Instructions to use mskov/whisper-small-esc50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mskov/whisper-small-esc50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mskov/whisper-small-esc50")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mskov/whisper-small-esc50") model = AutoModelForSpeechSeq2Seq.from_pretrained("mskov/whisper-small-esc50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9864bd460f8124e8634d39b1d90d81470a969a34a052d304da38958d1582a9b0
- Size of remote file:
- 967 MB
- SHA256:
- 7b4a92351711becc1a8bfedbb646a3f1b8110e8c1feac722740bdc7acf3dbff2
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