Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
Estonian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/whisper-small-smj-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/whisper-small-smj-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/whisper-small-smj-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/whisper-small-smj-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/whisper-small-smj-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/whisper-small-smj-test: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/NbAiLab/whisper-small-smj-test/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/whisper-small-smj-test/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/whisper-small-smj-test/resolve/main/flax_model.msgpack
967 MB
- Xet hash:
- 52d0341b90df69bd70379e3efda83a01faabdeba35824aadf1b93ebf7148dcef
- Size of remote file:
- 967 MB
- SHA256:
- d6aa0f10e3c7c40fb77f5192a890ecdf487854ce9cf0e7a299ba1780a0b14d70
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