Alicloud Ai Audio Tts Voice Design

Voice design workflows with Alibaba Cloud Model Studio Qwen TTS VD models. Use when creating custom synthetic voices from text descriptions and using them fo...

Security Scan

The skill's behavior generally matches a voice-design integration for Alibaba Cloud, but its metadata omits a required API credential and it instructs installing a third-party Python package (dashscope) without provenance — these inconsistencies warrant caution.

Purpose & Capability

The name and description match the instructions and helper script: preparing requests for Alibaba Cloud Qwen TTS voice-design models. However, the package metadata declares no required environment variables or credentials while SKILL.md explicitly asks for DASHSCOPE_API_KEY or a dashscope_api_key in ~/.alibabacloud/credentials — a mismatch between declared requirements and operational instructions.

Instruction Scope

SKILL.md stays within voice-design scope (prepare request JSON, validate response, run a minimal read-only check, save outputs). But it explicitly requires an API key (DASHSCOPE_API_KEY) and instructs adding credentials to a user home file; those credentials are not declared in the skill metadata. Instructions also require installing and invoking a pip package (dashscope) which will be executed on the host — this expands the runtime surface beyond what the metadata states.

Install Mechanism

There is no formal install spec (instruction-only), which is low-risk in principle. But SKILL.md instructs creating a venv and running `pip install dashscope`. Installing an externally published Python package at runtime is a moderate risk unless the package provenance is confirmed. The included helper script is small and benign-looking.

Credentials

The credential requested in the instructions (DASHSCOPE_API_KEY or dashscope_api_key in ~/.alibabacloud/credentials) is reasonable for calling Alibaba Cloud services, but the skill metadata does not declare any required env vars or primary credential — this omission reduces transparency and is concerning. No other unrelated secrets are requested.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. It does not modify other skills or system-wide settings in the provided files. Runtime behavior is limited to using a venv and running a small helper script.

What to consider before installing

This skill appears to implement Alibaba Cloud Qwen TTS voice-design flows, but before installing or using it: (1) confirm the maintainers and provenance of the 'dashscope' package — prefer an official Alibaba SDK or an audited package; (2) treat DASHSCOPE_API_KEY as sensitive and only set it in a controlled environment (use a disposable test key if possible); (3) run the helper script in an isolated virtualenv or container and inspect the package contents you pip-install; (4) update or ask the author to correct the skill metadata to declare required env vars and credentials so you can review them up front; (5) avoid saving sensitive production credentials or broad resource identifiers in output evidence files unless necessary. These steps will reduce risk and make the skill's requirements transparent.

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