v1.0

tests/test_voice_training.py

@@ -0,0 +1,178 @@
+"""Voice training pipeline: warm start/resume, piper compatibility fixes, data filters."""
+from __future__ import annotations
+
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+
+HIGH_ARCH = {
+    "resblock": "1", "resblock_kernel_sizes": (3, 7, 11),
+    "resblock_dilation_sizes": ((1, 3, 5), (1, 3, 5), (1, 3, 5)),
+    "upsample_rates": (8, 8, 2, 2), "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": (16, 16, 4, 4),
+}
+
+
+def _voice_dir(root: Path) -> Path:
+    training = root / "v" / "training"
+    (training / "wavs").mkdir(parents=True)
+    (training / "metadata.csv").write_text("clip_00000|hallo|hallo\n", encoding="utf-8")
+    return root / "v"
+
+
+class TrainingStartTests(unittest.TestCase):
+    def setUp(self) -> None:
+        try:
+            import torch  # noqa: F401
+        except ImportError:
+            self.skipTest("torch not installed")
+        from javis.voices import piper_train
+        self.pt = piper_train
+        self.cmds: list[list[str]] = []
+
+        def fake_run(cmd, **kw):
+            self.cmds.append(cmd)
+            return 1  # stop after stage 1
+
+        patches = [patch.object(piper_train, "_run", fake_run),
+                   patch.object(piper_train, "MIN_FREE_BYTES", 0)]
+        for p in patches:
+            p.start()
+            self.addCleanup(p.stop)
+
+    def _ckpt(self, path: Path, **hp) -> Path:
+        import torch
+        path.parent.mkdir(parents=True, exist_ok=True)
+        torch.save({"state_dict": {"w": torch.zeros(1)}, "hyper_parameters": hp}, path)
+        return path
+
+    def test_first_run_warm_starts_from_base_with_its_architecture(self) -> None:
+        import yaml
+        with tempfile.TemporaryDirectory() as d:
+            voice = _voice_dir(Path(d))
+            self._ckpt(voice / "training" / "piper_base" / "epoch=2665-step=1.ckpt",
+                       dataset=[Path("x")], **HIGH_ARCH)
+            self.pt.run_pipeline(voice)
+            cmd = self.cmds[0]
+            self.assertIn("javis.voices.piper_compat", cmd)
+            self.assertIn("--model.warmstart_ckpt", cmd)
+            self.assertNotIn("--ckpt_path", cmd)
+            arch = yaml.safe_load(Path(cmd[cmd.index("--config") + 1]).read_text(encoding="utf-8"))
+            self.assertEqual(arch["model"]["resblock"], "1")
+            self.assertEqual(arch["model"]["upsample_rates"], [8, 8, 2, 2])
+            self.assertFalse((voice / "training" / "warmstart.pt").exists())  # removed after fit
+
+    def test_later_runs_resume_own_checkpoint_and_ignore_base(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            voice = _voice_dir(Path(d))
+            base = self._ckpt(voice / "training" / "piper_base" / "epoch=1.ckpt", **HIGH_ARCH)
+            last = self._ckpt(voice / "training" / "piper_lightning" / "lightning_logs"
+                              / "version_0" / "checkpoints" / "last.ckpt", **HIGH_ARCH)
+            self.pt.run_pipeline(voice, base_checkpoint=base)
+            cmd = self.cmds[0]
+            self.assertEqual(cmd[cmd.index("--ckpt_path") + 1], str(last))
+            self.assertNotIn("--model.warmstart_ckpt", cmd)
+
+    def test_linux_checkpoint_with_posixpath_loads(self) -> None:
+        import pathlib
+        import torch
+
+        class FakePosixPath:  # pickled as pathlib.PosixPath, like a Linux checkpoint
+            def __init__(self, p):
+                self.p = p
+
+            def __reduce__(self):
+                return (FakePosixPath, (self.p,))
+
+        FakePosixPath.__module__ = "pathlib"
+        FakePosixPath.__name__ = FakePosixPath.__qualname__ = "PosixPath"
+        with tempfile.TemporaryDirectory() as d:
+            p = Path(d) / "linux.ckpt"
+            with patch.object(pathlib, "PosixPath", FakePosixPath):
+                torch.save({"hyper_parameters": {"dataset": [FakePosixPath("/ssd/x")]}}, p)
