115 lines
3.7 KiB
Python
115 lines
3.7 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 Online tests for Qwen3-TTS model with text input and audio output.
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These tests verify the /v1/audio/speech endpoint works correctly with
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actual model inference, not mocks.
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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 load_test_audio_data_url
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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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pytestmark = [pytest.mark.full_model, pytest.mark.omni]
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
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MODEL = "Qwen/Qwen3-TTS-12Hz-0.6B-Base"
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# See tests/e2e/online_serving/test_qwen3_tts_base.py for the vendored-asset rationale.
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REF_AUDIO_URL = load_test_audio_data_url("qwen3_tts/clone_2.wav")
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REF_TEXT = "Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you."
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def get_prompt(prompt_type="text"):
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"""Text prompt for text-to-audio tests (same as test_qwen3_omni - beijing test case)."""
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prompts = {
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"text": "The weather is nice today, perfect for a walk in the park.",
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}
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return prompts.get(prompt_type, prompts["text"])
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def get_max_batch_size(size_type="few"):
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"""Batch size for concurrent requests (same as test_qwen3_omni)."""
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batch_sizes = {"few": 5, "medium": 100, "large": 256}
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return batch_sizes.get(size_type, 5)
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tts_server_params = [
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pytest.param(
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OmniServerParams(
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model=MODEL,
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stage_config_path=get_deploy_config_path("qwen3_tts.yaml"),
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server_args=["--trust-remote-code", "--disable-log-stats"],
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),
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id="async_chunk",
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),
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# Synchronous (no async-chunk) variant — ``--no-async-chunk`` alone
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# flips the deploy yaml's bool and the pipeline dispatches to the
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# end-to-end codec processor. No variant yaml / pipeline needed.
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pytest.param(
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OmniServerParams(
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model=MODEL,
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stage_config_path=get_deploy_config_path("qwen3_tts.yaml"),
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server_args=["--trust-remote-code", "--disable-log-stats", "--no-async-chunk"],
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),
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id="no_async_chunk",
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),
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]
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
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def test_voice_clone_streaming_001(omni_server, openai_client) -> None:
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"""
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Test text input processing and audio output via OpenAI API.
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Deploy Setting: default yaml
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Input Modal: text
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Output Modal: audio
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Input Setting: stream=True
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Datasets: few requests
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"""
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request_config = {
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"model": omni_server.model,
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"input": get_prompt(),
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"stream": True,
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"response_format": "wav",
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"task_type": "Base",
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"voice": "clone",
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"ref_audio": REF_AUDIO_URL,
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"ref_text": REF_TEXT,
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}
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openai_client.send_audio_speech_request(request_config, request_num=get_max_batch_size("few"))
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
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def test_response_format_001(omni_server, openai_client) -> None:
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"""
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Test text input processing and audio output via OpenAI API.
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Deploy Setting: default yaml
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Input Modal: text
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Output Modal: audio
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Input Setting: non-stream PCM
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Datasets: few requests
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"""
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request_config = {
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"model": omni_server.model,
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"input": get_prompt(),
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"response_format": "pcm",
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"task_type": "Base",
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"voice": "clone",
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"ref_audio": REF_AUDIO_URL,
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"ref_text": REF_TEXT,
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}
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openai_client.send_audio_speech_request(request_config)
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