[Model] Add IndexTTS2 text-to-speech support (#3838)

Signed-off-by: BeatSeat <wendavid552@gmail.com>
Signed-off-by: David <david@podcast.ai>
Signed-off-by: wendavid552 <wendavid552@gmail.com>
Signed-off-by: WenDavid <davidzhangcf@foxmail.com>

tests/e2e/offline_inference/test_indextts2.py

@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""E2E offline inference tests for IndexTTS2 two-stage pipeline.
+
+Stage 0 (GPT AR) → Stage 1 (S2Mel + BigVGAN). Output is 22050 Hz mono WAV.
+Covers: basic TTS.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunner
+from tests.helpers.stage_config import get_deploy_config_path
+from vllm_omni import Omni
+from vllm_omni.model_executor.models.indextts2.prompt_utils import (
+    build_indextts2_prefill_prompt_ids,
+)
+
+MODEL_NAME = "IndexTeam/IndexTTS-2"
+STAGE_CONFIG = get_deploy_config_path("indextts2.yaml")
+
+_OMNI_RUNNER_PARAM = (
+    MODEL_NAME,
+    STAGE_CONFIG,
+)
+
+ASSETS_DIR = Path(__file__).resolve().parents[2] / "assets" / "indextts2"
+REF_AUDIO = str(ASSETS_DIR / "ref_audio.wav")
+
+pytestmark = [
+    pytest.mark.advanced_model,
+    pytest.mark.tts,
+    pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
+]
+
+SAMPLE_RATE = 22050
+
+
+def _audio_from_mm(mm: dict) -> torch.Tensor | None:
+    audio = mm.get("audio")
+    if audio is None:
+        audio = mm.get("model_outputs")
+    if isinstance(audio, list):
+        chunks = [chunk.reshape(-1) for chunk in audio if isinstance(chunk, torch.Tensor) and chunk.numel() > 0]
+        return torch.cat(chunks, dim=0) if chunks else None
+    return audio if isinstance(audio, torch.Tensor) else None
+
+
+def _sample_rate_from_mm(mm: dict) -> int:
+    sr = mm.get("sr")
+    if isinstance(sr, list):
+        sr = sr[-1] if sr else None
+    if hasattr(sr, "item"):
+        return int(sr.item())
+    return int(sr) if sr is not None else SAMPLE_RATE
+
+
+def _build_request(text: str, ref_audio: str, **extra) -> dict:
+    info = {
+        "text": [text],
+        "voice": [ref_audio],
+    }
+    info.update(extra)
+    return {
+        "prompt_token_ids": build_indextts2_prefill_prompt_ids(MODEL_NAME, text),
+        "additional_information": info,
+    }
+
+
+def _collect_audio(omni: Omni, request: dict) -> tuple[torch.Tensor, int]:
+    for omni_out in omni.generate(request):
+        mm = omni_out.multimodal_output
+        assert mm is not None, "Expected multimodal_output"
+        audio = _audio_from_mm(mm)
+        assert audio is not None, "Expected audio output"
+        return audio.cpu(), _sample_rate_from_mm(mm)
+    raise AssertionError("No outputs received")
+
+
+def _assert_valid_audio(audio: torch.Tensor, sr: int) -> None:
+    assert sr == SAMPLE_RATE
+    assert audio.numel() > 0
+    assert torch.any(audio != 0).item()
+
+
+@hardware_test(res={"cuda": "L4"})
+def test_basic_english(omni_runner: OmniRunner) -> None:
+    """English TTS: text + ref_audio, no emotion conditioning."""
+    req = _build_request("Hello, this is a voice synthesis test.", REF_AUDIO)
+    audio, sr = _collect_audio(omni_runner.omni, req)
+    _assert_valid_audio(audio, sr)