update finetuning pipeline and runtime device handling

Support optional ref_audio samples in finetuning and make runtime device selection explicit while keeping auto fallback behavior consistent. Also ignore the local app override file to avoid accidental commits.

Made-with: Cursor
This commit is contained in:
刘鑫
2026-04-11 11:08:50 +08:00
parent abf01b9bf3
commit e4e049624c
10 changed files with 379 additions and 47 deletions
+31
View File
@@ -58,6 +58,7 @@ def test_parser_defaults_to_voxcpm2():
parser = cli._build_parser()
args = parser.parse_args(["design", "--text", "hello", "--output", "out.wav"])
assert args.hf_model_id == "openbmb/VoxCPM2"
assert args.device == "auto"
assert args.no_optimize is False
@@ -85,6 +86,7 @@ def test_load_model_respects_no_optimize_for_local_model(monkeypatch):
cli.load_model(args)
assert calls["kwargs"]["device"] == "auto"
assert calls["kwargs"]["optimize"] is False
@@ -110,6 +112,7 @@ def test_load_model_defaults_optimize_for_hf(monkeypatch):
cli.load_model(args)
assert calls["kwargs"]["device"] == "auto"
assert calls["kwargs"]["optimize"] is True
@@ -136,9 +139,37 @@ def test_load_model_respects_no_optimize_for_hf(monkeypatch):
cli.load_model(args)
assert calls["kwargs"]["device"] == "auto"
assert calls["kwargs"]["optimize"] is False
def test_load_model_passes_explicit_device_to_hf(monkeypatch):
calls = {}
class FakeVoxCPM:
@classmethod
def from_pretrained(cls, **kwargs):
calls["kwargs"] = kwargs
return DummyModel()
monkeypatch.setattr(cli, "VoxCPM", FakeVoxCPM)
args = cli._build_parser().parse_args(
[
"design",
"--text",
"hello",
"--output",
"out.wav",
"--device",
"mps",
]
)
cli.load_model(args)
assert calls["kwargs"]["device"] == "mps"
def test_design_subcommand_applies_control(monkeypatch, tmp_path):
dummy_model = DummyModel()
monkeypatch.setattr(cli, "load_model", lambda args: dummy_model)
+49
View File
@@ -0,0 +1,49 @@
from __future__ import annotations
import importlib.util
import sys
import types
from pathlib import Path
import pytest
ROOT = Path(__file__).resolve().parents[1]
UTILS_PATH = ROOT / "src" / "voxcpm" / "model" / "utils.py"
transformers_stub = types.ModuleType("transformers")
transformers_stub.PreTrainedTokenizer = object
sys.modules.setdefault("transformers", transformers_stub)
spec = importlib.util.spec_from_file_location("voxcpm.model.utils", UTILS_PATH)
utils = importlib.util.module_from_spec(spec)
assert spec.loader is not None
spec.loader.exec_module(utils)
def test_resolve_runtime_device_auto_falls_back_to_cpu(monkeypatch):
monkeypatch.setattr(utils.torch.cuda, "is_available", lambda: False)
monkeypatch.setattr(utils, "_has_mps", lambda: False)
assert utils.resolve_runtime_device(None, "cuda") == "cpu"
def test_resolve_runtime_device_auto_uses_mps_when_available(monkeypatch):
monkeypatch.setattr(utils.torch.cuda, "is_available", lambda: False)
monkeypatch.setattr(utils, "_has_mps", lambda: True)
assert utils.resolve_runtime_device("auto", "cuda") == "mps"
def test_resolve_runtime_device_respects_explicit_cpu(monkeypatch):
monkeypatch.setattr(utils.torch.cuda, "is_available", lambda: True)
monkeypatch.setattr(utils, "_has_mps", lambda: True)
assert utils.resolve_runtime_device("cpu", "cuda") == "cpu"
def test_resolve_runtime_device_rejects_unavailable_explicit_cuda(monkeypatch):
monkeypatch.setattr(utils.torch.cuda, "is_available", lambda: False)
monkeypatch.setattr(utils, "_has_mps", lambda: True)
with pytest.raises(ValueError, match="CUDA is not available"):
utils.resolve_runtime_device("cuda:0", "cuda")