Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("freshpearYoon/train4")
model = AutoModelForSpeechSeq2Seq.from_pretrained("freshpearYoon/train4")Quick Links
whisper_finetune
This model is a fine-tuned version of openai/whisper-base on the aihub_3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4807
- Cer: 14.7381
- Wer: 40.8215
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: 32
- eval_batch_size: 16
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 0.2038 | 0.4 | 500 | 0.4405 | 13.6475 | 39.1975 |
| 0.1892 | 0.8 | 1000 | 0.4491 | 14.5230 | 40.5892 |
| 0.1218 | 1.2 | 1500 | 0.4710 | 14.4216 | 40.2519 |
| 0.1227 | 1.6 | 2000 | 0.4879 | 14.3981 | 40.1969 |
| 0.1311 | 2.0 | 2500 | 0.4638 | 14.6655 | 40.9614 |
| 0.0945 | 2.4 | 3000 | 0.4783 | 14.6635 | 40.9190 |
| 0.0874 | 2.8 | 3500 | 0.4743 | 14.3360 | 40.4492 |
| 0.0759 | 3.2 | 4000 | 0.4807 | 14.7381 | 40.8215 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for freshpearYoon/train4
Base model
openai/whisper-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="freshpearYoon/train4")