Instructions to use junsor/whisper-tiny-aishell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junsor/whisper-tiny-aishell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="junsor/whisper-tiny-aishell")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("junsor/whisper-tiny-aishell") model = AutoModelForSpeechSeq2Seq.from_pretrained("junsor/whisper-tiny-aishell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from junsor/whisper-tiny-aishell: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/junsor/whisper-tiny-aishell/resolve/main/training_args.bin
- Command line
-
hf download hf://junsor/whisper-tiny-aishell/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/junsor/whisper-tiny-aishell/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 5e14fd61a090f66dd23d8e6f0dbf73d74471193d1718cbd97976ead7bd360ffa
- Size of remote file:
- 4.09 kB
- SHA256:
- d3d5141b02f3852922c4b15d164fa30209deb785bb880e0b07ccb975301f1082
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.