Lahgtna-OmniVoice Egyptian — v2 (multi-voice, diacritized)
Multi-voice Egyptian-Arabic (arz) TTS, fine-tuned on a combined ~34 h, two-narrator
corpus. Speaks two trained voices (Eqkawkab + Noselleel) and clones any reference voice
zero-shot. This repo is the hub for the project — it also hosts the benchmark,
streaming interface, code, training guide, and sample sets.
For the best multi-voice pronunciation, see v3 (non-diacritized) — it beats v2 by ~38% lower CER on pure-Arabic.
👪 Model family
| repo | voices | text | |
|---|---|---|---|
| base | lahgtna-omnivoice-v2 | — | diacritized |
| v1 | egyptian | 1 | diacritized |
| v2 ← you are here | egyptian-v2 | 2 | diacritized |
| v3 (recommended) | egyptian-v3 | 2 | raw |
| 🎛️ Compare GUI | Space | — | — |
| 📚 Data | eqkawkab · noselleel | — | — |
What's in this repo
model.safetensors+ config + tokenizer — the v2 modelbenchmark/— 4-way ASR-CER benchmark + 60 audio clipsSTREAMING.md+samples_streaming/— low-latency streaming (TTFC ~0.8s)samples_v2_multivoice/,samples_v2_long/— both-voice demosTRAINING_GUIDE.md+code/— full reproducible recipe + scripts
Usage
import torch, soundfile as sf
from omnivoice.models.omnivoice import OmniVoice
m = OmniVoice.from_pretrained("ehabnegm/lahgtna-omnivoice-egyptian-v2", device_map="cuda", dtype=torch.float16)
audio = m.generate(text="أهلاً بيك", language="arz", ref_audio="voice.wav", ref_text="نص المرجع")
sf.write("out.wav", audio[0], 24000)
Training
~34 h / 10,828 clips (Eqkawkab + Noselleel), diacritized text. Continued from v1 checkpoint-6000,
8,000 steps, SDPA, bf16, 1× L4. Train loss 3.93 → 1.92.
Benchmark (ASR round-trip CER, 13 pure-Arabic sentences — lower = clearer)
| model | CER ↓ |
|---|---|
| v1 | 0.056 |
| v3 | 0.063 |
| v2 | 0.102 |
| base | 0.109 |
Full benchmark + audio in benchmark/. |
321-word long-form stress test
A full Egyptian-Arabic article (~321 words, ~2:50) synthesized by each model, then transcribed back with Whisper large-v3 (ASR round-trip CER — lower = stays on-text).
| model | audio | RTF | CER ↓ |
|---|---|---|---|
| v3 | 173 s | 0.25 | 0.21 — stays coherent ✅ |
| base | 170 s | 0.35 | 0.69 — drifts ⚠️ |
| v1 | 170 s | 0.35 | 0.71 — drifts ⚠️ |
| v2 | 170 s | 0.43 | 0.71 — drifts ⚠️ |
Finding: on paragraph-length input the diacritized models (base/v1/v2) lose coherence and
babble; v3 (non-diacritized) is the only version that stays on-text — decisive for long-form
and agent replies. Listen: base.wav, v1.wav, v2.wav, v3.wav.
Audio: long300/.
Limitations & responsible use
CATT diacritics on heavy colloquial are approximate; code-switched English is not trained. Audio derived from public YouTube channels (@Eqkawkab, @noselleel) — credit the creators, get permission before commercial use, and don't use the voices to impersonate or mislead.