Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Bashkir
whisper
Eval Results (legacy)
Instructions to use stdbug/whisper-tiny-ba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stdbug/whisper-tiny-ba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stdbug/whisper-tiny-ba")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stdbug/whisper-tiny-ba") model = AutoModelForSpeechSeq2Seq.from_pretrained("stdbug/whisper-tiny-ba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from stdbug/whisper-tiny-ba: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/stdbug/whisper-tiny-ba/resolve/main/training_args.bin
- Command line
-
hf download hf://stdbug/whisper-tiny-ba/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/stdbug/whisper-tiny-ba/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 45e3ede78a3172e1c676ccf931ec019f9e9281cf85c9ce51db3b2e4119278582
- Size of remote file:
- 5.5 kB
- SHA256:
- dca58f7fce49607ef9f8f936747d4e166b08985d2581f260815a3b4f08d72ae1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.