Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("oceanstar/bridze")
model = AutoModelForSpeechSeq2Seq.from_pretrained("oceanstar/bridze")Quick Links
oceanstar-bridze
This model is a fine-tuned version of openai/whisper-base on the bridzeDataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1880
- Cer: 7.3894
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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss |
|---|---|---|---|---|
| 0.3652 | 0.06 | 500 | 11.3504 | 0.3574 |
| 0.2788 | 0.13 | 1000 | 9.1325 | 0.2645 |
| 0.2213 | 0.1 | 1500 | 9.3132 | 0.2388 |
| 0.2257 | 0.13 | 2000 | 8.6295 | 0.2194 |
| 0.1941 | 0.16 | 2500 | 7.5109 | 0.2068 |
| 0.1395 | 0.19 | 3000 | 7.3247 | 0.1969 |
| 0.1787 | 0.23 | 3500 | 7.5517 | 0.1905 |
| 0.1639 | 0.26 | 4000 | 7.3894 | 0.1880 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.10.1
- Datasets 2.14.2
- Tokenizers 0.13.3
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Model tree for oceanstar/bridze
Base model
openai/whisper-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oceanstar/bridze")