c708aecb18
Signed-off-by: Zhang Jian <jianmusings@gmail.com> Signed-off-by: Zhang <jianmusings@gmail.com> Signed-off-by: Zhang <zhang.jian@u.nus.edu> Signed-off-by: Zhang Jian <e0322744@u.nus.edu> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: Zhang Jian <e0322744@u.nus.edu> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import os
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from tests.helpers.mark import hardware_test
|
|
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
|
|
from vllm_omni.outputs import OmniRequestOutput
|
|
from vllm_omni.platforms import current_omni_platform
|
|
|
|
# ruff: noqa: E402
|
|
REPO_ROOT = Path(__file__).resolve().parents[2]
|
|
if str(REPO_ROOT) not in sys.path:
|
|
sys.path.insert(0, str(REPO_ROOT))
|
|
|
|
from vllm_omni import Omni
|
|
|
|
# Prefer a tiny/random checkpoint for CI.
|
|
# Override in CI if needed: AUDIOX_TEST_MODEL=<model-or-local-path>
|
|
models = [os.environ.get("AUDIOX_TEST_MODEL", "zhangj1an/audiox_random")]
|
|
|
|
|
|
@pytest.mark.core_model
|
|
@pytest.mark.diffusion
|
|
@hardware_test(res={"cuda": "L4", "xpu": "B60"})
|
|
@pytest.mark.parametrize("model_name", models)
|
|
def test_audiox_model(model_name: str):
|
|
m = Omni(model=model_name, model_class_name="AudioXPipeline")
|
|
|
|
# Keep runtime short for CI.
|
|
seconds_total = 2.0
|
|
# AudioXPipeline always emits 44.1 kHz stereo (advertised via class-level
|
|
# ``audio_sample_rate``); the trimmed output should match this rate.
|
|
sample_rate = 44100
|
|
|
|
outputs = m.generate(
|
|
prompts={"prompt": "A dog barking in a quiet park."},
|
|
sampling_params_list=OmniDiffusionSamplingParams(
|
|
num_inference_steps=4,
|
|
guidance_scale=6.0,
|
|
generator=torch.Generator(current_omni_platform.device_type).manual_seed(42),
|
|
num_outputs_per_prompt=1,
|
|
extra_args={
|
|
"audiox_task": "t2a",
|
|
"seconds_start": 0.0,
|
|
"seconds_total": seconds_total,
|
|
},
|
|
),
|
|
)
|
|
|
|
assert outputs is not None
|
|
first_output = outputs[0]
|
|
assert first_output.final_output_type == "audio"
|
|
assert hasattr(first_output, "request_output") and first_output.request_output
|
|
|
|
req_out = first_output.request_output
|
|
assert isinstance(req_out, OmniRequestOutput)
|
|
assert req_out.final_output_type == "audio"
|
|
assert hasattr(req_out, "multimodal_output") and req_out.multimodal_output
|
|
|
|
audio = req_out.multimodal_output.get("audio")
|
|
assert isinstance(audio, np.ndarray)
|
|
# audio shape: (batch, channels, samples)
|
|
assert audio.ndim == 3
|
|
assert audio.shape[0] == 1
|
|
assert audio.shape[1] == 2
|
|
assert audio.shape[2] > 0
|
|
expected_samples = int(seconds_total * sample_rate)
|
|
assert abs(audio.shape[2] - expected_samples) <= 2 * 1024
|