kresnik/zeroth_korean
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How to use seastar105/whisper-medium-ko-zeroth with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="seastar105/whisper-medium-ko-zeroth") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("seastar105/whisper-medium-ko-zeroth")
model = AutoModelForSpeechSeq2Seq.from_pretrained("seastar105/whisper-medium-ko-zeroth")μ μ§ μμ λΆλ€μ΄ λ€μ΄λ‘λνμ μ μ¬μ©νλ κ±Έλ‘ λ³΄μ λλ€. https://huggingface.co/seastar105/whisper-medium-komixv2 μ λ μ’μ μ νλλ₯Ό κΈ°λ κ°λ₯ν νμΈνλ λͺ¨λΈμ΄ μμΌλ―λ‘ μ΄μͺ½μ μΆμ²ν©λλ€.
This model is a fine-tuned version of openai/whisper-medium on the Zeroth Korean 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 | Cer |
|---|---|---|---|---|---|
| 0.0873 | 0.72 | 1000 | 0.1086 | 7.7549 | 2.5597 |
| 0.0258 | 1.44 | 2000 | 0.0805 | 4.5475 | 1.7588 |
| 0.0091 | 2.16 | 3000 | 0.0719 | 3.7946 | 1.5664 |
| 0.0086 | 2.88 | 4000 | 0.0704 | 3.5537 | 1.5232 |
| 0.0019 | 3.59 | 5000 | 0.0727 | 3.6440 | 1.4840 |