imTak/korean-audio-text-develop
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How to use imTak/whisper_large_v3_turbo_korean_Develop with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="imTak/whisper_large_v3_turbo_korean_Develop") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("imTak/whisper_large_v3_turbo_korean_Develop")
model = AutoModelForSpeechSeq2Seq.from_pretrained("imTak/whisper_large_v3_turbo_korean_Develop")This model is a fine-tuned version of imTak/whisper_large_v3_ko_ft_ft on the Develop dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2119 | 1.9455 | 500 | 0.2721 | 22.6690 |
| 0.0714 | 3.8911 | 1000 | 0.2542 | 19.9135 |
| 0.0145 | 5.8366 | 1500 | 0.2417 | 18.5037 |
| 0.0018 | 7.7821 | 2000 | 0.2410 | 16.6453 |
| 0.0263 | 9.7276 | 2500 | 0.2818 | 19.4169 |
| 0.0179 | 11.6732 | 3000 | 0.2806 | 18.5838 |
| 0.008 | 13.6187 | 3500 | 0.2977 | 18.1032 |
| 0.0072 | 15.5642 | 4000 | 0.2920 | 17.8949 |
| 0.0011 | 17.5097 | 4500 | 0.2875 | 16.8376 |
| 0.0024 | 19.4553 | 5000 | 0.3072 | 17.8629 |
| 0.0009 | 21.4008 | 5500 | 0.2943 | 16.8536 |
| 0.0002 | 23.3463 | 6000 | 0.3041 | 16.8055 |
| 0.0001 | 25.2918 | 6500 | 0.2993 | 16.6773 |
| 0.0001 | 27.2374 | 7000 | 0.3016 | 16.4851 |
| 0.0001 | 29.1829 | 7500 | 0.3043 | 16.4050 |
| 0.0001 | 31.1284 | 8000 | 0.3054 | 16.4370 |
Base model
openai/whisper-large-v3