wangyuandamy-why-3459
4af0ddc1c2
Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""
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E2E expansion tests for Step-Audio2 online serving (weekly CI).
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Tests audio-to-text generation via /v1/chat/completions.
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Request shape follows examples/online_serving/step_audio2/openai_chat_completion_client.py.
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"""
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import os
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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 OmniServerParams
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from tests.helpers.stage_config import get_deploy_config_path
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os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
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MODEL = "stepfun-ai/Step-Audio-2-mini"
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DEPLOY_CONFIG = get_deploy_config_path("step_audio2_ci.yaml")
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SAMPLE_RATE = 16000
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SEED = 42
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SYNTH_PHRASE = "how are you"
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TEST_PARAMS = [
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pytest.param(
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OmniServerParams(
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model=MODEL,
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server_args=["--deploy-config", DEPLOY_CONFIG, "--trust-remote-code"],
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env_dict={"VLLM_IMAGE_FETCH_TIMEOUT": "60"},
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),
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id="step_audio2",
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)
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]
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pytestmark = [
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pytest.mark.parametrize("omni_server", TEST_PARAMS, indirect=True),
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pytest.mark.slow,
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pytest.mark.tts,
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]
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def _synthetic_audio_base64(duration_sec: int = 2) -> str:
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return generate_synthetic_audio(
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duration_sec,
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1,
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SAMPLE_RATE,
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phrase_text=SYNTH_PHRASE,
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)["base64"]
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def _default_sampling_params_list() -> list[dict]:
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return [
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{
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": -1,
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"max_tokens": 256,
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"seed": SEED,
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"detokenize": True,
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"repetition_penalty": 1.05,
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},
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]
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def _build_audio_to_text_request_config(omni_server, audio_base64: str) -> dict:
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"""Build an audio-to-text chat completion request config."""
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audio_url = f"data:audio/wav;base64,{audio_base64}"
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return {
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"model": omni_server.model,
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"messages": [
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{
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": "You are a speech recognition assistant. Transcribe the audio accurately.",
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}
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],
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},
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{
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"role": "user",
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"content": [
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{"type": "audio_url", "audio_url": {"url": audio_url}},
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{"type": "text", "text": "Please transcribe the audio content."},
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],
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},
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],
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"modalities": ["text"],
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"sampling_params_list": _default_sampling_params_list(),
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}
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@hardware_test(res={"cuda": "H100"}, num_cards=1)
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def test_single_audio_to_text_request(omni_server, online_client) -> None:
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"""Test a single audio-to-text request via the chat completions API."""
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request_config = _build_audio_to_text_request_config(
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omni_server,
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_synthetic_audio_base64(duration_sec=2),
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)
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online_client.send_omni_request(request_config)
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@hardware_test(res={"cuda": "H100"}, num_cards=1)
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@pytest.mark.parametrize("request_num", [2, 4])
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def test_concurrent_audio_to_text_requests(omni_server, online_client, request_num: int) -> None:
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"""Test concurrent audio-to-text requests."""
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request_config = _build_audio_to_text_request_config(
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omni_server,
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_synthetic_audio_base64(duration_sec=2),
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)
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responses = online_client.send_omni_request(request_config, request_num=request_num)
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assert len(responses) == request_num
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