Text-to-Speech
Transformers
Safetensors
Qwen3-TTS
English
text-generation
tts
prompttts
qwen3-tts
voice-design
vocence
british-english
uk-accent
Instructions to use matthewliu0302/grit_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matthewliu0302/grit_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="matthewliu0302/grit_v4")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("matthewliu0302/grit_v4", dtype="auto") - Notebooks
- Google Colab
- Kaggle
vocence_miner_v8
A naturalness-first prompt-driven TTS, built on top of magma90909/vocence_miner_v7. Two things distinguish this checkpoint:
- British English coverage. Phrasings like "A man with a British English accent", "A Scottish woman, conversational", "a Welsh narrator" land on a real distribution rather than slipping back to neutral US English.
- Conversational subtlety. Tuned for everyday delivery โ "speaking warmly", "softly sad", "with a touch of anger, controlled" โ rather than theatrical intensity. The model deliberately steps back when you don't ask for drama.
24 kHz mono WAV output, single forward call, no reference audio, no PEFT runtime. Everything ships in this repo.
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