Instructions to use VictorMV/whisper-small-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorMV/whisper-small-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="VictorMV/whisper-small-trained")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("VictorMV/whisper-small-trained") model = AutoModelForSpeechSeq2Seq.from_pretrained("VictorMV/whisper-small-trained") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13c46a2ae8895688d8e45922146bb4f7e8374b6031569f3c95cd12304630f247
- Size of remote file:
- 1.93 GB
- SHA256:
- 9efad7a57ff12b8d169f639ebb54f0f46c99f9e545955490f3edcb3e7cc6691a
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