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>
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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E2E expansion tests for Step-Audio2 offline inference (nightly CI).
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Full-model inference with audio input and audio output.
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"""
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pytest
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from tests.helpers.mark import hardware_test
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from tests.helpers.media import generate_synthetic_audio
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from tests.helpers.runtime import OmniRunner
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from vllm_omni.outputs import OmniRequestOutput
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MODEL = "stepfun-ai/Step-Audio-2-mini"
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STAGE_CONFIG = str(Path(__file__).parent / "stage_configs" / "step_audio2_ci.yaml")
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TEST_PARAMS = [(MODEL, STAGE_CONFIG)]
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pytestmark = [pytest.mark.slow, pytest.mark.tts]
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DEFAULT_SYSTEM_PROMPT = "你是一个语音对话助手，能够理解音频输入并生成语音回复。"
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SAMPLE_RATE = 16000
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def _synthetic_audio(duration_sec: int = 2) -> tuple[np.ndarray, int]:
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audio = generate_synthetic_audio(duration_sec, 1, SAMPLE_RATE)["np_array"]
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if len(audio.shape) == 2:
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audio = audio.squeeze()
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return np.asarray(audio, dtype=np.float32), SAMPLE_RATE
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def _build_step_audio2_input(
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user_text: str,
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*,
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system_prompt: str = DEFAULT_SYSTEM_PROMPT,
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audio: tuple[np.ndarray, int] | None = None,
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) -> dict[str, Any]:
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user_content = ""
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multi_modal_data: dict[str, Any] = {}
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if audio is not None:
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user_content += "<audio_patch>"
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multi_modal_data["audio"] = audio
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user_content += user_text
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full_prompt = (
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f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
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f"<|im_start|>user\n{user_content}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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input_dict: dict[str, Any] = {"prompt": full_prompt}
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if multi_modal_data:
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input_dict["multi_modal_data"] = multi_modal_data
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return input_dict
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def _assert_non_empty_text_output(outputs: list[OmniRequestOutput]) -> None:
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assert len(outputs) > 0
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text_output = next((o for o in outputs if o.final_output_type == "text"), None)
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assert text_output is not None
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assert text_output.request_output is not None
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text_content = text_output.request_output.outputs[0].text
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assert text_content is not None
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assert len(text_content.strip()) > 0
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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@pytest.mark.parametrize("omni_runner", TEST_PARAMS, indirect=True)
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def test_audio_to_text_and_audio(omni_runner: OmniRunner) -> None:
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"""Test processing audio input and generating text + audio output."""
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audio = _synthetic_audio(duration_sec=2)
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omni_input = _build_step_audio2_input("请复述这段音频的内容。", audio=audio)
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outputs = omni_runner.generate([omni_input])
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_assert_non_empty_text_output(outputs)
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audio_output = next((o for o in outputs if o.final_output_type == "audio"), None)
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if audio_output is not None:
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assert audio_output.request_output is not None
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audio_tensor = audio_output.request_output.outputs[0].multimodal_output.get("audio")
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if audio_tensor is not None:
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assert audio_tensor.numel() > 0
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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@pytest.mark.parametrize("omni_runner", TEST_PARAMS, indirect=True)
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def test_text_only_input(omni_runner: OmniRunner) -> None:
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"""Test processing text-only input (no audio)."""
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omni_input = _build_step_audio2_input("你好，请用中文回答。")
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outputs = omni_runner.generate([omni_input])
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_assert_non_empty_text_output(outputs)
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