Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Russian
whisper
Generated from Trainer
Instructions to use Apness/whisper-small-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apness/whisper-small-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Apness/whisper-small-ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Apness/whisper-small-ru") model = AutoModelForSpeechSeq2Seq.from_pretrained("Apness/whisper-small-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Apness/whisper-small-ru: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Apness/whisper-small-ru/resolve/main/training_args.bin
- Command line
-
hf download hf://Apness/whisper-small-ru/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Apness/whisper-small-ru/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 9d19abaf5166b19e366383702120b801cd4981e70ba9e474ed1a077fd413ea7f
- Size of remote file:
- 4.73 kB
- SHA256:
- 6313eed0d7d7860493618684504f2f2ab7f82a31dddbd35eaa6e828668d52b97
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.