Instructions to use Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5") model = AutoModelForSpeechSeq2Seq.from_pretrained("Subhadeep/whisper-tiny-fet-small-model-Hi-Bank_v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 412ce7d85f52ad9dc9ee891bcc174a19b5baa5b4f603d93ff8bcb7c8135ff3fb
- Size of remote file:
- 3.64 kB
- SHA256:
- d6554d0b1f866d7884a06ea9946d64a7873dd219e0843f303938ebaca7bbdd99
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