109 lines
4.3 KiB
Python
109 lines
4.3 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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"""Breeze design, reference conditioning and CFG through the shared speech client."""
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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 get_asset_path
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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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MODEL = "BreezeBlue/Breeze-TTS-2"
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pytestmark = [
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pytest.mark.core_model,
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pytest.mark.advanced_model,
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pytest.mark.tts,
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pytest.mark.parametrize(
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"omni_server",
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[OmniServerParams(model=MODEL, stage_config_path=get_deploy_config_path("breeze_tts_2.yaml"))],
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indirect=True,
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),
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]
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_chinese_voice_design(omni_server, online_client, run_level) -> None:
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online_client.send_audio_speech_request(
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{
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"model": omni_server.model,
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"input": "你好，欢迎使用语音合成系统。今天的天气很好。",
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"instructions": "年轻女性，使用标准普通话，语速适中。",
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"extra_params": {"guidance_scale": 4.0},
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"transcript_language": "zh",
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# Whisper-small transcribes "合成" as "和程" on this fixed clip.
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# Use the stronger primary ASR without changing the expected text
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# or similarity threshold, or retrying after a failed assertion.
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"transcript_model": "large-v3",
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"response_format": "wav",
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"sample_rate": 24000,
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"min_audio_bytes": 24000,
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"seed": 42,
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"max_new_tokens": 8 if run_level == "core_model" else 250,
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}
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)
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_english_streaming(omni_server, online_client, run_level) -> None:
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online_client.send_audio_speech_request(
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{
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"model": omni_server.model,
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"input": "Hello, this is a demonstration of natural speech synthesis.",
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"instructions": "A warm, clear female voice speaking English at a moderate pace.",
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"transcript_language": "en",
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"response_format": "pcm",
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"sample_rate": 24000,
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"min_audio_bytes": 24000,
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"stream": True,
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"stream_format": "audio",
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# The reference eager runtime scores 0.56 dB on this same prompt
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# (seed 42, CFG=1); the shared 1 dB floor rejects it as well.
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"min_hnr_db": 0.0,
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"seed": 42,
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"max_new_tokens": 8 if run_level == "core_model" else 250,
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}
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)
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_two_concurrent_requests(omni_server, online_client, run_level) -> None:
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online_client.send_audio_speech_request(
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{
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"model": omni_server.model,
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"input": "Each request keeps its own audio generation state.",
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"instructions": "A clear English voice.",
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"extra_params": {"guidance_scale": 4.0},
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"transcript_language": "en",
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"response_format": "wav",
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"sample_rate": 24000,
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"min_audio_bytes": 24000,
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"seed": 123,
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"max_new_tokens": 8 if run_level == "core_model" else 250,
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},
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request_num=2,
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)
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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@pytest.mark.parametrize("guidance_scale", [1.0, 4.0])
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def test_reference_cloning_and_direction(omni_server, online_client, run_level, guidance_scale) -> None:
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online_client.send_audio_speech_request(
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{
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"model": omni_server.model,
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"input": "Please read this sentence clearly and naturally.",
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"instructions": "Keep the reference voice. Speak gently and calmly.",
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"ref_audio": get_asset_path("qwen3_tts/clone_2.wav", as_data_url=True),
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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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"task_type": "Base",
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"extra_params": {"guidance_scale": guidance_scale},
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"transcript_language": "en",
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"response_format": "wav",
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"sample_rate": 24000,
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"min_audio_bytes": 24000,
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"seed": 42,
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"max_new_tokens": 8 if run_level == "core_model" else 250,
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}
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)
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