DSN 2026 spoken language ID (Whisper-base encoder)
Spoken language identification for 12 Nigerian languages plus an other class,
for the DSN 2026 "Find the Language I Am Speaking" competition.
Approach
- Encoder of
openai/whisper-base, fine-tuned end to end withWhisperForAudioClassification(weighted layer sum over encoder layers). - Input window of 10 s: the processor pads or truncates every clip to its first 10 s, and the encoder keeps the matching first positions of Whisper's position table.
- Training: rare-language oversampling (inverse-sqrt class frequency), random crops, waveform augmentation (speed, gain, reverberation, telephone band-limiting with mu-law, coloured noise, babble from other training clips), SpecAugment, label smoothing 0.1.
- Per-class logit offsets tuned to maximise language-weighted F1, kept only if they also improve held-out halves of the validation split, and folded into the classifier bias.
Training data
- Train and validation splits combined, for the epoch count chosen by the train-only run. Calibration offsets were reused from that run.
- Source:
Data-Science-Nigeria/dsn-2026-lid-train. Its sources keep their own licences; see the dataset's ATTRIBUTION.md and LICENSE.md.
External data
- None. No prohibited source was used.
Validation (language-weighted F1)
- note: trained on train + validation, so it has no held-out score
- epochs: 7
- main run val language-weighted F1 (calibrated): 0.6811
Limitations
- Nupe has no validation clips, so its calibration offset is left at zero.
- Training data is mostly read speech, including religious recordings, so conversational speech may score lower.
- Licence of this repo: MIT, which is the licence of the base Whisper model. The training data's licences are separate.