Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use smallsuper/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smallsuper/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="smallsuper/whisper-small-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("smallsuper/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("smallsuper/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from smallsuper/whisper-small-dv: direct link, hf CLI and curl.
- Browser
- Download file 6.03 kB
-
https://huggingface.co/smallsuper/whisper-small-dv/resolve/main/training_args.bin
- Command line
-
hf download hf://smallsuper/whisper-small-dv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/smallsuper/whisper-small-dv/resolve/main/training_args.bin
6.03 kB
- Xet hash:
- 0d70ba0ca5f93dfd6158b227d4e34dcba8bdff7b846f3bc3f1089047d26bd1e0
- Size of remote file:
- 6.03 kB
- SHA256:
- f50b4d033ea585ea37ea1a4897813d39ede85317d273096f36aaed501af21c47
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