alicloud-ai-audio-tts-voice-design（阿里云 ai 音频 tts 语音设计）

Category: provider

Model Studio Qwen TTS Voice Design

Use voice design models to create controllable synthetic voices from natural language descriptions.

Critical model names

Use one of these exact model strings:

• qwen3-tts-vd-2026-01-26
• qwen3-tts-vd-realtime-2026-01-15

Prerequisites

• Install SDK in a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope

• Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Normalized interface (tts.voice_design)

Request

• voice_prompt (string, required) target voice description
• text (string, required)
• stream (bool, optional)

Response

• audio_url (string) or streaming PCM chunks
• voice_id (string)
• request_id (string)

Operational guidance

• Write voice prompts with tone, pace, emotion, and timbre constraints.
• Build a reusable voice prompt library for product consistency.
• Validate generated voice in short utterances before long scripts.

Local helper script

Prepare a normalized request JSON and validate response schema:

.venv/bin/python skills/ai/audio/alicloud-ai-audio-tts-voice-design/scripts/prepare_voice_design_request.py \
  --voice-prompt "A warm female host voice, clear articulation, medium pace" \
  --text "This is a voice-design demo"

Output location

• Default output: output/ai-audio-tts-voice-design/audio/
• Override base dir with OUTPUT_DIR.

Validation

mkdir -p output/alicloud-ai-audio-tts-voice-design
for f in skills/ai/audio/alicloud-ai-audio-tts-voice-design/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-ai-audio-tts-voice-design/validate.txt

Pass criteria: command exits 0 and output/alicloud-ai-audio-tts-voice-design/validate.txt is generated.

Output And Evidence

• Save artifacts, command outputs, and API response summaries under output/alicloud-ai-audio-tts-voice-design/.
• Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Workflow

1. Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.
2. Run one minimal read-only query first to verify connectivity and permissions.
3. Execute the target operation with explicit parameters and bounded scope.
4. Verify results and save output/evidence files.

References

• references/sources.md