JosueG/wav2vec2-xlsr-adja-c4v2

Wav2Vec2 XLS-R Adja ASR C4v2

This is the thesis-final deployable CTC ASR model for Adja speech. It is the main public ASR weight location used by the May 2026 CS thesis release.

Thesis Role

This model is the primary Adja speech-to-text artifact. It is used as an ASR baseline, an endpoint model in the S2TT pipeline, and one of the reverse-WER judges for TTS evaluation.

Model And Data

• Task: automatic speech recognition
• Base model: facebook/wav2vec2-xls-r-300m
• Training data: Orpheus Adja speech lineage, public canonical dataset JosueG/adja-speech-asr-tts
• Input audio: use 16 kHz audio at inference time unless your pipeline handles resampling explicitly
• Release repo: FrejusGdm/cs-thesis-may-2026

Headline Result

The C4v2 acoustic model reports 25.05% CER and 72.39% WER with greedy decoding in the project registry. With a character language model and Optuna shallow-fusion tuning, the best reported ASR setting reaches 22.67% normalized CER and 70.76% normalized WER.

See:

• results/adja-nmt/D4_ctc_lm/conclusion.md
• docs/source-repos/adja-nmt/experiment-registry.md

Limitations

• The public model repo is the acoustic model surface; some LM decoding experiments live in the result reports rather than inside this model repo.
• Deployed endpoint behavior can differ from offline evaluation because of tokenizer reconstruction, chunking, and resampling.
• CER is more informative than WER for this release because Adja word-boundary and orthographic normalization choices are still evolving.

Citation

If you use this model, cite:

Josue Godeme. 2026. CS Thesis May 2026: French-Adja MT and Adja Speech Experiments. https://github.com/FrejusGdm/cs-thesis-may-2026