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")# 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
- Xet hash:
- d59a76a4311557bdcd8702d5d525458e0df43d1d2c4f3cd22dbae382f8434d5a
- Size of remote file:
- 488 MB
- SHA256:
- 4b16c7feeed2a6561ef2d1de05e3b86b40b3b0634f20400a6bf0622a03cf9e0e
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