Whisper Small Ro 80mel

--- language: [ro] license: apache-2.0 tags: [automatic-speech-recognition, romanian, whisper, fine-tuned] metrics: [wer, cer] model-index: - name: whisper-small_ro-80mel results: - task: {type: automatic-speech-recognition} dataset: {name: Romanian merged corpus (80-mel), type: custom} metrics: - {type: wer, value: 0.0925} - {type: cer, value: 0.0293} --- # whisper-small_ro-80mel — Romanian Fine-tune Fine-tuned [`openai/whisper-small`](https://huggingface.co/openai/whisper-small) on a merged Romanian corpus (~445h). Input: **80-mel** spectrograms. ## Performance (beam=5, full val) | Metric | Value | |--------|-------| | **WER** | **0.0925** | | **CER** | **0.0293** | Evaluated on 25,348 validation samples (52.75h). ## Training | Parameter | Value | |-----------|-------| | Base | `openai/whisper-small` | | GPU | NVIDIA A100 80GB PCIe | | Features | 80-mel | | Effective batch | 192 (48×4) | | Optimizer | adamw_torch | | LR / scheduler | 6e-06 / cosine | | Warmup | 500 | | Max epochs | 10 | | Precision | BF16 | | Selection | eval_loss (early stop 5) | | Date | 2026-09-20 | ## 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.

--- language: - ro license: apache-2.0 library_name: transformers pipeline_tag: automatic-speech-recognition base_model: openai/whisper-small tags: - whisper - romanian - asr - speech-recognition - generated_from_trainer datasets: - mozilla-foundation/common_voice_25_0 - google/fleurs - facebook/voxpopuli metrics: - wer - cer model-index: - name: whisper-small_ro-80mel results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: mixed name: Romanian ASR corpus (multi-source, held-out validation) metrics: - type: wer value: 9.21 name: WER (beam=5) - type: wer value: 8.8 name: WER normalized (lowercase, no punctuation) - type: cer value: 3.03 name: CER (beam=5) --- # Whisper Small — Romanian (whisper-small_ro-80mel) Fine-tuned [openai/whisper-small](https://huggingface.co/openai/whisper-small) for Romanian automatic speech recognition. Trained as part of the **Dorele** project — a fully Romanian-language, low-latency smart-home voice assistant — where Whisper models serve as on-premise STT engines. ## Results Evaluated on a held-out validation split of 25,348 samples, beam size 5: | Metric | Value | |---|---| | WER | **9.21 %** | | WER (normalized: lowercase, punctuation stripped) | 8.8 % | | CER | 3.03 % | The validation set is a deliberately hard multi-source mix (read speech, parliamentary/spontaneous speech, command-domain utterances), so numbers are not directly comparable to single-corpus benchmarks like Common Voice test. ## Training data ~185 K training samples drawn from a combined Romanian ASR corpus (~290 K samples / ~619 h total across splits), assembled from: - Common Voice 25 (Romanian) - Google FLEURS (ro_ro) - VoxPopuli (Romanian) - Echo read-speech corpus - RODigits (spoken digits) - USPDATRO - CRoWL - RO-Smart synthetic TTS data (smart-home command domain) Audio pre-processed to 80-bin log-mel features (30 s windows, Whisper standard). Splits: train 185,175 / validation 25,348 / test 78,167. ## Training procedure Full fine-tune (no LoRA) on a single RTX 5070 Ti 16 GB, BF16 autocast, SDPA attention. | Hyperparameter | Value | |---|---| | Parameters | 244 M | | Effective batch size | 192 (12 × 16 grad accum) | | Learning rate | 2e-5, cosine schedule | | Warmup steps | 750 | | Max epochs | 10 (early stopping, patience 5 on eval WER) | | Optimizer | AdamW (fused) | | Label...

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