qwen-tts_skill

This skill enables offline local text-to-speech using Qwen3-TTS-12Hz-1.7B-CustomVoice to generate multi-language audio with emotion and style.
  • Python

2.5k

GitHub Stars

5

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill qwen-tts

  • _meta.json269 B
  • MAC_SERVER.md4.4 KB
  • README.md1.4 KB
  • SKILL.md4.5 KB
  • VOICEDESIGN.md4.3 KB

Overview

This skill provides local text-to-speech using the Qwen3-TTS-12Hz-1.7B-CustomVoice model. It runs entirely offline after the initial model download and supports 10 languages, 9 premium speaker voices, and instruction-based voice control for emotion, tone, and style. Use it as a privacy-friendly alternative to cloud TTS services when you need reliable, on-device audio generation.

How this skill works

The skill installs a small runtime and downloads the 1.7GB TTS model on first use. A command-line script accepts text, language, speaker, and an instruction string to shape emotion and style, then synthesizes WAV audio locally (16 kHz). It auto-detects available hardware, preferring GPU if available and falling back to CPU when memory is limited.

When to use it

  • Generate offline audio files from text for apps or demos
  • Create voice messages, narration, or audio prompts without cloud services
  • Control emotional tone, style, and pacing via instruction strings
  • Produce speech in supported languages including Italian for local projects
  • Integrate with automation pipelines that capture output file paths

Best practices

  • Use a speaker matched to the text language for best quality (native-language speakers recommended)
  • Provide concise instruction strings (emotion, tone, style) to shape delivery
  • Pre-run setup on target machines to avoid model download delays during production
  • Prefer GPU for faster synthesis; expect longer generation times on CPU
  • List speakers with --list-speakers when choosing a voice

Example use cases

  • Generate Italian narration: synthesize onboarding messages or tutorials in Italian using the Italian-capable speakers
  • Create dynamic IVR prompts: produce multiple versions with varying emotion and tone using instruction-based control
  • Localizable game dialogue: synthesize character lines offline with different premium voices
  • Privacy-sensitive voice output: produce customer-facing audio without sending text to cloud services
  • CI/CD audio asset generation: script TTS to produce updated audio files and return path for automation

FAQ

The model is about 1.7GB and the virtual environment plus dependencies is roughly 500MB; the model auto-downloads on first run.

Which languages and voices are supported?

It supports 10 languages (including Italian, English, Chinese, Japanese, Korean) and nine premium speakers. Use each speaker’s native language for optimal quality, though cross-language synthesis is supported.

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