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[CI]Remove SoulX-Singer support (#6362)

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Signed-off-by: wangyu <410167048@qq.com>
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wangyu authored and GitHub committed 2026-08-23 07:16:32 +08:00
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tests/e2e/offline_inference/test_soulxsinger_expansion.py
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"""E2E offline inference tests for SoulX-Singer (single-stage, preprocess inline)."""
import functools
import importlib
import json
import os
import shutil
from pathlib import Path
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
import numpy as np
import pytest
from tests.helpers.mark import hardware_test
from tests.helpers.media import get_asset_path
from tests.helpers.runtime import OmniRunner
from tests.helpers.stage_config import get_deploy_config_path
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
PROMPT_AUDIO = get_asset_path("soulxsinger/zh_prompt.mp3")
TARGET_AUDIO = get_asset_path("soulxsinger/music.mp3")
PROMPT_METADATA = get_asset_path("soulxsinger/zh_prompt.json")
TARGET_METADATA = get_asset_path("soulxsinger/music.json")
SAMPLE_RATE = 24_000
# phone_set.json (phoneme vocab) is not shipped on HuggingFace; SVS loads it
# from the model dir. Stage it from this pinned upstream commit so the SVS DiT
# can run in CI; the SVS tests skip gracefully if it cannot be fetched.
_PHONE_SET_URL = (
"https://raw.githubusercontent.com/Soul-AILab/SoulX-Singer/"
"81aeb3ae772c70093c3de74dc23c92d983801ae4/soulxsinger/utils/phoneme/phone_set.json"
)
if not PROMPT_AUDIO.is_file() or not TARGET_AUDIO.is_file():
pytest.skip(
f"Missing SoulX-Singer audio assets: {PROMPT_AUDIO.name}, {TARGET_AUDIO.name}",
allow_module_level=True,
)
pytestmark = [pytest.mark.slow, pytest.mark.diffusion, pytest.mark.tts]
_CASES = (
pytest.param(
"SoulXSingerPipeline",
"soulxsinger_svs.yaml",
{
"language": "Mandarin",
"vocal_sep": False,
"control": "score",
"auto_shift": False,
"pitch_shift": 0,
},
("g2pM", "g2p_en"),
id="svs",
),
pytest.param(
"SoulXSingerSVCPipeline",
"soulxsinger_svc.yaml",
{"vocal_sep": False, "auto_shift": False, "pitch_shift": 0},
(),
id="svc",
),
)
@functools.lru_cache(maxsize=1)
def _resolve_weights() -> tuple[Path, Path, Path]:
from huggingface_hub import snapshot_download
base = Path(snapshot_download("Soul-AILab/SoulX-Singer", allow_patterns=["*"]))
# phone_set.json is not on HF; best-effort stage it from pinned upstream so
# SVS can load. SVS tests skip (not fail) if both this and a manual copy are absent.
phone_set = base / "phoneme" / "phone_set.json"
if not phone_set.is_file() and not (base / "phone_set.json").is_file():
try:
from urllib.request import urlretrieve
phone_set.parent.mkdir(exist_ok=True)
urlretrieve(_PHONE_SET_URL, phone_set)
except Exception:
pass
# make a temporary svc directory
svc_dir = base.parent / "SoulX-Singer-SVC"
svc_dir.mkdir(parents=True, exist_ok=True)
shutil.copyfile(base / "config.yaml", svc_dir / "config.yaml")
shutil.move(base / "model-svc.pt", svc_dir / "model-svc.pt")
if raw := os.environ.get("SOULX_PREPROCESS_WEIGHTS_DIR"):
pre = Path(raw).expanduser().resolve()
if (pre / "rmvpe" / "rmvpe.pt").is_file():
return base, svc_dir, pre
from huggingface_hub import snapshot_download
pre = Path(snapshot_download("Soul-AILab/SoulX-Singer-Preprocess", allow_patterns=["*"]))
return base, svc_dir, pre
@pytest.fixture(scope="session")
def soulx_weights() -> tuple[Path, Path, Path]:
try:
return _resolve_weights()
except Exception as exc:
pytest.skip(f"Set SOULXSINGER_MODEL_DIR / SOULX_PREPROCESS_WEIGHTS_DIR. ({exc})")
def _flatten_audio(audio_val) -> np.ndarray:
import torch
if isinstance(audio_val, list):
chunks = [c.detach().cpu().float().numpy().reshape(-1) for c in audio_val if c is not None]
return np.concatenate(chunks) if chunks else np.array([], dtype=np.float32)
if isinstance(audio_val, torch.Tensor):
return audio_val.detach().cpu().float().numpy().reshape(-1)
return np.asarray(audio_val, dtype=np.float32).reshape(-1)
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@pytest.mark.parametrize("architecture,deploy_yaml,extra_args,py_deps", _CASES)
def test_soulxsinger_multistage_from_audio(
soulx_weights: tuple[Path, Path, Path],
architecture: str,
deploy_yaml: str,
extra_args: dict,
py_deps: tuple[str, ...],
) -> None:
for mod in py_deps:
try:
importlib.import_module(mod)
except ImportError as exc:
pytest.fail(f"SoulX SVS requires {mod}: {exc}")
base_dir, svc_dir, preprocess_dir = soulx_weights
# SVS mode requires phone_set.json in the model directory
if architecture == "SoulXSingerPipeline":
json.dump(
{"model_type": "soulxsinger", "architectures": ["SoulXSingerPipeline"], "max_num_seqs": 1},
open(base_dir / "config.json", "w"),
indent=4,
)
model = str(base_dir)
if not (base_dir / "phoneme" / "phone_set.json").is_file() and not (base_dir / "phone_set.json").is_file():
pytest.skip(
"SoulX-Singer SVS test requires phoneme/phone_set.json. "
"Copy it from github.com/Soul-AILab/SoulX-Singer into the model dir. "
"See `examples/offline_inference/text_to_speech/README.md` for details."
