Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use FCameCode/whisper-tiny-it-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FCameCode/whisper-tiny-it-8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FCameCode/whisper-tiny-it-8")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FCameCode/whisper-tiny-it-8") model = AutoModelForSpeechSeq2Seq.from_pretrained("FCameCode/whisper-tiny-it-8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0ca64b72b224284829e2409b2b663897c8dd8d292dfe83df39107252d390ce2
- Size of remote file:
- 151 MB
- SHA256:
- 1187d327777e93b9e60d6d42edaa77de5088d8b5497cc8aac1d45ec98c8c0df6
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