zeropointnine/Darwin-TTS-1.7B-Cross-Qwen3Tokenizer

Darwin-TTS-1.7B-Cross — Qwen3-TTS compatibility repack

This repository is a compatibility repack of FINAL-Bench/Darwin-TTS-1.7B-Cross.

The original Darwin checkpoint appears to omit the speech_tokenizer/ directory required by the standard qwen-tts loader. This repack adds the missing speech_tokenizer/ files from Qwen/Qwen3-TTS-12Hz-1.7B-Base.

No model blending, training, fine-tuning, or behavioral changes were performed in this repack. The purpose is only to make the model load with:

from qwen_tts import Qwen3TTSModel

model = Qwen3TTSModel.from_pretrained("zeropointnine/Darwin-TTS-1.7B-Cross-Qwen3Tokenizer")

Provenance

• Main model weights and model card: FINAL-Bench/Darwin-TTS-1.7B-Cross
• Added tokenizer assets: Qwen/Qwen3-TTS-12Hz-1.7B-Base
• License: Apache 2.0, matching the upstream model cards.

Original Darwin-TTS-1.7B-Cross model card follows below:

🧬 Darwin-TTS-1.7B-Cross

World's first cross-modal FFN transfer from LLM to TTS — emotion-enhanced speech synthesis without any training.

This model is a cross-modal application of the Darwin Family framework, introduced in the paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning.

Authors: Taebong Kim, Youngsik Hong, Minsik Kim, Sunyoung Choi, Jaewon Jang, Junghoon Shin, Minseo Kim.

Darwin-TTS blends 3% of Qwen3-1.7B (LLM) FFN weights into Qwen3-TTS-1.7B (TTS) talker module. No training, no data, no GPU hours — just weight-space arithmetic.

Key Discovery

Blend (α)	Emotion	Quality	Status
0%	Baseline	Normal	Original Qwen3-TTS
1%	No change	Normal	Too subtle
3%	Emotion appears	Normal	★ This model (default)
5%	Emotion intensified	Normal	★★ Max stable
10%	Broken	Failed	Infinite generation

Why It Works

Qwen3-1.7B (LLM) and Qwen3-TTS-1.7B's talker share 100% identical architecture:

                    Qwen3-1.7B (LLM)    Qwen3-TTS talker    Match
hidden_size         2048                 2048                ✅
intermediate_size   6144                 6144                ✅
num_hidden_layers   28                   28                  ✅
num_attention_heads 16                   16                  ✅
num_key_value_heads 8                    8                   ✅

This means zero SVD, zero truncation, zero layer mapping — pure 1:1 lerp blending across all 84 FFN tensors (gate_proj, up_proj, down_proj × 28 layers).

Architecture

Qwen3-TTS-1.7B (4-module structure):
┌─────────────────────────────────────────────────────┐
│ talker (28L Qwen3 LM backbone)                      │
│   └── 84 FFN tensors blended with LLM (α=3%)       │ ← MODIFIED
│       └── talker.model.layers.N.mlp.{gate,up,down}  │
├─────────────────────────────────────────────────────┤
│ code_predictor (5L, h=1024)                          │ ← UNTOUCHED
├─────────────────────────────────────────────────────┤
│ speech_tokenizer (12Hz RVQ codec)                    │ ← UNTOUCHED
├─────────────────────────────────────────────────────┤
│ encoder/decoder (audio waveform)                     │ ← UNTOUCHED
└─────────────────────────────────────────────────────┘

FFN Source: Qwen3-1.7B (LLM)
└── model.layers.N.mlp.{gate,up,down}_proj.weight
    └── Key mapping: model.layers.N → talker.model.layers.N (1:1)

Only the talker's FFN weights are modified. The code_predictor, speech_tokenizer, and encoder/decoder remain 100% original — preserving the audio codec pipeline entirely.

Quick Start

Option 1: Load pre-blended weights (this model)

from qwen_tts import Qwen3TTSModel
import torch

# Load Darwin-TTS-1.7B-Cross (α=3% pre-blended)
model = Qwen3TTSModel.from_pretrained(
    "FINAL-Bench/Darwin-TTS-1.7B-Cross",
    device_map="cuda:0",
    dtype=torch.bfloat16
)

# Synthesize
wavs, sr = model.generate_voice_clone(
    text="안녕하세요, 저는 다윈 인공지능입니다!",
    ref_audio="your_voice.wav",
    ref_text="ref",
    x_vector_only_mode=True
)

Option 2: Custom blend ratio (runtime blending)

from qwen_tts import Qwen3TTSModel
model = Qwen3TTSModel.from_pretrained("FINAL-Bench/Darwin-TTS-1.7B-Cross")
wavs, sr = model.generate_voice_clone(
    text="정말 기쁜 소식이에요!",
    ref_audio="voice.wav",
    ref_text="ref",
    x_vector_only_mode=True
)

CLI

python darwin_tts_blend.py --alpha 3 --text "Hello, Darwin!" --ref voice.wav --output speech.wav

Installation

pip install torch qwen-tts safetensors soundfile huggingface_hub

Research Background

The Problem

Cross-modal capability transfer (e.g., adding emotion to TTS) traditionally requires:

• Thousands of hours of emotional speech data
• Hundreds of GPU hours for training
• Careful data curation and annotation

The Darwin Approach

Darwin's evolutionary merge framework, originally developed for LLM merging (Darwin LLM V7 achieved GPQA Diamond 86.9%, World #5), is extended to cross-modal transfer:

1. Find architecture-compatible models across modalities (LLM ↔ TTS)
2. Blend FFN weights at low ratios (3~5%) using simple lerp
3. Preserve modality-specific components (audio codec, tokenizer)

Key Findings

1. Cross-modal FFN transfer works — LLM's language understanding patterns enhance TTS emotional expressiveness
2. Sweet spot is 3~5% — TTS is far more sensitive than LLM merging (which tolerates 7~93%)
3. Same backbone is required — Qwen3 × Qwen3 succeeded; cross-backbone merges (e.g., Llama) failed.
4. 10%+ destroys TTS — LLM's "continue generating tokens" pattern overrides the TTS stop signal, causing 655-second outputs
5. Bidirectional potential — LLM + TTS FFN may enable "Speaking LLM" (the GPT-4o direction)

Model Details

• Model type: Text-to-Speech (cross-modal FFN blended)
• Base models: Qwen3-TTS-1.7B-Base + Qwen3-1.7B (3% FFN)
• Parameters: ~2.1B
• Languages: Korean, English, Japanese, Chinese + 6 more
• License: Apache 2.0
• Blend ratio: α=0.03 (3%)
• FFN tensors modified: 84 / 976 total (8.6%)
• Build time: ~2 minutes (no training)

Citation

If you find this work useful in your research, please cite:

@article{kim2026darwin,
  title={Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning},
  author={Kim, Taebong and Hong, Youngsik and Kim, Minsik and Choi, Sunyoung and Jang, Jaewon and Shin, Junghoon and Kim, Minseo},
  journal={arXiv preprint arXiv:2605.14386},
  year={2026}
}

Credits

VIDRAFT (비드래프트) — Darwin Evolutionary Merge Framework

Built on Qwen3-TTS-1.7B and Qwen3-1.7B by Alibaba Cloud (Apache 2.0).

Related

• Darwin-27B-Opus — Darwin LLM Flagship
• FINAL Bench — Text AGI Benchmark
• Darwin Evolutionary Merge Framework — CMA-ES + FFN crossbreeding