| --- |
| license: apache-2.0 |
| language: |
| - sr |
| base_model: |
| - openai/whisper-small |
| datasets: |
| - google/fleurs |
| - Sagicc/audio-lmb-ds |
| - espnet/yodas_owsmv4 |
| - classla/ParlaSpeech-RS |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper Small |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: Common Voice 24.0 |
| type: mozilla-foundation/common_voice_24_0 |
| config: sr |
| split: test |
| args: sr |
| metrics: |
| - name: Wer |
| type: wer |
| value: 0.0709 |
| library_name: transformers |
| --- |
| |
| # whisper-small-sr |
|
|
| Fine-tuned **OpenAI Whisper Small**. |
|
|
| **Output script:** this model is intended to produce **Serbian Latin** only. |
|
|
| - **WER** on Common Voice 24.0 Serbian test: **7.09%** |
|
|
| ## Model description |
|
|
|
|
| ## Training and evaluation data |
|
|
| This model was fine-tuned on a **mixture of publicly available Serbian speech corpora**, including: |
|
|
| - Mozilla Common Voice 24.0, evaluated on **CV test (sr)** |
| - FLEURS Serbian |
| - ParlaSpeech-RS (subset of the full dataset) |
| - Additional Serbian corpora used in the training pipeline |
|
|
|
|
| ## Training procedure |
|
|
| - Epochs: 8 |
| - Batch size: 32 |
| - Optimizer: AdamW |
| - LR: 6e-5 with warmup (50 steps) + cosine decay to min_lr = 1e-7 |
| - Mixed precision: bfloat16 |
| - SpecAugment: frequency + time masking |
| - Sampling: weighted sampling across datasets |
| |
| ### Training results |
| |
| | Epoch | Train loss | CV WER | |
| |------:|------------------:|-------:| |
| | 1 | 0.331 | 0.1562 | |
| | 2 | 0.338 | 0.1202 | |
| | 3 | 0.241 | 0.1062 | |
| | 4 | 0.187 | 0.0913 | |
| | 5 | 0.150 | 0.0853 | |
| | 6 | 0.122 | 0.0745 | |
| | 7 | 0.106 | 0.0709 | |
| |
| ## Evaluation Metrics |
| |
| - **WER (normalized)** on **Common Voice 24.0 Serbian test**: **7.09%** |
| - Text normalization used for WER: |
| - punctuation removed |
| - lowercased |
| - Cyrillic → Latin conversion |
| - numbers converted to words |
| |
| |
| |
