diff --git a/.github/workflows/tests-macos.yml b/.github/workflows/tests-macos.yml index d53ce2b..28f8cab 100644 --- a/.github/workflows/tests-macos.yml +++ b/.github/workflows/tests-macos.yml @@ -25,10 +25,7 @@ jobs: python-version: '3.11' - name: Install uv - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - shell: bash + uses: astral-sh/setup-uv@v3 - name: Configure Git for private repos run: | diff --git a/.github/workflows/tests-ubuntu.yml b/.github/workflows/tests-ubuntu.yml index 0401d15..3e933f0 100644 --- a/.github/workflows/tests-ubuntu.yml +++ b/.github/workflows/tests-ubuntu.yml @@ -24,11 +24,31 @@ jobs: with: python-version: '3.11' - - name: Install uv + - name: Install Qt system dependencies run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - shell: bash + sudo apt-get update + sudo apt-get install -y \ + libegl1 \ + libgl1 \ + libglib2.0-0 \ + libdbus-1-3 \ + libxkbcommon0 \ + libxcb-icccm4 \ + libxcb-image0 \ + libxcb-keysyms1 \ + libxcb-randr0 \ + libxcb-render-util0 \ + libxcb-shape0 \ + libxcb-sync1 \ + libxcb-xfixes0 \ + libxcb-xinerama0 \ + libxcb-cursor0 \ + libfontconfig1 \ + libxkbcommon-x11-0 \ + xvfb + + - name: Install uv + uses: astral-sh/setup-uv@v3 - name: Configure Git for private repos run: | @@ -40,7 +60,7 @@ jobs: - name: Run tests run: | - uv run invoke run-tests + xvfb-run -a uv run invoke run-tests - name: Run linting run: | diff --git a/.github/workflows/tests-windows.yml b/.github/workflows/tests-windows.yml index 1eb5895..13d4bf8 100644 --- a/.github/workflows/tests-windows.yml +++ b/.github/workflows/tests-windows.yml @@ -25,10 +25,7 @@ jobs: python-version: '3.11' - name: Install uv - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - shell: bash + uses: astral-sh/setup-uv@v3 - name: Configure Git for private repos run: | diff --git a/.gitignore b/.gitignore index 0a9e218..468de47 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,8 @@ ENV/ build/ dist/ downloads/ +installer/windows/output/ +tools/ .eggs/ .eggs/** *.egg-info/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3da7c96..8af580e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ repos: hooks: - id: shredguard-check name: shredguard check - entry: shredguard check + entry: uv run --only-group dev shredguard check language: system types: [text] diff --git a/assets/voxkit.ico b/assets/voxkit.ico new file mode 100644 index 0000000..274be30 Binary files /dev/null and b/assets/voxkit.ico differ diff --git a/installer/linux/VoxKit.desktop b/installer/linux/VoxKit.desktop new file mode 100644 index 0000000..d8c8be2 --- /dev/null +++ b/installer/linux/VoxKit.desktop @@ -0,0 +1,8 @@ +[Desktop Entry] +Name=VoxKit +Exec=VoxKit +Icon=voxkit +Type=Application +Categories=Science;Audio;Education; +Comment=AI/ML Speech Pathology Analysis +Terminal=false diff --git a/installer/windows/VoxKit.iss b/installer/windows/VoxKit.iss new file mode 100644 index 0000000..0170b76 --- /dev/null +++ b/installer/windows/VoxKit.iss @@ -0,0 +1,42 @@ +#define AppName "VoxKit" +#define AppVersion "0.4.1" +#define AppPublisher "Brain Behavior Analytics Lab" +#define AppURL "https://github.com/BrainBehaviorAnalyticsLab/voxkit-desktop" +#define AppExeName "VoxKit.exe" + +[Setup] +AppId={{A3F2C1D4-8B7E-4F6A-9D2E-1C5B3A7E8F90} +AppName={#AppName} +AppVersion={#AppVersion} +AppPublisher={#AppPublisher} +AppPublisherURL={#AppURL} +AppSupportURL={#AppURL} +AppUpdatesURL={#AppURL} +DefaultDirName={autopf}\{#AppName} +DefaultGroupName={#AppName} +DisableProgramGroupPage=yes +OutputDir=output +OutputBaseFilename=VoxKit-setup-windowsOS +SetupIconFile=..\..\assets\voxkit.ico +Compression=lzma +SolidCompression=yes +WizardStyle=modern +PrivilegesRequired=lowest +ArchitecturesAllowed=x64compatible +ArchitecturesInstallIn64BitMode=x64compatible + +[Languages] +Name: "english"; MessagesFile: "compiler:Default.isl" + +[Tasks] +Name: "desktopicon"; Description: "{cm:CreateDesktopIcon}"; GroupDescription: "{cm:AdditionalIcons}" + +[Files] +Source: "..