Drop-in 4 B TTS model for Egyptian Arabic. This is ehabnegm/higgs-tts-3-4b-egyptian-v3 with a
LoRA adapter merged in, fine-tuned on 98 hours of denoised, consensus-verified Egyptian speech.
No adapter loading required — it works with the existing serving stack unchanged.
Why this checkpoint
Trained for 3 epochs; step 600 was selected, not the final step. Validation loss kept falling
through step 1134, but ASR round-trip word error rate started rising after step 600 — the signature
of mild overfitting to the training distribution. We shipped the checkpoint that generalizes best,
not the one with the prettiest loss curve.
Checkpoint
Val loss
CER mean
CER median
WER mean
base (higgs-tts-3-4b-egyptian-v3)
4.2168
0.0300
0.0177
0.1146
step 600 — this model
4.0995
0.0301
0.0145
0.1012
step 1134 (final)
4.0875
0.0331
0.0198
0.1219
Measured on 40 held-out clips, video-disjoint from training. CER/WER come from transcribing the
generated audio back with oddadmix/whisper-large-v3-turbo-arabic-dialectal-v2 and comparing to the
input text. WER improved 12% relative to base; CER mean is unchanged.
Generation (reference-conditioned, 25 Hz frames, 8 codebooks with delay pattern) is implemented in
serving/serve_openai.py — gen_raw() and synth_pcm().
1curl -X POST localhost:8000/v1/audio/speech \2 -H 'content-type: application/json'\3 -d '{"input":"إزيك يا صاحبي؟ عامل إيه النهاردة؟","voice":"masri"}' --output out.wav
You must supply a voice reference in refs/*.pt — a dict with codes[T, 8] and text_ids.
Any 6–10 s clip of the target speaker works.
⚠ scoring artifact — Arabic ASR transliterates English
The numbers and code-switch scores are measurement artifacts, not failures. Judge those by ear.
Limitations
Single narrator, single domain. Popular-science / history narration. Zero-shot cloning of other
voices still works (inherited from Higgs v3) but has not been re-benchmarked after fine-tuning.
Transcripts were machine-generated. The training text came from a 3-way ASR consensus, not
human transcription. Residual systematic errors can be learned.
CER did not improve, only WER. Gains are at the word level, not the character level.
Not evaluated for speaker similarity. We measured intelligibility, not voice likeness — the
metric that matters most for voice products was not quantified.
Inherits all Higgs Audio v3 licence restrictions.
Ethics
The voice derives from publicly available YouTube content by a real, identifiable person. Do not
publish, commercialize, or impersonate without the speaker's consent. Disclose synthetic audio.
