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("DragonLine/train04")
model = AutoModelForSpeechSeq2Seq.from_pretrained("DragonLine/train04")Quick Links
train04
This model is a fine-tuned version of openai/whisper-base on the ksponspeech_04 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4226
- Cer: 14.6674
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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.4513 | 0.13 | 100 | 0.4522 | 15.1448 |
| 0.4537 | 0.26 | 200 | 0.4484 | 14.7932 |
| 0.4419 | 0.39 | 300 | 0.4439 | 14.4669 |
| 0.4402 | 0.52 | 400 | 0.4399 | 14.8185 |
| 0.4397 | 0.65 | 500 | 0.4353 | 14.4394 |
| 0.4359 | 0.77 | 600 | 0.4282 | 14.0848 |
| 0.4245 | 0.9 | 700 | 0.4226 | 14.6674 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for DragonLine/train04
Base model
openai/whisper-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DragonLine/train04")