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 training_state.bin from NbAiLabArchive/scream_small_beta: direct link, hf CLI and curl.
- Browser
- Download file 14.8 kB
-
https://huggingface.co/NbAiLabArchive/scream_small_beta/resolve/b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/training_state.bin
- Command line
-
hf download hf://NbAiLabArchive/scream_small_beta@b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/training_state.bin
-
curl -L -o training_state.bin https://huggingface.co/NbAiLabArchive/scream_small_beta/resolve/b03c7a5e0d2769e5c4e329e681b40aaa134d13e9/training_state.bin
14.8 kB
- Xet hash:
- f11abaff0a03d3eaa2239d08c9b2434aaed3f21630d643fd37cdb5d47c2beb35
- Size of remote file:
- 14.8 kB
- SHA256:
- c6157bda4cfd69ce41943f32484fc9df62db937a2906d473f106e9161fc89eb7
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