Text-To-Audio¶

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/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 \
  --cache-backend tea_cache \
  --output stable_audio_output.wav

To reduce per-GPU memory for multi-GPU inference, launch with HSDP:

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 \
  --use-hsdp \
  --hsdp-shard-size 2 \
  --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).
• --use-hsdp: enable HSDP weight sharding for the Stable Audio DiT.
• --hsdp-shard-size: number of GPUs used for HSDP sharding.
• --hsdp-replicate-size: number of HSDP replica groups.
• --cache-backend: cache acceleration backend. Stable Audio currently supports tea_cache.
• --output: path to save the generated WAV file.

Example materials¶