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>
98 lines
2.9 KiB
Python
98 lines
2.9 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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"""E2E offline inference tests for IndexTTS2 two-stage pipeline.
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Stage 0 (GPT AR) → Stage 1 (S2Mel + BigVGAN). Output is 22050 Hz mono WAV.
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Covers: basic TTS.
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"""
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from __future__ import annotations
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import pytest
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import torch
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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 OmniRunner
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from tests.helpers.stage_config import get_deploy_config_path
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from vllm_omni import Omni
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from vllm_omni.model_executor.models.indextts2.prompt_utils import (
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build_indextts2_prefill_prompt_ids,
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)
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MODEL_NAME = "IndexTeam/IndexTTS-2"
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STAGE_CONFIG = get_deploy_config_path("indextts2.yaml")
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_OMNI_RUNNER_PARAM = (
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MODEL_NAME,
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STAGE_CONFIG,
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)
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REF_AUDIO = str(get_asset_path("indextts2/ref_audio.wav"))
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pytestmark = [
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pytest.mark.slow,
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pytest.mark.tts,
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pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
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]
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SAMPLE_RATE = 22050
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def _audio_from_mm(mm: dict) -> torch.Tensor | None:
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audio = mm.get("audio")
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if audio is None:
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audio = mm.get("model_outputs")
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if isinstance(audio, list):
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chunks = [chunk.reshape(-1) for chunk in audio if isinstance(chunk, torch.Tensor) and chunk.numel() > 0]
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return torch.cat(chunks, dim=0) if chunks else None
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return audio if isinstance(audio, torch.Tensor) else None
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def _sample_rate_from_mm(mm: dict) -> int:
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sr = mm.get("sr")
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if isinstance(sr, list):
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sr = sr[-1] if sr else None
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if sr is None:
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return SAMPLE_RATE
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item = getattr(sr, "item", None)
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if callable(item):
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return int(item())
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return int(sr)
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def _build_request(text: str, ref_audio: str, **extra) -> dict:
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info = {
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"text": [text],
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"voice": [ref_audio],
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}
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info.update(extra)
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return {
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"prompt_token_ids": build_indextts2_prefill_prompt_ids(MODEL_NAME, text),
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"additional_information": info,
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}
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def _collect_audio(omni: Omni, request: dict) -> tuple[torch.Tensor, int]:
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for omni_out in omni.generate(request):
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mm = omni_out.multimodal_output
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assert mm is not None, "Expected multimodal_output"
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audio = _audio_from_mm(mm)
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assert audio is not None, "Expected audio output"
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return audio.cpu(), _sample_rate_from_mm(mm)
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raise AssertionError("No outputs received")
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def _assert_valid_audio(audio: torch.Tensor, sr: int) -> None:
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assert sr == SAMPLE_RATE
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assert audio.numel() > 0
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assert torch.any(audio != 0).item()
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@hardware_test(res={"cuda": "L4"})
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def test_basic_english(omni_runner: OmniRunner) -> None:
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"""English TTS: text + ref_audio, no emotion conditioning."""
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req = _build_request("Hello, this is a voice synthesis test.", REF_AUDIO)
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audio, sr = _collect_audio(omni_runner.omni, req)
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_assert_valid_audio(audio, sr)
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