Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
whisper-event
Eval Results (legacy)
Instructions to use agnesluhtaru/whisper-small-et with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agnesluhtaru/whisper-small-et with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="agnesluhtaru/whisper-small-et")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("agnesluhtaru/whisper-small-et") model = AutoModelForSpeechSeq2Seq.from_pretrained("agnesluhtaru/whisper-small-et", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from agnesluhtaru/whisper-small-et: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/agnesluhtaru/whisper-small-et/resolve/cd9c886bfc4c1c1b1002aebb8a1df677da34740a/training_args.bin
- Command line
-
hf download hf://agnesluhtaru/whisper-small-et@cd9c886bfc4c1c1b1002aebb8a1df677da34740a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/agnesluhtaru/whisper-small-et/resolve/cd9c886bfc4c1c1b1002aebb8a1df677da34740a/training_args.bin
3.63 kB
- Xet hash:
- 834143eaa5f5177e51d76d2aa1decae2210dbb36373fb640395d4ecdb3781eae
- Size of remote file:
- 3.63 kB
- SHA256:
- 6ca508f95d4760cedd1e47bcf5634afa108f0ffc54f7ee3b919595a1f47e84f2
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