Qwen Audio

High-performance audio library with text-to-speech (TTS) and speech-to-text (STT).

Overview

Qwen-Audio is a high-performance audio processing library optimized. It delivers fast, efficient TTS and STT with support for multiple models, languages, and audio formats.

Prerequisites

• Python 3.10+

Environment checks

Before using any capability, verify that all items in ./references/env-check-list.md are complete.

Capabilities

Voice Management

Voices are stored in the ./voices/ directory at the skill root level. Each voice has its own folder containing:

• ref_audio.wav - Reference audio file
• ref_text.txt - Reference text transcript
• ref_instruct.txt - Voice style description

Create a Voice

Create a reusable voice profile using VoiceDesign model. The --instruct parameter is required to describe the voice style:

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" voice create --text "This is a sample voice reference text." --instruct "A warm, friendly female voice with a professional tone." --id "my-voice-id"

Optional: --id "my-voice-id" to specify a custom voice ID.

Returns (JSON):

{
  "id": "my-voice-id",
  "ref_audio": "/<qwen-audio-skill-path>/voices/my-voice-id/ref_audio.wav",
  "ref_text": "This is a sample voice reference text.",
  "instruct": "A warm, friendly female voice with a professional tone.",
  "duration": 3.456,
  "sample_rate": 24000,
  "success": true
}

List Voices

List all created voice profiles:

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" voice list

Returns (JSON):

[
  {
    "id": "my-voice-id",
    "ref_audio": "/<qwen-audio-skill-path>/voices/my-voice-id/ref_audio.wav",
    "ref_text": "This is a sample voice reference text.",
    "instruct": "A warm, friendly female voice with a professional tone.",
    "duration": 3.456,
    "sample_rate": 24000
  }
]

Text to Speech

TTS Voice Pre-check (Required)

Before any tts generation, always confirm the available voices first:

1. Run voice list to check the current voice profiles.
2. If the returned list is empty, stop and ask the user what kind of voice they want to create first. Offer style choices, for example:
   • Warm and friendly female narrator
   • Deep and steady male broadcast voice
   • Young and energetic neutral voice
   • Calm and professional customer-service voice Then run voice create only after the user confirms a style.
3. If the returned list is not empty, show the available voice id values and ask the user to confirm which one should be used as the --ref_voice reference id for generation.

Only run tts after this confirmation step is complete.

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" tts --text "hello world" --output "/path/to/save.wav"

Returns (JSON):

{
  "audio_path": "/path/to/save.wav",
  "duration": 1.234,
  "sample_rate": 24000,
  "success": true
}

Voice Cloning

Clone any voice using a reference audio sample. Provide the wav file and its transcript:

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" tts --text "hello world" --output "/path/to/save.wav" --ref_audio "sample_audio.wav" --ref_text "This is what my voice sounds like."

ref_audio: reference audio to clone ref_text: transcript of the reference audio

Use a Created Voice

After creating a voice, use it for TTS with the --ref_voice parameter. The instruct will be automatically loaded:

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" tts --text "New text to speak" --output "/path/to/save.wav" --ref_voice "my-voice-id" --instruct "Very happy and excited."

Optional: --instruct to emotion control.

Automatic Speech Recognition (STT)

uv run --project "/<qwen-audio-skill-path>" python "<qwen-audio-skill-path>/scripts/qwen-audio.py" stt --audio "/sample_audio.wav" --output "/path/to/save.txt" --output-format txt

Test audio: https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav output-format: "txt" | "ass" | "srt" | "all"

Returns (JSON):

{
  "text": "transcribed text content",
  "duration": 10.5,
  "sample_rate": 16000,
  "files": ["/path/to/save.txt", "/path/to/save.srt"],
  "success": true
}