wangyuandHongsheng Liu
7619ed6b82
Signed-off-by: [Your Name] <your.email@example.com> Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Signed-off-by: [Your Name] <[Your Email]> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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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.helpers.mark import hardware_test
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from tests.helpers.runtime import OmniRunner
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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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from vllm_omni.platforms import current_omni_platform
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# Prefer a tiny/random checkpoint for CI.
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# Override in CI if needed: AUDIOX_TEST_MODEL=<model-or-local-path>
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models = [os.environ.get("AUDIOX_TEST_MODEL", "zhangj1an/audiox_random")]
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# (model, stage_configs_path, extra_omni_kwargs) for ``omni_runner`` indirect parametrize
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_OMNI_RUNNER_PARAMS = [(m, None, {"model_class_name": "AudioXPipeline"}) for m in models]
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pytestmark = [
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pytest.mark.slow,
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pytest.mark.diffusion,
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pytest.mark.parametrize("omni_runner", _OMNI_RUNNER_PARAMS, indirect=True, ids=models),
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]
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@hardware_test(res={"cuda": "L4", "xpu": "B60"})
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def test_audiox_model(omni_runner: OmniRunner) -> None:
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# Keep runtime short for CI.
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seconds_total = 2.0
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# AudioXPipeline always emits 44.1 kHz stereo (advertised via class-level
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# ``audio_sample_rate``); the trimmed output should match this rate.
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sample_rate = 44100
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outputs = omni_runner.omni.generate(
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prompts={"prompt": "A dog barking in a quiet park."},
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sampling_params_list=OmniDiffusionSamplingParams(
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num_inference_steps=4,
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guidance_scale=6.0,
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generator=torch.Generator(current_omni_platform.device_type).manual_seed(42),
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num_outputs_per_prompt=1,
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extra_args={
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"audiox_task": "t2a",
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"seconds_start": 0.0,
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"seconds_total": seconds_total,
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},
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),
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)
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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 == "audio"
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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
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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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assert audio.ndim == 3
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assert audio.shape[0] == 1
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assert audio.shape[1] == 2
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assert audio.shape[2] > 0
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expected_samples = int(seconds_total * sample_rate)
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assert abs(audio.shape[2] - expected_samples) <= 2 * 1024
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