Instructions to use Isaacyn/whisper-small-id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Isaacyn/whisper-small-id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Isaacyn/whisper-small-id")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Isaacyn/whisper-small-id") model = AutoModelForSpeechSeq2Seq.from_pretrained("Isaacyn/whisper-small-id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model.onnx from Isaacyn/whisper-small-id: direct link, hf CLI and curl.
- Browser
- Download file 615 MB
-
https://huggingface.co/Isaacyn/whisper-small-id/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://Isaacyn/whisper-small-id/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/Isaacyn/whisper-small-id/resolve/main/decoder_model.onnx
615 MB
- Xet hash:
- 8b782e9c27071d5c2c39651c4ea685cd37a5b485605f84b20f08dba50f42314a
- Size of remote file:
- 615 MB
- SHA256:
- d01b19d2f563a001a7d4ffb798dcf03dc55daddf5f5735accfe9d8f43f370fc9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.