Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
Safetensors
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_medium_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_medium_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_medium_beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_medium_beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_medium_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/scream_medium_beta: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/NbAiLabArchive/scream_medium_beta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/scream_medium_beta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/scream_medium_beta/resolve/main/flax_model.msgpack
3.06 GB
- Xet hash:
- 5da923be139984f8ef716c80bc99910b4cbd0dffb1e79eb9ca5a5fb988e96eae
- Size of remote file:
- 3.06 GB
- SHA256:
- b8955ca156d617eb94a83a3da03162287e1d935cececf7b4dc5a32b536b67a87
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