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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DeividasM/whisper-medium-lt") model = AutoModelForSpeechSeq2Seq.from_pretrained("DeividasM/whisper-medium-lt") - Notebooks
- Google Colab
- Kaggle
Whisper Medium Lithuanian CV11
This model is a fine-tuned version of openai/whisper-large on the mozilla-foundation/common_voice_11_0 lt dataset. It achieves the following results on the evaluation set:
- Loss: 0.354951
- Wer: 20.446244
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0056 | 9.42 | 1000 | 0.3252 | 20.5534 |
| 0.0023 | 18.8 | 2000 | 0.3549 | 20.4462 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- Wer on mozilla-foundation/common_voice_11_0self-reported20.446