AliciaandHongsheng Liu
d3ea94309d
Signed-off-by: Alicia <115451386+congw729@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
import sys
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from pathlib import Path
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import numpy as np
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import pytest
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import torch
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from tests.utils import hardware_test
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.outputs import OmniRequestOutput
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# ruff: noqa: E402
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REPO_ROOT = Path(__file__).resolve().parents[2]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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from vllm_omni import Omni
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# Use random weights model for CI testing (small, no authentication required)
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models = ["linyueqian/stable_audio_random"]
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@pytest.mark.core_model
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@pytest.mark.diffusion
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@hardware_test(res={"cuda": "L4"})
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@pytest.mark.parametrize("model_name", models)
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def test_stable_audio_model(model_name: str):
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m = Omni(model=model_name)
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# Use minimal settings for testing
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# Generate a short 2-second audio clip with minimal inference steps
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audio_start_in_s = 0.0
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audio_end_in_s = 2.0 # Short duration for fast testing
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sample_rate = 44100 # Stable Audio uses 44100 Hz
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outputs = m.generate(
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prompts={
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"prompt": "The sound of a dog barking",
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"negative_prompt": "Low quality.",
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},
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sampling_params_list=OmniDiffusionSamplingParams(
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num_inference_steps=4, # Minimal steps for speed
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guidance_scale=7.0,
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generator=torch.Generator("cuda").manual_seed(42),
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num_outputs_per_prompt=1,
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extra_args={
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"audio_start_in_s": audio_start_in_s,
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"audio_end_in_s": audio_end_in_s,
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},
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),
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)
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# Extract audio from OmniRequestOutput
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assert outputs is not None
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first_output = outputs[0]
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assert first_output.final_output_type == "image"
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assert hasattr(first_output, "request_output") and first_output.request_output
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req_out = first_output.request_output[0]
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assert isinstance(req_out, OmniRequestOutput)
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assert req_out.final_output_type == "audio"
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assert hasattr(req_out, "multimodal_output") and req_out.multimodal_output
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audio = req_out.multimodal_output.get("audio")
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assert isinstance(audio, np.ndarray)
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# audio shape: (batch, channels, samples)
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# For stable-audio-open-1.0: sample_rate=44100, so 2 seconds = 88200 samples
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assert audio.ndim == 3
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assert audio.shape[0] == 1 # batch size
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assert audio.shape[1] == 2 # stereo channels
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expected_samples = int((audio_end_in_s - audio_start_in_s) * sample_rate)
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assert audio.shape[2] == expected_samples # 88200 samples for 2 seconds
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