Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use hackvermin/whisper-tiny-us with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hackvermin/whisper-tiny-us with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hackvermin/whisper-tiny-us")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hackvermin/whisper-tiny-us") model = AutoModelForSpeechSeq2Seq.from_pretrained("hackvermin/whisper-tiny-us", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hackvermin/whisper-tiny-us: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/hackvermin/whisper-tiny-us/resolve/main/training_args.bin
- Command line
-
hf download hf://hackvermin/whisper-tiny-us/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hackvermin/whisper-tiny-us/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 79ab0906f6d87bb17a091a261aa4add48c2dd374078eeb4b4e19ae4641865fce
- Size of remote file:
- 5.91 kB
- SHA256:
- 19c32ce832784bf767c63c9e4e574bcb597750c19e2be3016bc4e032e62b3037
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.