Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_small_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_small_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_small_beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_small_beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_small_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/scream_small_beta: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/NbAiLabArchive/scream_small_beta/resolve/b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/scream_small_beta@b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/scream_small_beta/resolve/b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/flax_model.msgpack
967 MB
- Xet hash:
- 9712e5c75e4e10330869a1622c1c1cb0e4ffb4addfb2294add9128d6541234ae
- Size of remote file:
- 967 MB
- SHA256:
- f0fe6b0e5191e99999517decc7e587d8af277ba06dd18d18d6ee1578d99bdc6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.