# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Rifky/whisper-tiny-ko")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Rifky/whisper-tiny-ko")Quick Links
whisper-tiny-ko
This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4594
- Wer: 60.2614
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.6045 | 0.04 | 1 | 2.4594 | 60.2614 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rifky/whisper-tiny-ko")