vocence-miner04

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.

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Use it

pip install qwen-tts transformers torch soundfile

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.

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What instruct understands

Lead with gender on emotion-heavy prompts to avoid timbre drift.

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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.

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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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Local A/B evaluator (subnet-like)

Use eval_ab.py to compare two checkpoints with Vocence-like weighting and pass threshold:

pip install qwen-tts transformers torch soundfile audiojudge
python eval_ab.py \
  --a-source . \
  --b-source <other_model_or_path> \
  --dataset ./eval_prompts.jsonl \
  --openai-key $OPENAI_API_KEY \
  --device cuda:0

Dataset format (.jsonl, one object per line):

{"text":"hello world","instruction":"gender: male | pitch: mid | speed: normal | age_group: adult | emotion: neutral | tone: casual | accent: us","source_audio":"/abs/path/source.wav"}

source_audio is optional, but recommended to include naturalness scoring like subnet validation.