Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Ukrainian
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("gencgeray/whisper-tiny-uk")
model = AutoModelForSpeechSeq2Seq.from_pretrained("gencgeray/whisper-tiny-uk")Quick Links
Whisper tiny uk - Herai Hench KI-11
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: 0.7245
- Wer: 58.3932
- Cer: 18.1853
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: 6e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.7374 | 0.8065 | 1000 | 0.8454 | 64.6981 | 22.8643 |
| 0.6193 | 1.6129 | 2000 | 0.7735 | 61.6387 | 20.3717 |
| 0.5334 | 2.4194 | 3000 | 0.7444 | 60.3618 | 18.8322 |
| 0.4709 | 3.2258 | 4000 | 0.7318 | 59.5903 | 19.9146 |
| 0.4616 | 4.0323 | 5000 | 0.7242 | 58.3134 | 18.0517 |
| 0.4209 | 4.8387 | 6000 | 0.7245 | 58.3932 | 18.1853 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
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Model tree for gencgeray/whisper-tiny-uk
Base model
openai/whisper-tinyEvaluation results
- Wer on Common Voice 11.0self-reported58.393
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gencgeray/whisper-tiny-uk")