grit_v1

vocence_miner_v8

A naturalness-first prompt-driven TTS, built on top of magma90909/vocence_miner_v8.

Generate

pip install qwen-tts transformers torch soundfile

from qwen_tts import Qwen3TTSModel
import soundfile as sf

m = Qwen3TTSModel.from_pretrained("magma90909/vocence_miner_v8")

wavs, sr = m.generate_voice_design(
    text="The train to Edinburgh departs from platform four.",
    instruct="A man with a British English accent, calm and natural.",
    language="english",
)
sf.write("out.wav", wavs[0], sr)

demo.py walks through three preset prompts.

How to write instruct

The model responds best to subtle, conversational language — not intensifiers like "intensely sad" or "nearly shouting". Stack these elements freely:

Some example prompts that work well:

A British man speaks calmly and naturally.
A woman with a Scottish accent, in an everyday speaking tone.
A man, softly sad, calm and unhurried.
A British news anchor, professional and neutral, at a brisk steady pace.
A clear, neutral voice reading the sentence.

Best-fit and not-fit

Best at:

• Natural, everyday English — both US and UK
• Bedtime storyteller / news anchor / meditation guide style reads
• Conversational sadness, warmth, mild anger, gentle pleasure

Less suited for:

• Theatrical / caricatured delivery (loud anger, shouted joy, dramatic sadness)
• Extreme intensifier prompts ("nearly shouting", "intensely sad") — the model intentionally tones these down
• Languages other than English

CC BY-NC-SA 4.0 — research and non-commercial use only.

Files

model.safetensors            # merged Talker weights (3.6 GB)
speech_tokenizer/            # Qwen3 12 Hz audio codec (~650 MB)
tokenizer.json + ...         # text tokenizer
config.json + ...            # model configs
miner.py                     # Vocence engine
chute_config.yml             # Chutes build (TEE / pro_6000)
vocence_config.yaml          # runtime knobs
demo.py                      # quick smoke test

The Vocence files make this repo deployable on Bittensor SN78 (Vocence) via the canonical Vocence/Chutes wrapper without modification.