Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Chinese
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use arun100/whisper-tiny-cn-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arun100/whisper-tiny-cn-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arun100/whisper-tiny-cn-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arun100/whisper-tiny-cn-1") model = AutoModelForSpeechSeq2Seq.from_pretrained("arun100/whisper-tiny-cn-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arun100/whisper-tiny-cn-1: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/arun100/whisper-tiny-cn-1/resolve/main/training_args.bin
- Command line
-
hf download hf://arun100/whisper-tiny-cn-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arun100/whisper-tiny-cn-1/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- 4fd7bfb50093c2f061cf75534e15ccd45f3334ac3414fa5ceb78456fcbc87892
- Size of remote file:
- 4.79 kB
- SHA256:
- b8e7c74bca0599babe8426ed913f7ef81420119316245965e80877e4ec0f3f11
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.