Instructions to use washeed/audio-transcribe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use washeed/audio-transcribe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="washeed/audio-transcribe")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("washeed/audio-transcribe") model = AutoModelForSpeechSeq2Seq.from_pretrained("washeed/audio-transcribe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from washeed/audio-transcribe: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/washeed/audio-transcribe/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://washeed/audio-transcribe/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/washeed/audio-transcribe/resolve/main/flax_model.msgpack
6.17 GB
- Xet hash:
- 18c69ef9dcd6bd4241b47401d19ea762a2aac78247c1b7a3b9b6aecaf6e5c551
- Size of remote file:
- 6.17 GB
- SHA256:
- e9c7b745947df8856bb809974ac7f9ef2704f7834ab1760fc925f38f8f5517f5
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