Automatic Speech Recognition
Transformers
PyTorch
Estonian
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use TalTechNLP/whisper-medium-et with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalTechNLP/whisper-medium-et with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TalTechNLP/whisper-medium-et")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TalTechNLP/whisper-medium-et") model = AutoModelForSpeechSeq2Seq.from_pretrained("TalTechNLP/whisper-medium-et", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e28bb3cf9c5b77861bc8319ea222354735643dd3b1f7292878546e3e8acbbb30
- Size of remote file:
- 3.06 GB
- SHA256:
- 5084a155264b2630b8583db0cae8d3dafb2e9a304e781986e7e34e8a45a1b68f
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