Instructions to use alexandradiaconu/whisper-large-child-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexandradiaconu/whisper-large-child-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alexandradiaconu/whisper-large-child-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alexandradiaconu/whisper-large-child-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("alexandradiaconu/whisper-large-child-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from alexandradiaconu/whisper-large-child-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.59 kB
-
https://huggingface.co/alexandradiaconu/whisper-large-child-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://alexandradiaconu/whisper-large-child-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/alexandradiaconu/whisper-large-child-v2/resolve/main/training_args.bin
5.59 kB
- Xet hash:
- 3705197d9e5f0c87c36abe2d0b452bb8b09034e73539557bbc63f3aa0b016714
- Size of remote file:
- 5.59 kB
- SHA256:
- 76a3724dfa8014055288b722773c0811366f46d85f8dbfdb8dd10344450d9154
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.