Automatic Speech Recognition
Transformers
PyTorch
Lithuanian
whisper
whisper-event
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use DeividasM/whisper-medium-lt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeividasM/whisper-medium-lt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DeividasM/whisper-medium-lt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DeividasM/whisper-medium-lt") model = AutoModelForSpeechSeq2Seq.from_pretrained("DeividasM/whisper-medium-lt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from DeividasM/whisper-medium-lt: direct link, hf CLI and curl.
- Browser
- Download file 6.11 GB
-
https://huggingface.co/DeividasM/whisper-medium-lt/resolve/main/optimizer.pt
- Command line
-
hf download hf://DeividasM/whisper-medium-lt/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/DeividasM/whisper-medium-lt/resolve/main/optimizer.pt
6.11 GB
- Xet hash:
- 4adcf20b80919cda30a5b7d9394880b92656a20357662e35f1107d543c85eaa8
- Size of remote file:
- 6.11 GB
- SHA256:
- 44c93f54c18a63e4585ef4d37d3c30ae716d6ae29cb27fee7ef65183229a04c4
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