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") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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dataset:
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name: mozilla-foundation/common_voice_11_0
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type: mozilla-foundation/common_voice_11_0
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metrics:
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- name: Test WER
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type: wer
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dataset:
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name: mozilla-foundation/common_voice_11_0
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type: mozilla-foundation/common_voice_11_0
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config: et
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split: test
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metrics:
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- name: Test WER
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type: wer
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