whisper-medium_ro-80mel — Romanian Fine-tune
Fine-tuned openai/whisper-medium
on a merged Romanian corpus (~445h). Input: 80-mel spectrograms.
Performance (beam=5, full val)
| Metric | Value |
|---|---|
| WER | 0.0656 |
| CER | 0.0228 |
| Evaluated on 25,348 validation samples (52.75h). |
Training
| Parameter | Value |
|---|---|
| Base | openai/whisper-medium |
| GPU | NVIDIA A100 80GB PCIe |
| Features | 80-mel |
| Effective batch | 128 (16×8) |
| Optimizer | adamw_torch |
| LR / scheduler | 4e-06 / cosine |
| Warmup | 500 |
| Max epochs | 8 |
| Precision | BF16 |
| Selection | eval_loss (early stop 5) |
| Date | 2026-09-21 |
Dataset
| Dataset | Samples | Hours |
|---|---|---|
| Echo | 163,050 | 374.33h |
| VoxPopuli | 27,060 | 85.20h |
| RODigits | 15,389 | 37.55h |
| RO-Smart (TTS) | 10,572 | 17.28h |
| CommonVoice-25 | 19,850 | 22.15h |
| FLEURS | 4,161 | 13.73h |
| USPDATRO | 2,637 | 4.31h |
| TOTAL | 242,719 | 554.55h |
Notes
- decoder_start_token_id=50258 (SOT); positional label masking; begin_suppress_tokens=[220].
- Checkpoint selection on eval_loss; final WER via standalone beam=5 generate.