# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """E2E offline inference tests for MOSS-TTS two-stage pipeline. Tests the MossTTSDelayModel path using MOSS-VoiceGenerator (1.7B) — the smallest MossTTSDelayModel variant — so the test runs on a single A10G or L4 without requiring an 80 GB GPU. A second test class covers MOSS-TTS-Realtime (1.7B, MossTTSRealtime) to exercise the local-transformer generation path. Both classes use a module-scoped engine so the model is loaded once per test module execution. Do NOT add a second engine fixture in this file — that triggers mid-module teardown races (see skill invariant I4). """ from __future__ import annotations import os import urllib.request import pytest import torch from vllm import SamplingParams from tests.helpers.mark import hardware_test from vllm_omni import Omni # --------------------------------------------------------------------------- # Shared constants # --------------------------------------------------------------------------- # Selected by the nightly "TTS · Function Test" (-m "full_model and L4 and tts"). # Without the tts + run-level marks these tests are collected but never run. pytestmark = [pytest.mark.full_model, pytest.mark.tts] SAMPLE_RATE = 24_000 # All MOSS-TTS full variants output 24 kHz REF_AUDIO_URL = "https://raw.githubusercontent.com/OpenMOSS/MOSS-TTS/main/assets/audio/reference_zh_1.wav" _DEFAULT_SAMPLING = SamplingParams( temperature=1.7, top_p=0.8, top_k=25, max_tokens=512, # short for tests (~20 s at 24 kHz, ~12.5 tokens/s) seed=42, detokenize=False, ) # --------------------------------------------------------------------------- # Session fixtures # --------------------------------------------------------------------------- @pytest.fixture(scope="session") def ref_audio_path(tmp_path_factory) -> str: """Download the upstream reference clip once per session. Set ``MOSS_TTS_SKIP_ON_NET_FAIL=1`` to skip in air-gapped environments. """ cache_dir = tmp_path_factory.mktemp("moss_tts_ref") target = cache_dir / "zh_1.wav" try: with urllib.request.urlopen(REF_AUDIO_URL, timeout=30) as resp: target.write_bytes(resp.read()) except Exception as exc: msg = f"Cannot fetch reference clip {REF_AUDIO_URL}: {exc}" if os.environ.get("MOSS_TTS_SKIP_ON_NET_FAIL"): pytest.skip(msg) pytest.fail(msg) if not target.exists() or target.stat().st_size == 0: pytest.fail(f"Reference clip empty after download: {target}") return str(target) # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- def _build_request(text: str, ref_audio_path: str, seed: int = 42) -> dict: return { "prompt": "<|im_start|>assistant\n", "additional_information": { "task_type": ["voice_clone"], "text": [text], "mode": ["voice_clone"], "prompt_audio_path": [ref_audio_path], "seed": [seed], }, } def _collect_audio(omni: Omni, request: dict) -> tuple[torch.Tensor, int]: """Run one request; return (waveform_cpu, sample_rate).""" for stage_outputs in omni.generate(request, _DEFAULT_SAMPLING): for req_output in stage_outputs.request_output: mm = req_output.outputs[0].multimodal_output assert mm is not None, "Expected non-None multimodal_output" # Never use ``a or b`` on tensor values — a multi-element tensor # raises on truthiness. Check ``is None`` explicitly and handle the # pre-consolidation list form. audio = mm.get("audio") if audio is None: audio = mm.get("model_outputs") sr = mm.get("sr") assert audio is not None, "Expected 'audio' or 'model_outputs' in multimodal_output" if isinstance(audio, list): audio = torch.cat( [t.reshape(-1) for t in audio if isinstance(t, torch.Tensor) and t.numel() > 0], dim=0, ) assert isinstance(audio, torch.Tensor), f"Expected Tensor, got {type(audio)}" return audio.reshape(-1).cpu(), int(sr.item()) if sr is not None else SAMPLE_RATE raise AssertionError("No stage outputs received") # --------------------------------------------------------------------------- # MossTTSDelayModel — MOSS-VoiceGenerator 