114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""E2E offline inference test for MOSS-TTS-v1.5 (MossTTSDelay-8B).
|
|
|
|
MOSS-TTS-v1.5 is a continued-training upgrade of MOSS-TTS 1.0 with the SAME
|
|
``MossTTSDelay`` architecture and API, so it reuses the existing talker/codec
|
|
path unchanged — this test just pins that the 8B v1.5 checkpoint loads and
|
|
produces audio.
|
|
|
|
The model is 8B, so this lives in its own file (one module-scoped engine per
|
|
file, per skill invariant I4) and is gated behind an 80 GB GPU; the small-GPU
|
|
MossTTSDelay coverage stays in ``test_moss_tts.py`` (1.7B VoiceGenerator).
|
|
"""
|
|
|
|
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
|
|
|
|
# H100-gated (8B). Picked up by the nightly "TTS · Function Test with H100"
|
|
# job (-m "full_model and H100 and tts"); the L4 MOSS-TTS tests land in the
|
|
# sibling L4 tts lane instead.
|
|
# TODO: Fix this test
|
|
# pytestmark = [pytest.mark.full_model, pytest.mark.tts]
|
|
|
|
SAMPLE_RATE = 24_000
|
|
REF_AUDIO_URL = "https://raw.githubusercontent.com/OpenMOSS/MOSS-TTS/main/assets/audio/reference_zh_1.wav"
|
|
_MODEL = "OpenMOSS-Team/MOSS-TTS-v1.5"
|
|
|
|
_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,
|
|
)
|
|
|
|
|
|
@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.
|
|
"""
|
|
target = tmp_path_factory.mktemp("moss_tts_v15_ref") / "zh_1.wav"
|
|
try:
|
|
with urllib.request.urlopen(REF_AUDIO_URL, timeout=30) as resp:
|
|
target.write_bytes(resp.read())
|
|
except Exception as exc: # noqa: BLE001
|
|
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)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def v15_engine():
|
|
return Omni(model=_MODEL, stage_init_timeout=600)
|
|
|
|
|
|
def _build_request(text: str, ref_audio_path: str) -> 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": [42],
|
|
},
|
|
}
|
|
|
|
|
|
def _collect_audio(omni: Omni, request: dict) -> tuple[torch.Tensor, int]:
|
|
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"
|
|
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")
|
|
|
|
|
|
@hardware_test(res={"cuda": "H100"})
|
|
def test_moss_tts_v15_voice_clone(v15_engine, ref_audio_path):
|
|
"""MOSS-TTS-v1.5 (8B): voice_clone produces non-empty 24 kHz audio."""
|
|
req = _build_request("Hello, this is a MOSS-TTS v1.5 voice cloning test.", ref_audio_path)
|
|
audio, sr = _collect_audio(v15_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"
|