Instructions to use ashwin123/whisper-tiny-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashwin123/whisper-tiny-ml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ashwin123/whisper-tiny-ml")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ashwin123/whisper-tiny-ml") model = AutoModelForSpeechSeq2Seq.from_pretrained("ashwin123/whisper-tiny-ml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ashwin123/whisper-tiny-ml: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/ashwin123/whisper-tiny-ml/resolve/main/training_args.bin
- Command line
-
hf download hf://ashwin123/whisper-tiny-ml/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ashwin123/whisper-tiny-ml/resolve/main/training_args.bin
4.16 kB
- Xet hash:
- 763d385f7c04a661fdc7e63d1d935bcd68f2038a60cf77af21ccfecf55a34fc3
- Size of remote file:
- 4.16 kB
- SHA256:
- c73e5abd7260aef87770fd5b4bd3e94f565db6674415a36c98e31dfd2870a254
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.