)
if architecture == "SoulXSingerSVCPipeline":
json.dump(
{"model_type": "soulxsinger", "architectures": ["SoulXSingerSVCPipeline"], "max_num_seqs": 1},
open(svc_dir / "config.json", "w"),
indent=4,
)
model = str(svc_dir)
with OmniRunner(
model,
deploy_config=get_deploy_config_path(deploy_yaml),
async_chunk=False,
) as runner:
sampling = OmniDiffusionSamplingParams(
num_inference_steps=4,
guidance_scale=3.0,
seed=42,
extra_args={
"prompt_audio": str(PROMPT_AUDIO),
"target_audio": str(TARGET_AUDIO),
"preprocess_weights_dir": str(preprocess_dir),
**extra_args,
},
)
prompt = {"prompt_token_ids": [0]}
outputs = runner.generate([prompt], sampling)
assert outputs and outputs[0].error is None, outputs[0].error if outputs else "no output"
mm = outputs[0].multimodal_output
assert isinstance(mm, dict) and "audio" in mm
audio = _flatten_audio(mm["audio"])
assert 12_000 <= audio.size
assert np.isfinite(audio).all() and float(np.max(np.abs(audio))) > 1e-4
duration_s = audio.size / SAMPLE_RATE
assert 50.0 <= duration_s <= 52.0, f"duration={duration_s:.1f}s"
@hardware_test(res={"cuda": "L4"}, num_cards=1)
def test_soulxsinger_svs_precomputed(soulx_weights: tuple[Path, Path, Path]) -> None:
"""SVS from precomputed note/lyric metadata.
Exercises the SVS DiT + vocoder serving path while skipping the heavy
upstream preprocess (funasr ASR + ROSVOT note transcription), so it runs in
the merge gate without those non-pip dependencies.
"""
base_dir, _svc_dir, preprocess_dir = soulx_weights
if not PROMPT_METADATA.is_file() or not TARGET_METADATA.is_file():
pytest.skip(f"Missing SoulX-Singer metadata fixtures: {PROMPT_METADATA.name}, {TARGET_METADATA.name}")
if not (base_dir / "phoneme" / "phone_set.json").is_file() and not (base_dir / "phone_set.json").is_file():
pytest.skip(
"SoulX-Singer SVS requires phoneme/phone_set.json (upstream fetch unavailable). "
"See `examples/offline_inference/text_to_speech/README.md`."
)
json.dump(
{"model_type": "soulxsinger", "architectures": ["SoulXSingerPipeline"], "max_num_seqs": 1},
open(base_dir / "config.json", "w"),
indent=4,
)
with OmniRunner(
str(base_dir),
deploy_config=get_deploy_config_path("soulxsinger_svs.yaml"),
async_chunk=False,
) as runner:
sampling = OmniDiffusionSamplingParams(
num_inference_steps=4,
guidance_scale=3.0,
seed=42,
extra_args={
"language": "Mandarin",
"control": "score",
"prompt_metadata_path": str(PROMPT_METADATA),
"target_metadata_path": str(TARGET_METADATA),
"audio_path": str(PROMPT_AUDIO),
"preprocess_weights_dir": str(preprocess_dir),
},
)
prompt = {"prompt_token_ids": [0]}
outputs = runner.generate([prompt], sampling)
assert outputs and outputs[0].error is None, outputs[0].error if outputs else "no output"
mm = outputs[0].multimodal_output
assert isinstance(mm, dict) and "audio" in mm
audio = _flatten_audio(mm["audio"])
assert 12_000 <= audio.size
assert np.isfinite(audio).all() and float(np.max(np.abs(audio))) > 1e-4
duration_s = audio.size / SAMPLE_RATE
assert 50.0 <= duration_s <= 52.0, f"duration={duration_s:.1f}s"