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vllm-omni/examples/offline_inference/text_to_audio
3158e9d28f [Entrypoint][Refactor] vLLM-Omni Entrypoint Refactoring (#1908)
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Co-authored-by: yinpe <11810305@mail.sustech.edu.cn>
Co-authored-by: Peiqi Yin <yinpeiqi809@gmail.com>
Co-authored-by: wuhang <whlbx@hotmail.com>
Co-authored-by: Peiqi Yin <60515999+yinpeiqi@users.noreply.github.com>
Co-authored-by: linyueqian <linyueqian@outlook.com>
Co-authored-by: princepride <wangzhipeng628@gmail.com>
Co-authored-by: lishunyang <lishunyang12@163.com>
2026-03-18 17:33:53 +08:00
..

Text-To-Audio

The stabilityai/stable-audio-open-1.0 pipeline generates audio from text prompts.

Prerequisites

If you use a gated model (e.g., stabilityai/stable-audio-open-1.0), ensure you have access:

  1. Accept Model License: Visit the model page on Hugging Face (e.g., [stabilityai/stable-audio-open-1.0]) and accept the user agreement.
  2. Authenticate: Log in to Hugging Face locally to access the gated model.
    huggingface-cli login
    

Local CLI Usage

python text_to_audio.py \
  --model stabilityai/stable-audio-open-1.0 \
  --prompt "The sound of a hammer hitting a wooden surface" \
  --negative-prompt "Low quality" \
  --seed 42 \
  --guidance-scale 7.0 \
  --audio-length 10.0 \
  --num-inference-steps 100 \
  --output stable_audio_output.wav

Key arguments:

  • --prompt: text description (string).
  • --negative-prompt: negative prompt for classifier-free guidance.
  • --seed: integer seed for deterministic generation.
  • --guidance-scale: classifier-free guidance scale.
  • --audio-length: audio duration in seconds.
  • --num-inference-steps: diffusion sampling steps.(more steps = higher quality, slower).
  • --output: path to save the generated WAV file.