commonNative-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-small-libirClean-vs-commonNative-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-small-libirClean-vs-commonNative-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-small-libirClean-vs-commonNative-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-small-libirClean-vs-commonNative-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 733a6e0cc03f86d74ad81ee3698e037639703f62af167aeb71145db004cfc5e8
- Size of remote file:
- 3.57 kB
- SHA256:
- 04f979cb6d255b092257b754352ead6bd71edc58328a6cb2cee21683b7fc615c
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