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 with WhisperForAudioClassification (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 split only. The validation split was used for epoch selection and calibration.
• 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)

• best epoch (0-based): 6
• val language-weighted F1 (raw): 0.616
• val language-weighted F1 (calibrated, in-sample): 0.6811
• calibration held-out gain (2-fold): 0.0369
• calibration applied: True
• per-class F1: {'yor': 0.862, 'ibo': 0.989, 'hau': 0.987, 'ego': 0.917, 'anw': 0.982, 'efi': 0.211, 'ibb': 0.69, 'idu': 0.943, 'igl': 0.215, 'nup': 0.0, 'tiv': 0.58, 'urh': 0.988, 'other': 0.892}

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.