[CI]Remove SoulX-Singer support (#6362)
Signed-off-by: wangyu <410167048@qq.com>
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@@ -1,16 +0,0 @@
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[
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{
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"index": "vocal_240_51240",
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"language": "Mandarin",
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"time": [
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240,
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51240
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],
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"text": "<SP> 只 是 因 为 为 在 人 群 群 中 多 看 了 你 一 眼 <SP> 再 也 没 能 忘 掉 你 容 颜 <SP> 梦 想 着 着 偶 偶 然 然 有 <SP> 一 一 天 再 相 见 <SP> 从 此 我 开 始 始 孤 孤 单 思 念 念 <SP> 想 想 你 时 你 你 在 天 边 <SP> 想 你 时 你 在 眼 前 前 <SP> 想 你 时 你 你 在 脑 海 <SP>",
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0.Line truncated
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}
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@@ -1,16 +0,0 @@
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[
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{
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"index": "vocal_320_10687",
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"language": "Mandarin",
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"time": [
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320,
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10687
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],
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"duration": "0.23 0.34 0.26 0.70 0.52 0.46 0.36 0.44 0.14 0.24 0.64 0.47 0.51 1.10 0.28 0.38 0.32 0.32 0.38 0.32 0.31 0.19 1.45",
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"text": "<SP> 除 了 想 你 你 <SP> 除 了 了 爱 你 你 <SP> 我 什 么 什 么 都 愿 愿 意",
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"phoneme": "<SP> zh_chu2 zh_le5 zh_xiang3 zh_ni3 zh_ni3 <SP> zh_chu2 zh_le5 zh_le5 zh_ai4 zh_ni3 zh_ni3 <SP> zh_wo3 zh_shen2 zh_me5 zh_shen2 zh_me5 zh_dou1 zh_yuan4 zh_yuan4 zh_yi4",
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"note_pitch": "0 62 65 67 67 69 0 67 69 67 65 67 69 67 67 66 64 64 60 60 65 67 0",
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}
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@@ -1,241 +0,0 @@
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"""E2E offline inference tests for SoulX-Singer (single-stage, preprocess inline)."""
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import functools
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import importlib
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import json
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import os
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import shutil
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from pathlib import Path
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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import numpy as np
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import pytest
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from tests.helpers.mark import hardware_test
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from tests.helpers.media import get_asset_path
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from tests.helpers.runtime import OmniRunner
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from tests.helpers.stage_config import get_deploy_config_path
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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PROMPT_AUDIO = get_asset_path("soulxsinger/zh_prompt.mp3")
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TARGET_AUDIO = get_asset_path("soulxsinger/music.mp3")
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PROMPT_METADATA = get_asset_path("soulxsinger/zh_prompt.json")
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TARGET_METADATA = get_asset_path("soulxsinger/music.json")
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SAMPLE_RATE = 24_000
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# phone_set.json (phoneme vocab) is not shipped on HuggingFace; SVS loads it
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# from the model dir. Stage it from this pinned upstream commit so the SVS DiT
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# can run in CI; the SVS tests skip gracefully if it cannot be fetched.
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_PHONE_SET_URL = (
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"https://raw.githubusercontent.com/Soul-AILab/SoulX-Singer/"
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"81aeb3ae772c70093c3de74dc23c92d983801ae4/soulxsinger/utils/phoneme/phone_set.json"
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)
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if not PROMPT_AUDIO.is_file() or not TARGET_AUDIO.is_file():
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pytest.skip(
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f"Missing SoulX-Singer audio assets: {PROMPT_AUDIO.name}, {TARGET_AUDIO.name}",
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allow_module_level=True,
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)
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pytestmark = [pytest.mark.slow, pytest.mark.diffusion, pytest.mark.tts]
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_CASES = (
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pytest.param(
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"SoulXSingerPipeline",
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"soulxsinger_svs.yaml",
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{
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"language": "Mandarin",
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"vocal_sep": False,
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"control": "score",
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"auto_shift": False,
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"pitch_shift": 0,
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},
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("g2pM", "g2p_en"),
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id="svs",
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),
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pytest.param(
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"SoulXSingerSVCPipeline",
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"soulxsinger_svc.yaml",
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{"vocal_sep": False, "auto_shift": False, "pitch_shift": 0},
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(),
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id="svc",
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),
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)
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@functools.lru_cache(maxsize=1)
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def _resolve_weights() -> tuple[Path, Path, Path]:
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from huggingface_hub import snapshot_download
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base = Path(snapshot_download("Soul-AILab/SoulX-Singer", allow_patterns=["*"]))
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# phone_set.json is not on HF; best-effort stage it from pinned upstream so
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# SVS can load. SVS tests skip (not fail) if both this and a manual copy are absent.
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phone_set = base / "phoneme" / "phone_set.json"
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if not phone_set.is_file() and not (base / "phone_set.json").is_file():
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try:
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from urllib.request import urlretrieve
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phone_set.parent.mkdir(exist_ok=True)
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urlretrieve(_PHONE_SET_URL, phone_set)
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except Exception:
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pass
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# make a temporary svc directory
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svc_dir = base.parent / "SoulX-Singer-SVC"
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svc_dir.mkdir(parents=True, exist_ok=True)
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shutil.copyfile(base / "config.yaml", svc_dir / "config.yaml")
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shutil.move(base / "model-svc.pt", svc_dir / "model-svc.pt")
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if raw := os.environ.get("SOULX_PREPROCESS_WEIGHTS_DIR"):
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pre = Path(raw).expanduser().resolve()
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if (pre / "rmvpe" / "rmvpe.pt").is_file():
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return base, svc_dir, pre
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from huggingface_hub import snapshot_download
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pre = Path(snapshot_download("Soul-AILab/SoulX-Singer-Preprocess", allow_patterns=["*"]))
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return base, svc_dir, pre
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|
||||
@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"
|
||||
@@ -78,8 +78,6 @@ EXCLUDED_MODELS = [
|
||||
"StableDiffusionXLPipeline",
|
||||
"Gr00tN1d7Pipeline",
|
||||
"Pi0Pipeline",
|
||||
"SoulXSingerPipeline",
|
||||
"SoulXSingerSVCPipeline",
|
||||
"SanaWmPipeline",
|
||||
]
|
||||
|
||||
|
||||
Reference in new issue
Block a user