Model Card for whisper-small-ko-finetuned
This is a fine-tuned version of the SungBeom/whisper-small-ko model on a custom Korean speech recognition dataset.
It performs automatic speech recognition (ASR) for Korean audio data and achieves strong performance on the validation set.
Model Details
Model Description
This model is based on the Whisper-small architecture and fine-tuned on 62,327 Korean audio-transcript pairs using Hugging Face Transformers and PyTorch.
It is designed for general-domain Korean speech recognition (conversational, broadcast, news, etc.).
- Developed by: [Jeongwon Kim]
- Shared by: [kimthegarden]
- Model type: Encoder-decoder Transformer (WhisperForConditionalGeneration)
- Language(s): Korean (
ko) - License: MIT
- Fine-tuned from model:
SungBeom/whisper-small-ko
Model Sources
- Repository: [https://huggingface.co/kimthegarden/whisper-small-ko-low-qual-voice]
- Notebook: Fine-tuned using a custom
whisper_finetuning.ipynb - Demo [optional]: [Gradio or Streamlit demo link if available]
Uses
Direct Use
- Korean automatic speech recognition (ASR)
- Offline or batch transcription of Korean speech data
- Integration into Korean-language voice assistant systems
Downstream Use
- Further fine-tuning on domain-specific datasets (e.g. legal, medical, education)
- Research into Korean ASR model robustness or multilingual Whisper models
Out-of-Scope Use
- Transcription of non-Korean speech (this model is Korean-only)
- Real-time streaming ASR (not latency-optimized)
- Zero-shot or few-shot adaptation to other languages
Bias, Risks, and Limitations
- The model may show reduced accuracy on:
- Regional dialects or accents not represented in the training data
- Very noisy environments
- Children’s speech or non-native pronunciation
- The model has not been tested for fairness across different speakers (gender, age, etc.)
Recommendations
We recommend testing the model on your specific data domain before deployment.
Additional fine-tuning or data filtering may be required for sensitive use cases (e.g. education, healthcare).
How to Get Started with the Model
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
model = WhisperForConditionalGeneration.from_pretrained("your-username/whisper-small-ko-finetuned")
processor = WhisperProcessor.from_pretrained("your-username/whisper-small-ko-finetuned")
# Input: 16kHz waveform (float32 numpy or tensor)
inputs = processor(audio_waveform, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
predicted_ids = model.generate(inputs.input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])