---
license: cc-by-nc-sa-4.0
base_model: Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign
pipeline_tag: text-to-speech
library_name: transformers
language:
  - en
tags:
  - tts
  - qwen3-tts
  - voice-cloning
  - lora-finetuned
  - vocence
  - custom-voice
---

# fxstar1128/talentdev_01

**Custom voice LoRA fine-tuned Qwen3-TTS model** trained by fxstar1128 on specialized voice data. This checkpoint represents a targeted adaptation of the `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` base model using Low-Rank Adaptation (LoRA) techniques for enhanced voice characteristics.

## Model Overview

This model was fine-tuned with:
- **LoRA rank**: 32
- **LoRA alpha**: 64
- **Training strategy**: Single-epoch targeted voice optimization
- **Base architecture**: Qwen3-TTS with merged LoRA weights
- **Output format**: 24kHz mono WAV

The fine-tuning focused on capturing specific voice characteristics while maintaining the naturalness and expressiveness of the base Qwen3-TTS architecture.

## Quick Start

### Installation

```bash
pip install qwen-tts transformers torch soundfile
```

### Basic Usage

```python
from qwen_tts import Qwen3TTSModel
import soundfile as sf

# Load the fine-tuned model
model = Qwen3TTSModel.from_pretrained("fxstar1128/talentdev_01")

# Generate speech
audio, sample_rate = model.generate_voice_design(
    text="Hello, this is a demonstration of the fine-tuned voice model.",
    instruct="A natural speaking voice, clear and expressive.",
    language="english",
)

# Save output
sf.write("output.wav", audio[0], sample_rate)
```

## Training Details

This model was trained using the following configuration:

- **Optimizer**: AdamW (lr=2.5e-9)
- **Batch size**: 2
- **Gradient accumulation**: 4 steps
- **Max gradient norm**: 1.0
- **Trainable parameters**: ~46.8M (2.37% of total)
- **Dataset**: Custom voice dataset with Qwen audio codes
- **Speaker embedding**: Custom projection layer (1024 → 2048)

The LoRA adapters were merged back into the base weights, so this model runs at full inference speed with no PEFT overhead.

## Prompt Engineering

The model inherits the prompt-following capabilities of the base Qwen3-TTS model. Effective prompts typically include:

**Voice characteristics:**
- Gender and age indicators (*"a young woman"*, *"an older man"*)
- Speaking style (*"conversational"*, *"professional"*, *"warm"*)
- Emotional tone (*"calm"*, *"enthusiastic"*, *"thoughtful"*)

**Example prompts:**

```
A clear, natural voice speaking conversationally.
A professional speaker with measured pacing.
A warm, friendly voice with subtle expressiveness.
A calm narrator with natural intonation.
```

## Model Architecture

- **Base**: Qwen3-TTS Voice Design model
- **LoRA targets**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Modules saved**: talker.model.codec_embedding, speaker_embedding_projection
- **Attention**: SDPA (Scaled Dot-Product Attention)
- **Mixed precision**: bfloat16

## Performance Characteristics

**Strengths:**
- Natural voice quality with fine-tuned characteristics
- Fast inference (merged weights, no adapter overhead)
- Consistent voice across different prompts
- Maintains base model's expressiveness

**Considerations:**
- Optimized for specific voice characteristics learned during training
- Best results with prompts similar to training style
- English language focused

## Files Included

```
model.safetensors            # Merged model weights (base + LoRA)
config.json                  # Model configuration
tokenizer.json               # Text tokenizer
speech_tokenizer/            # Audio codec components
vocence_config.yaml          # Runtime configuration
chute_config.yml             # Deployment configuration
miner.py                     # Vocence integration
demo.py                      # Example inference script
```

## Deployment

This model is compatible with:
- **Bittensor SN78 (Vocence)** subnet miners
- **Chutes TEE** deployment framework
- Standard Hugging Face Transformers pipeline
- Direct qwen-tts inference

## License & Attribution

**License**: CC BY-NC-SA 4.0 (Non-Commercial)

**Base model**: Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign
**Fine-tuned by**: fxstar1128  
**Framework**: Qwen3-TTS by Alibaba

This model is intended for research and non-commercial applications only.

## Citation

```bibtex
@misc{fxstar1128_talentdev01,
  author = {fxstar1128},
  title = {talentdev_01: LoRA Fine-tuned Qwen3-TTS Voice Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/fxstar1128/talentdev_01}},
}
```

---

*Built with Qwen3-TTS • Trained with LoRA • Deployed on Vocence*