whisper-medium-dido-yvanchyk-v2

whisper-medium - Fine-tuned for Ukrainian ASR

This model is a fine-tuned version of openai/whisper-medium on the Dido Yvanchyk Audio Dataset v2 for Ukrainian speech recognition.

Model Description

Fine-tune openai/whisper-medium on Dido-Yvanchyk dataset

Training Details

Training Data

Training Hyperparameters

Training Results

The model was trained for 8000 steps with evaluation every 500 steps. The best checkpoint was selected based on the lowest CER.

Best Model Checkpoint: Step 7000

Final Evaluation Metrics

Validation Set

Test Set

Usage

Using Pipeline (Recommended)

from transformers import pipeline
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    "automatic-speech-recognition",
    model="KSE-RESEARCH-Group/whisper-medium-dido-yvanchyk-v2",
    device=device,
)

result = pipe(
    "path/to/audio.wav",
    generate_kwargs={
        "task": "transcribe",
        "language": "ukrainian",
    },
    chunk_length_s=30,
)
print(result["text"])

Using Transformers Directly

from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch

model_id = "KSE-RESEARCH-Group/whisper-medium-dido-yvanchyk-v2"

processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

# Move to GPU if available
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Process audio (audio_array should be a numpy array at 16kHz)
input_features = processor(
    audio_array, 
    sampling_rate=16000, 
    return_tensors="pt"
).input_features.to(device)

# Generate transcription
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)

Infrastructure

Hardware

Environment

Training Time

Experiment Details

Citation

If you use this model, please cite:

@misc{KSE-RESEARCH-Group-whisper-medium-dido-yvanchyk-v2,
  author = {KSE-RESEARCH-Group},
  title = {whisper-medium - Fine-tuned for Ukrainian ASR},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/KSE-RESEARCH-Group/whisper-medium-dido-yvanchyk-v2}
}

License

This model is released under the MIT license.

Acknowledgements

• Base model: openai/whisper-medium
• Dataset: Dido Yvanchyk Audio Dataset v2
• Training infrastructure: NVIDIA GeForce RTX 4090