Automatic Speech Recognition
Transformers
JAX
TensorBoard
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_non_large_timestamp_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_non_large_timestamp_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_non_large_timestamp_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_non_large_timestamp_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_non_large_timestamp_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/scream_non_large_timestamp_test: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/NbAiLabArchive/scream_non_large_timestamp_test/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/scream_non_large_timestamp_test/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/scream_non_large_timestamp_test/resolve/main/flax_model.msgpack
6.17 GB
- Xet hash:
- ffe9398a8f0a06ea0912b627c4f83f890a4715a4a584857a09c419e335ee7d96
- Size of remote file:
- 6.17 GB
- SHA256:
- 28169cca19b54a2453bb7d983341904eb35d20c682773d9cccf244c2898e753f
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