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:
- 8e8ed8430def1aea0fa8f1c6905b3b9ca07de03faf1b3482fb6e1c1d02680c11
- Size of remote file:
- 967 MB
- SHA256:
- d6c5c797601f34ed38bd6f4b9d1e3e5a86dd27c34769f9eb9a1ca51ce83a9a3a
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