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:
- e34ce28612f32122d52901b0276ed238277e42c143f5e17259027acde7767f98
- Size of remote file:
- 967 MB
- SHA256:
- 132339cde52853fdb38adcecd97ac1c982a015bd8a688b3d31082b7565590b89
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