Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hungarian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use abbenedek/whisper-tiny-hu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abbenedek/whisper-tiny-hu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="abbenedek/whisper-tiny-hu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("abbenedek/whisper-tiny-hu") model = AutoModelForSpeechSeq2Seq.from_pretrained("abbenedek/whisper-tiny-hu") - Notebooks
- Google Colab
- Kaggle
Whisper Small En - Benedek Borbely
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8894
- Wer: 71.2273
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8364 | 0.54 | 400 | 0.8894 | 71.2273 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.14.5
- Tokenizers 0.15.2
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Model tree for abbenedek/whisper-tiny-hu
Base model
openai/whisper-tinyEvaluation results
- Wer on Common Voice 11.0test set self-reported71.227