Mans Model Mostafa

Qwen3-TTS — KSA Arabic Fine-tune

A fine-tuned version of Qwen/Qwen3-TTS-12Hz-1.7B-Base for Saudi Arabian (Khaleeji/KSA) Arabic speech synthesis.

Training data: vadimbelsky/KSA_Arabic_English_Dataset_13k — ~13 k Arabic utterances in the KSA dialect, filtered to 1–20 s duration.

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How Arabic support was added

The base model ships with a fixed set of languages in its codec token vocabulary; Arabic was not among them. Adding it required changes at three levels:

1. Arabic language embedding — warm-start initialisation

Arabic was assigned codec token ID 2072. Rather than initialising this embedding randomly, it was set to the mean of all existing language embeddings before training:

ARABIC_LANG_ID = 2072
codec_emb = qwen3tts.model.talker.model.codec_embedding
existing_ids = [v for k, v in config.talker_config.codec_language_id.items() if k != 'arabic']
avg = codec_emb.weight[existing_ids].float().mean(0)
codec_emb.weight[ARABIC_LANG_ID] = avg

2. Language-conditioned codec prefix (4-token think block)

A 4-token block injects the explicit language ID through the codec channel:

pos 3: codec_think_id
pos 4: codec_think_bos_id
pos 5: lang_id          ← Arabic token 2072
pos 6: codec_think_eos_id
pos 7: speaker embedding slot  ← shifted +1 vs. base model

The sequence offset in the collator was adjusted from +8 to +9, and codec_embedding_mask[7] = False so the speaker embedding is injected directly from the speaker encoder.

3. Language auto-detection

dataset.py detects Arabic automatically from Unicode range \u0600–\u06FF, so no explicit language field is needed per sample.

4. KSA speaker registration

Speaker ID 3000 (ksa_speaker) was registered. The embedding is extracted from a reference KSA audio clip by the frozen speaker encoder and written directly into the safetensors weights — the saved model is fully self-contained.

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Training setup

Setting	Value
Base model	Qwen/Qwen3-TTS-12Hz-1.7B-Base
Training data	vadimbelsky/KSA_Arabic_English_Dataset_13k (Arabic subset)
Optimizer	AdamW, lr=2e-6, weight decay=0.01
Precision	bf16 mixed precision
Gradient accumulation	4 steps (effective batch ~32)
Gradient clipping	1.0
Epochs	5 (this checkpoint: epoch 4)
Loss	talker_loss + 0.3 × sub_talker_loss

All model parameters were fine-tuned (no LoRA). The speaker encoder was kept frozen during training.

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Inference

Install dependencies:

pip install qwen-tts soundfile torch

Single utterance:

import torch
import soundfile as sf
from qwen_tts.inference.qwen3_tts_model import Qwen3TTSModel

tts = Qwen3TTSModel.from_pretrained(
    "vadimbelsky/qwen3-TTS-KSA",
    dtype=torch.bfloat16,
    device_map="cuda:0",
    attn_implementation="sdpa",
)

wavs, sr = tts.generate_custom_voice(
    text="الحين سويت فنجال قهوة، توني صحيت من النوم",
    speaker="ksa_speaker",
    language="arabic",
)
sf.write("output.wav", wavs[0], sr)

CLI with infer_ksa.py:

python infer_ksa.py \
    --checkpoint vadimbelsky/qwen3-TTS-KSA \
    --text "وين تبي تلتقي الحين؟" \
    --output out_ksa.wav

Output is 24 kHz mono WAV.

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Supported speakers & languages

Speaker name	Language
ksa_speaker	arabic

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License

Apache 2.0 — same as the base model.