Instructions to use arielcerdap/conformer-ctc-small-fluencybank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use arielcerdap/conformer-ctc-small-fluencybank with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("arielcerdap/conformer-ctc-small-fluencybank") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Conformer-CTC fine-tuned on FluencyBank Timestamped
Fine-tuned from nvidia/stt_en_conformer_ctc_small on the FluencyBank Timestamped dataset
(arielcerdap/TimeStamped-Splits),
targeting verbatim transcription of stuttered speech from adults who stutter (PWS).
Results on test set
| Metric | Value |
|---|---|
| WER | 18.26% |
Training details
- Base model:
nvidia/stt_en_conformer_ctc_small - Dataset:
arielcerdap/TimeStamped-Splits(train=2,744 / val=342 / test=342) - Target: verbatim transcription (disfluencies preserved)
- Optimizer: AdamW (lr=1e-5, weight_decay=1e-3)
- Scheduler: CosineAnnealing (warmup_ratio=0.05)
- Batch effective: 64
- Early stopping: patience=10 on val_wer
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