Automatic Speech Recognition
Transformers
PyTorch
Estonian
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use TalTechNLP/whisper-large-et with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalTechNLP/whisper-large-et with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TalTechNLP/whisper-large-et")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TalTechNLP/whisper-large-et") model = AutoModelForSpeechSeq2Seq.from_pretrained("TalTechNLP/whisper-large-et", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7db13f3200e92b9049bf348dc8df6e474cfd91b19858d48fc59da8af708783c
- Size of remote file:
- 6.17 GB
- SHA256:
- 8f8edba2e2b8974654d430b0ffe9d6bb1e7a394e84f226fe7a5acaf3bc94d6f3
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