Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swahili
whisper
Generated from Trainer
Instructions to use ElvisTata2024/whisper-small-sw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElvisTata2024/whisper-small-sw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ElvisTata2024/whisper-small-sw")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ElvisTata2024/whisper-small-sw") model = AutoModelForSpeechSeq2Seq.from_pretrained("ElvisTata2024/whisper-small-sw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ElvisTata2024/whisper-small-sw: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/ElvisTata2024/whisper-small-sw/resolve/main/training_args.bin
- Command line
-
hf download hf://ElvisTata2024/whisper-small-sw/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ElvisTata2024/whisper-small-sw/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 6f639439e1288372c4cbcfcfd3116ad92ddc48b44757cc8d1723f5b4eab8d5f9
- Size of remote file:
- 5.43 kB
- SHA256:
- a9ec5a020ecd92b7fd678d3c9bc4f20425e47528fd0f6fcdea6eb7a9acef911d
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