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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
- prompttts
- qwen3-tts
- voice-design
- vocence
---
# vocence_miner_v3
A reliability-and-naturalness pass over the prompt-driven Qwen3-TTS-12Hz-1.7B-VoiceDesign backbone. v3 ships two changes that matter at inference time:
**1. Full-sentence generation.** Earlier checkpoints would sometimes render only the first clause of a longer input — the rest of the sentence would be cut off, dropped, or replaced with silence. v3 generates the entire input from start to end, including longer sentences with intermediate clauses, em-dashes, and parenthetical asides.
**2. More natural delivery.** Across the same prompt set, v3 produces audibly smoother prosody — fewer flat reads on neutral prompts, less "narrated" surface on short utterances, and more believable breath placement on persona reads.
Everything else stays the same: free-form English `instruct`, 24 kHz mono output, single-call inference, no reference audio.
---
## Use it
```bash
pip install qwen-tts transformers torch soundfile
```
```python
from qwen_tts import Qwen3TTSModel
import soundfile as sf
m = Qwen3TTSModel.from_pretrained("magma90909/vocence_miner_v3")
wavs, sr = m.generate_voice_design(
text="When I got home, the lights were on, the back door was wide open, and somebody had left tea brewing on the kitchen counter.",
instruct="A nervous middle-aged man recounting the moment, slightly hushed, slightly fast.",
language="english",
)
sf.write("out.wav", wavs[0], sr)
```
The example deliberately uses a long, multi-clause sentence — the kind that earlier checkpoints would clip mid-read.
---
## What `instruct` understands
| Axis | Working values |
|------|----------------|
| Gender | male, female |
| Pitch | deep, low, medium, high, thin |
| Pace | slow, halting, moderate, brisk, fast |
| Affect | neutral, happy, sad, angry, fearful, urgent, calm, projected, whispered, sarcastic |
| Persona | bedtime storyteller, news anchor, sports announcer, stern parent, weary narrator |
Lead with gender on emotion-heavy prompts to avoid timbre drift.
---
## Caveats
- English only — other languages were not part of this checkpoint's adaptation set.
- Strongly expressive reads (drawn-out sad reads, projected announcer reads) may run slightly less precise on automatic transcription than the base. The trade-off was made deliberately for delivery character.
- CC BY-NC-SA 4.0 — research and non-commercial use only.
---
## What's in the repo
- `model.safetensors` — merged Talker weights
- `speech_tokenizer/` — Qwen3 12 Hz audio codec
- `tokenizer.json`, `vocab.json`, `merges.txt`, configs — text-side assets
- `miner.py`, `chute_config.yml`, `vocence_config.yaml` — Vocence engine glue (TEE / pro_6000)
- `demo.py` — quick smoke test
The Vocence files make this repo deployable on **Bittensor SN78 (Vocence)** via the canonical Vocence/Chutes wrapper without modification.
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