Instructions to use switlydev/custom-stt-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use switlydev/custom-stt-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="switlydev/custom-stt-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("switlydev/custom-stt-model") model = AutoModelForSpeechSeq2Seq.from_pretrained("switlydev/custom-stt-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from switlydev/custom-stt-model: direct link, hf CLI and curl.
- Browser
- Download file 968 MB
-
https://huggingface.co/switlydev/custom-stt-model/resolve/main/ggml-model.bin
- Command line
-
hf download hf://switlydev/custom-stt-model/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/switlydev/custom-stt-model/resolve/main/ggml-model.bin
968 MB
- Xet hash:
- f81139ba86871812ca467e6fdbe1b7cbdf8c1ec9e38f97a490aa6aff45ca60ae
- Size of remote file:
- 968 MB
- SHA256:
- a666e61aaf7a0626332069ba21f1c098b37dfcc9cefa70c87f089880bd682cc6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.