+            ckpt = self.pt._load_checkpoint(p)
+            self.assertEqual(str(ckpt["hyper_parameters"]["dataset"][0]), "/ssd/x")
+
+
+class PiperCompatTests(unittest.TestCase):
+    def test_int_resblock_builds_high_quality_blocks(self) -> None:
+        try:
+            import javis.voices.piper_compat  # noqa: F401 — applies the fixes
+            from piper.train.vits import models, modules
+            from pysilero_vad import SileroVoiceActivityDetector
+        except ImportError:
+            self.skipTest("piper training extras not installed")
+        g = models.Generator(192, 1, (3, 7, 11), ((1, 3, 5),) * 3, (8, 8, 2, 2), 64, (16, 16, 4, 4))
+        self.assertIsInstance(g.resblocks[0], modules.ResBlock1)
+        self.assertTrue(hasattr(SileroVoiceActivityDetector, "process_array"))
+
+
+class TranscriptGateTests(unittest.TestCase):
+    def test_unreliable_transcripts_are_rejected(self) -> None:
+        from javis.voices.audiobook_ingest import _transcript_ok
+
+        def tr(**kw):
+            return SimpleNamespace(**{"avg_logprob": -0.2, "no_speech_prob": 0.01,
+                                      "compression_ratio": 1.4, **kw})
+        self.assertTrue(_transcript_ok(tr()))
+        self.assertTrue(_transcript_ok(tr(avg_logprob=None, no_speech_prob=None,
+                                          compression_ratio=None)))
+        self.assertFalse(_transcript_ok(tr(avg_logprob=-1.3)))
+        self.assertFalse(_transcript_ok(tr(no_speech_prob=0.9)))
+        self.assertFalse(_transcript_ok(tr(compression_ratio=3.1)))
+
+
+class NarratorFilterTests(unittest.TestCase):
+    def test_dominant_speaker_scores_high(self) -> None:
+        from javis.voices.dataset_clean import narrator_scores
+
+        rng = np.random.default_rng(0)
+        a, b = np.eye(8)[0], np.eye(8)[1]
+        embs = np.array([v + rng.normal(0, 0.1, 8) for v in [a] * 70 + [b] * 30])
+        embs /= np.linalg.norm(embs, axis=1, keepdims=True)
+        s = narrator_scores(embs)
+        self.assertGreater(s[:70].min(), 0.8)
+        self.assertLess(s[70:].max(), 0.4)
+
+    def test_clean_dataset_filters_and_keeps_new_imports(self) -> None:
+        import soundfile as sf
+        from javis.voices.dataset_clean import clean_dataset
+
+        def embed(audio, sr):
+            # The first sample marks the speaker: 0.5 = narrator, else character voice.
+            return np.eye(4)[0] if audio[0] > 0.4 else np.eye(4)[1]
+
+        with tempfile.TemporaryDirectory() as d:
+            training = Path(d)
+            (training / "wavs").mkdir()
+
+            def add(stem: str, narrator: bool) -> str:
+                sf.write(str(training / "wavs" / f"{stem}.wav"),
+                         np.full(100, 0.5 if narrator else 0.1, dtype=np.float32), 22050)
+                return f"{stem}|text {stem}|text {stem}\n"
+
+            lines = [add(f"clip_{i:05d}", narrator=i % 4 != 0) for i in range(8)]
+            (training / "metadata.csv").write_text("".join(lines), encoding="utf-8")
+
+            report = clean_dataset(training, embed, dry_run=True)
+            self.assertEqual((report["kept"], report["dropped"]), (6, 2))
+            self.assertEqual(len((training / "metadata.csv").read_text().splitlines()), 8)
+
+            clean_dataset(training, embed)
+            self.assertEqual(len((training / "metadata.csv").read_text().splitlines()), 6)
+            self.assertEqual(len((training / "metadata.unfiltered.csv").read_text().splitlines()), 8)
+
+            # A later import appends to metadata.csv; re-filtering must keep it.
+            with open(training / "metadata.csv", "a", encoding="utf-8") as f:
+                f.write(add("clip_00100", narrator=True))
+            report = clean_dataset(training, embed)
+            self.assertEqual((report["clips"], report["kept"]), (9, 7))
+
+
+if __name__ == "__main__":
+    sys.exit(unittest.main())