| --- |
| 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. |
| |
