# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """E2E offline inference tests for MOSS-VoiceGenerator (MossTTSDelayModel, 1.7B). Uses the standard omni_runner + pytestmark pattern (one module-scoped engine per file, skill invariant I4). The realtime variant lives in the sibling file test_moss_tts_realtime.py. MOSS-VoiceGenerator synthesizes speech from a text *instruction* that describes the desired voice (e.g. "a warm female voice with an American accent"). It does NOT accept a reference audio clip. The request format requires running AutoProcessor.from_pretrained once per call to encode the (text, instruction) pair into the (prompt_token_ids, codes.ref) grid that the MossTTSDelayModel talker expects — same as examples/offline_inference/text_to_speech/moss_tts/ end2end.py:_build_unified_codes. No determinism test: VoiceGenerator produces variable-length output even with a fixed seed; waveform length reproducibility is not guaranteed. """ from __future__ import annotations import gc import os import pytest import torch from vllm import SamplingParams from tests.helpers.mark import hardware_test from tests.helpers.runtime import OmniRunner from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- MODEL = "OpenMOSS-Team/MOSS-VoiceGenerator" SAMPLE_RATE = 24_000 # Stage 0 = AR talker; max_tokens=512 keeps tests fast (~20 s of audio). # Stage 1 = codec decoder; greedy, large context to hold all codec tokens. _STAGE0_PARAMS = SamplingParams( temperature=1.7, top_p=0.8, top_k=25, max_tokens=512, seed=42, detokenize=False, ) _STAGE1_PARAMS = SamplingParams( temperature=0.0, top_p=1.0, top_k=-1, max_tokens=65536, seed=42, detokenize=False, ) _SAMPLING = [_STAGE0_PARAMS, _STAGE1_PARAMS] # --------------------------------------------------------------------------- # Deploy config # --------------------------------------------------------------------------- def _get_test_config() -> str: """Derive a CI-friendly config from moss_voice_generator.yaml. Reduces Stage 0 gpu_memory_utilization from 0.60 → 0.45 to leave headroom for Stage 1 (0.12) on a shared L4/A10G. max_num_seqs=1 keeps per-test peak memory predictable. """ return modify_stage_config( get_deploy_config_path("moss_voice_generator.yaml"), updates={ "stages": { 0: { "max_num_seqs": 1, "gpu_memory_utilization": 0.45, }, }, }, ) # --------------------------------------------------------------------------- # pytestmark — one engine for the whole module # --------------------------------------------------------------------------- pytestmark = [ pytest.mark.full_model, pytest.mark.tts, pytest.mark.parametrize("omni_runner", [(MODEL, _get_test_config())], indirect=True), ] # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- def _build_request(text: str, instruction: str) -> dict: """Build a VoiceGenerator request using AutoProcessor. Calls proc.build_user_message + proc() to produce the unified-codes grid (L, 1+n_vq) and splits it into prompt_token_ids + codes.ref, exactly as end2end.py:_build_unified_codes does for the voice_generator variant. The processor (including its CPU-resident audio_tokenizer) is freed before returning so it does not compete with the running engine. Set MOSS_TTS_SKIP_ON_NET_FAIL=1 to skip in air-gapped environments where the HF snapshot is not cached. """ from transformers import AutoProcessor try: proc = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True) except Exception as exc: msg = f"Cannot load AutoProcessor for {MODEL}: {exc}" if os.environ.get("MOSS_TTS_SKIP_ON_NET_FAIL"): pytest.skip(msg) pytest.fail(msg) user_msg = proc.build_user_message(text=text, instruction=instruction) batch = proc(conversations=[[user_msg]], mode="generation") unified = batch["input_ids"][0] # (L, 1+n_vq) text_ids = unified[:, 0].tolist() audio_codes = unified[:, 1:].contiguous().to(torch.int64) # (L, n_vq) del proc gc.collect() return { "prompt_token_ids": text_ids, "additional_information": {"codes": {"ref": audio_codes}}, } def _collect_audio(omni_runner: OmniRunner, request: dict) -> tuple[torch.Tensor, int]: """Run one request and return (waveform_cpu, sample_rate). Iterates all OmniRequestOutputs from generate() via the top-level multimodal_output property (which aggregates from completion outputs). AR-stage outputs have empty multimodal_output and are silently skipped. With async_chunk=True and FINAL_ONLY, the codec stage consolidates its chunks into a single tensor before yielding — but we handle list fallback for robustness. """ chunks: list[torch.Tensor] = [] sr_final = SAMPLE_RATE for out in omni_runner.omni.generate(request, _SAMPLING): mm = out.multimodal_output if not mm: continue audio = mm.get("audio") if audio is None: audio = mm.get("model_outputs") if audio is None: continue sr = mm.get("sr") if sr is not None: sr_final = int(sr.item() if hasattr(sr, "item") else sr) 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, ) if isinstance(audio, torch.Tensor) and audio.numel() > 0: chunks.append(audio.reshape(-1).cpu()) if not chunks: raise AssertionError("No audio output received from generate()") return torch.cat(chunks, dim=0), sr_final # --------------------------------------------------------------------------- # Tests # --------------------------------------------------------------------------- @pytest.mark.advanced_model @hardware_test(res={"cuda": "L4"}, num_cards=1) def test_moss_tts_english(omni_runner: OmniRunner) -> None: """VoiceGenerator: English instruction produces non-empty 24 kHz audio.""" req = _build_request( "Hello, this is a MOSS voice design test.", "a warm female voice with an American accent", ) audio, sr = _collect_audio(omni_runner, 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.advanced_model @hardware_test(res={"cuda": "L4"}, num_cards=1) def test_moss_tts_chinese(omni_runner: OmniRunner) -> None: """VoiceGenerator: Chinese input produces non-empty audio.""" req = _build_request( "你好，这是语音合成测试。", "一个清晰温暖的女声", ) audio, sr = _collect_audio(omni_runner, req) assert sr == SAMPLE_RATE assert audio.numel() > 0 assert not torch.all(audio == 0) @pytest.mark.advanced_model @hardware_test(res={"cuda": "L4"}, num_cards=1) def test_moss_tts_batch(omni_runner: OmniRunner) -> None: """VoiceGenerator: batch of two requests each returns non-empty audio.""" requests = [ _build_request("First sentence.", "a warm female voice"), _build_request("Second sentence.", "a young male voice"), ] results: list[torch.Tensor] = [] for out in omni_runner.omni.generate(requests, _SAMPLING): mm = out.multimodal_output if not mm: continue audio = mm.get("audio") if audio is None: audio = mm.get("model_outputs") if audio is None: continue 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, ) if isinstance(audio, torch.Tensor) and audio.numel() > 0: results.append(audio.reshape(-1).cpu()) # With async_chunk streaming + FINAL_ONLY, each request produces one # consolidated output. The >= 2 guard handles backward-compatibility if # consolidation behavior changes. assert len(results) >= 2, f"Expected at least 2 audio outputs, got {len(results)}" for i, audio in enumerate(results): assert audio.numel() > 0, f"Audio chunk[{i}] is empty"