1.7B # (tests the delay-pattern generation path on a small GPU) # --------------------------------------------------------------------------- _DELAY_MODEL = "OpenMOSS-Team/MOSS-VoiceGenerator" @pytest.fixture(scope="module") def delay_engine(): return Omni(model=_DELAY_MODEL, stage_init_timeout=300) @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_delay_english(delay_engine, ref_audio_path): """MossTTSDelayModel: English voice_clone produces non-empty 24 kHz audio.""" req = _build_request("Hello, this is a MOSS-TTS voice cloning test.", ref_audio_path) audio, sr = _collect_audio(delay_engine, req) assert sr == SAMPLE_RATE, f"Expected {SAMPLE_RATE} Hz, got {sr}" assert audio.numel() > 0, "Audio tensor is empty" assert not torch.all(audio == 0), "Audio is silence" @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_delay_chinese(delay_engine, ref_audio_path): """MossTTSDelayModel: Chinese input produces non-empty audio.""" req = _build_request("你好，这是语音合成测试。", ref_audio_path) audio, sr = _collect_audio(delay_engine, req) assert sr == SAMPLE_RATE assert audio.numel() > 0 assert not torch.all(audio == 0) @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_delay_deterministic(delay_engine, ref_audio_path): """MossTTSDelayModel: same seed yields identical waveforms.""" req = _build_request("Reproducibility check.", ref_audio_path, seed=99) audio1, _ = _collect_audio(delay_engine, req) audio2, _ = _collect_audio(delay_engine, req) assert audio1.shape == audio2.shape, "Shapes differ across identical seeds" assert torch.allclose(audio1, audio2, atol=1e-4), "Waveforms differ across identical seeds" @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_delay_batch(delay_engine, ref_audio_path): """MossTTSDelayModel: batch of two requests each returns non-empty audio.""" requests = [ _build_request("First sentence.", ref_audio_path), _build_request("Second sentence.", ref_audio_path), ] results: list[torch.Tensor] = [] for stage_outputs in delay_engine.generate(requests, [_DEFAULT_SAMPLING] * 2): for req_output in stage_outputs.request_output: mm = req_output.outputs[0].multimodal_output assert mm is not None audio = mm.get("audio") if audio is None: audio = mm.get("model_outputs") assert audio is not None if isinstance(audio, list): audio = torch.cat( [t.reshape(-1) for t in audio if isinstance(t, torch.Tensor) and t.numel() > 0], dim=0, ) results.append(audio.reshape(-1).cpu()) assert len(results) == 2, f"Expected 2 outputs, got {len(results)}" for i, audio in enumerate(results): assert audio.numel() > 0, f"Audio[{i}] is empty" # --------------------------------------------------------------------------- # MossTTSRealtime — MOSS-TTS-Realtime 1.7B # (tests the local-transformer generation path) # --------------------------------------------------------------------------- _REALTIME_MODEL = "OpenMOSS-Team/MOSS-TTS-Realtime" @pytest.fixture(scope="module") def realtime_engine(): return Omni(model=_REALTIME_MODEL, stage_init_timeout=300) @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_realtime_english(realtime_engine, ref_audio_path): """MossTTSRealtime: English voice_clone produces non-empty 24 kHz audio.""" req = _build_request("This is a real-time TTS streaming test.", ref_audio_path) audio, sr = _collect_audio(realtime_engine, req) assert sr == SAMPLE_RATE, f"Expected {SAMPLE_RATE} Hz, got {sr}" assert audio.numel() > 0, "Audio tensor is empty" assert not torch.all(audio == 0), "Audio is silence" @pytest.mark.omni @hardware_test(res={"cuda": "L4"}) def test_moss_tts_realtime_deterministic(realtime_engine, ref_audio_path): """MossTTSRealtime: same seed yields identical waveforms.""" req = _build_request("Determinism check for realtime model.", ref_audio_path, seed=7) audio1, _ = _collect_audio(realtime_engine, req) audio2, _ = _collect_audio(realtime_engine, req) assert audio1.shape == audio2.shape assert torch.allclose(audio1, audio2, atol=1e-4)