\..\dist\{#AppExeName}"; DestDir: "{app}"; Flags: ignoreversion + +[Icons] +Name: "{autoprograms}\{#AppName}"; Filename: "{app}\{#AppExeName}"; IconFilename: "{app}\{#AppExeName}" +Name: "{autodesktop}\{#AppName}"; Filename: "{app}\{#AppExeName}"; IconFilename: "{app}\{#AppExeName}"; Tasks: desktopicon + +[Run] +Filename: "{app}\{#AppExeName}"; Description: "{cm:LaunchProgram,{#StringChange(AppName, '&', '&&')}}"; Flags: nowait postinstall skipifsilent diff --git a/main.py b/main.py index fa00987..d2c30e8 100644 --- a/main.py +++ b/main.py @@ -1,8 +1,37 @@ import sys +import multiprocessing +import os + +# CRITICAL: Must run before any heavy imports (voxkit, torch, PyQt6, etc.). +# When PyTorch DataLoader workers spawn on Windows, each child re-launches the +# frozen exe and re-imports this module. freeze_support() short-circuits the +# child before it re-runs main() and tries to open another GUI window. +if __name__ == "__main__": + multiprocessing.freeze_support() + +# In a --windowed PyInstaller build there is no console, so sys.stdout and +# sys.stderr are None. Libraries like tqdm (used by transformers' Trainer) +# crash with AttributeError: 'NoneType' object has no attribute 'write' when +# they try to print progress. Redirect to devnull so writes silently no-op. +if sys.stdout is None: + sys.stdout = open(os.devnull, "w", encoding="utf-8") +if sys.stderr is None: + sys.stderr = open(os.devnull, "w", encoding="utf-8") + import faulthandler import logging -import os -import multiprocessing + +# Windows: configure console and stdout/stderr for UTF-8 before any output. +# Without this, rich's legacy renderer falls back to cp1252 and chokes on +# Unicode characters (e.g., circled letters in pipeline config YAML). +if sys.platform == 'win32': + try: + import ctypes + ctypes.windll.kernel32.SetConsoleOutputCP(65001) + sys.stdout.reconfigure(encoding='utf-8', errors='replace') + sys.stderr.reconfigure(encoding='utf-8', errors='replace') + except Exception: + pass # Apply patches for frozen (PyInstaller) environment BEFORE other imports if getattr(sys, 'frozen', False): @@ -17,10 +46,6 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s") log = logging.getLogger("voxkit.main") -# CRITICAL: Must be at the top for frozen apps using multiprocessing -if __name__ == "__main__": - multiprocessing.freeze_support() - # Enable detailed crash reports faulthandler.enable() diff --git a/pyproject.toml b/pyproject.toml index d65a0eb..b5c13ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,8 +16,8 @@ dependencies = [ "pyqt6>=6.9.1", "torch==2.8.0", "torchaudio==2.8.0", - "pypllrcomputer @ git+https://github.com/BrainBehaviorAnalyticsLab/PyPLLRComputer.git@48a0c934e75f73235ba5a002538b8588fd6697e7", - "wav2textgrid @ git+https://github.com/pkadambi/Wav2TextGrid.git@3862476185a7558d481f1ae1c9cbca2e48c825a0", + "pypllrcomputer", + "wav2textgrid", "datasets>=4.3.0", "accelerate>=1.11.0", "keyring>=25.6.0", @@ -28,7 +28,8 @@ dependencies = [ "pyyaml>=6.0.0", "rich>=14.2.0", "faster-whisper>=1.1.0", - "alignment-comparison-plots==0.1.1", + "alignment-comparison-plots", + "speechbrain>=1.0.3", ] [dependency-groups] @@ -58,6 +59,12 @@ Homepage = "https://github.com/BrainBehaviorAnalyticsLab/voxkit-desktop" Repository = "https://github.com/BrainBehaviorAnalyticsLab/voxkit-desktop" Issues = "https://github.com/BrainBehaviorAnalyticsLab/voxkit-desktop/issues" +[tool.uv.sources] +pypllrcomputer = { git = "https://github.com/pkadambi/PyPhonemePronunciationScorer", branch = "voxkit-windows-variant" } +wav2textgrid = { git = "https://github.com/pkadambi/Wav2TextGrid", branch = "voxkit-windows-variant" } +alignment-comparison-plots = { git = "https://github.com/WISCLab/alignment-comparison-plots", branch = "voxkit-windows-variant" } +speechbrain = { git = "https://github.com/BeckettFrey/speechbrain.git", branch = "fix/windows-lazy-import-inspect-path" } + [tool.ruff] line-length = 100 target-version = "py310" @@ -120,7 +127,9 @@ exclude = [ "build.py", "example_startup_script.py", "test_imports.py", - "tests/" + "tests/", + "build/", + "dist/", ] diff --git a/src/voxkit/analyzers/__init__.py b/src/voxkit/analyzers/__init__.py index 4f29df5..43bcd01 100644 --- a/src/voxkit/analyzers/__init__.py +++ b/src/voxkit/analyzers/__init__.py @@ -14,6 +14,14 @@ Extracts speaker count and audio file counts per speaker directory. Includes a bar chart visualization for quick dataset overview. +**ClipDurationStatisticsAnalyzer** + Reads audio metadata to compute total, average, min, and max clip duration + per speaker. Includes a bar chart visualization of total duration. + +**AudioFormatProfileAnalyzer** + Reads audio metadata to surface dominant sample rate, channel count, and + flags files that deviate from the speaker's dominant format. + Output Structure ---------------- Analyzer output is stored alongside dataset metadata:: @@ -35,7 +43,9 @@ from typing import List +from .audio_format_profile import AudioFormatProfileAnalyzer from .base import DatasetAnalyzer +from .clip_duration_statistics import ClipDurationStatisticsAnalyzer from .default_analyzer import DefaultAnalyzer @@ -69,9 +79,18 @@ def get_analyzer(self, analyzer_id: str) -> DatasetAnalyzer: raise ValueError(f"No analyzer with id: {analyzer_id}") -default_analyzer_instance = DefaultAnalyzer() +_default = DefaultAnalyzer() +_duration = ClipDurationStatisticsAnalyzer() +_format = AudioFormatProfileAnalyzer() + # Singleton instance for unified export/interface -ManageAnalyzers = AnalyzerManager({default_analyzer_instance.name: default_analyzer_instance}) +ManageAnalyzers = AnalyzerManager( + { + _default.name: _default, + _duration.name: _duration, + _format.name: _format, + } +) __all__ = [ "ManageAnalyzers", diff --git a/src/voxkit/analyzers/audio_format_profile.py b/src/voxkit/analyzers/audio_format_profile.py new file mode 100644 index 0000000..4844d81 --- /dev/null +++ b/src/voxkit/analyzers/audio_format_profile.py @@ -0,0 +1,84 @@ +"""Audio Format Profile Analyzer. + +Reads audio metadata (no decode) to surface sample rate and channel count +per speaker. Flags speakers with inconsistent formats across their files. + +Output Columns +-------------- +- **speaker_id**: Name of the speaker subdirectory +- **file_count**: Number of audio files scanned +- **dominant_sample_rate_hz**: Most common sample rate across the speaker's files +- **dominant_channels**: Most common channel count (1 = mono, 2 = stereo) +- **inconsistent_files**: Number of files that differ from the dominant sample rate +""" + +import logging +import os +from collections import Counter +from pathlib import Path +from typing import Any, Dict, List + +from .base import DatasetAnalyzer + +logger = logging.getLogger(__name__) + + +class AudioFormatProfileAnalyzer(DatasetAnalyzer): + """Per-speaker audio format profile: sample rate, channels, and consistency.""" + + @property + def name(self) -> str: + return "Audio Format Profile" + + @property + def description(self) -> str: + return "Sample rate, channel count, and format consistency per speaker" + + def analyze(self, dataset_path: str) -> List[Dict[str, Any]]: + import torchaudio + + results = [] + audio_extensions = {".wav", ".flac", ".mp3", ".ogg", ".m4a"} + + try: + for entry in os.scandir(dataset_path): + if not entry.is_dir(): + continue + + sample_rates: List[int] = [] + channels: List[int] = [] + for f in os.scandir(entry.path): + if not f.is_file() or Path(f.name).suffix.lower() not in audio_extensions: + continue + try: + info = torchaudio.info(f.path) + if info.sample_rate > 0: + sample_rates.append(info.sample_rate) + channels.append(info.num_channels) + else: + waveform, sr = torchaudio.load(f.path) + sample_rates.append(sr) + channels.append(waveform.shape[0]) + except Exception as e: + logger.warning("Skipping %s: %s", f.path, e) + + if not sample_rates: + continue + + dominant_sr = Counter(sample_rates).most_common(1)[0][0] + dominant_ch = Counter(channels).most_common(1)[0][0] + inconsistent = sum(1 for sr in sample_rates if sr != dominant_sr) + + results.append( + { + "speaker_id": entry.name, + "file_count": len(sample_rates), + "dominant_sample_rate_hz": dominant_sr, + "dominant_channels": dominant_ch, + "inconsistent_files": inconsistent, + } + ) + except Exception as e: + print(f"Error analyzing dataset: {e}") + + return results diff --git a/src/voxkit/analyzers/clip_duration_statistics.py b/src/voxkit/analyzers/clip_duration_statistics.py new file mode 100644 index 0000000..5f8d259 --- /dev/null +++ b/src/voxkit/analyzers/clip_duration_statistics.py @@ -0,0 +1,197 @@ +"""Clip Duration Statistics Analyzer. + +Reads audio metadata (no decode) to compute per-speaker clip duration stats. + +Output Columns +-------------- +- **speaker_id**: Name of the speaker subdirectory +- **file_count**: Number of audio files scanned +- **total_duration_s**: Total audio duration in seconds +- **avg_duration_s**: Average clip duration in seconds +- **min_duration_s**: Shortest clip in seconds +- **max_duration_s**: Longest clip in seconds +""" + +import logging +import os +from pathlib import Path +from typing import Any, Dict, List + +from .base import DatasetAnalyzer + +logger = logging.getLogger(__name__) + + +class ClipDurationStatisticsAnalyzer(DatasetAnalyzer): + """Per-speaker clip duration statistics derived from audio file metadata.""" + + @property + def name(self) -> str: + return "Clip Duration Statistics" + + @property + def description(self) -> str: + return "Total, average, min, and max clip duration per speaker" + + def analyze(self, dataset_path: str) -> List[Dict[str, Any]]: + import torchaudio + + results = [] + audio_extensions = {".wav", ".flac", ".mp3", ".ogg", ".m4a"} + + try: + for entry in os.scandir(dataset_path): + if not entry.is_dir(): + continue + + durations: List[float] = [] + for f in os.scandir(entry.path): + if not f.is_file() or Path(f.name).suffix.lower() not in audio_extensions: + continue + try: + info = torchaudio.info(f.path) + if info.num_frames > 0: + durations.append(info.num_frames / info.sample_rate) + else: + # num_frames is unreliable for some formats (MP3, M4A, OGG) + waveform, sr = torchaudio.load(f.path) + durations.append(waveform.shape[1] / sr) + except Exception as e: + logger.warning("Skipping %s: %s", f.path, e) + + if durations: + total = sum(durations) + results.append( + { + "speaker_id": entry.name, + "file_count": len(durations), + "total_duration_s": round(total, 2), + "avg_duration_s": round(total / len(durations), 2), + "min_duration_s": round(min(durations), 2), + "max_duration_s": round(max(durations), 2), + } + ) + except Exception as e: + print(f"Error analyzing dataset: {e}") + + return results + + def visualize(self, data: List[Dict[str, Any]]): + from PyQt6.QtCore import QRectF, Qt + from PyQt6.QtGui import QColor, QFont, QPainter + from PyQt6.QtWidgets import QLabel, QScrollArea, QVBoxLayout, QWidget + + entries = [] + for row in data: + speaker = str(row.get("speaker_id", "")) + try: + total = float(row.get("total_duration_s", 0)) + avg = float(row.get("avg_duration_s", 0)) + file_count = int(row.get("file_count", 0)) + except (TypeError, ValueError): + total, avg, file_count = 0.0, 0.0, 0 + entries.append((speaker, total, avg, file_count)) + entries.sort(key=lambda x: x[1], reverse=True) + + if not entries: + empty = QLabel("No data to visualize.") + empty.setAlignment(Qt.AlignmentFlag.AlignCenter) + return empty + + BAR_HEIGHT = 28 + BAR_SPACING = 6 + LABEL_WIDTH = 140 + PADDING = 16 + max_total = max(e[1] for e in entries) or 1 + VALUE_WIDTH = 120 + total_h = PADDING + len(entries) * (BAR_HEIGHT + BAR_SPACING) + PADDING + + class _Canvas(QWidget): + def __init__(self, parent=None): + super().__init__(parent) + self.setMinimumHeight(max(total_h, 100)) + self.setMinimumWidth(400) + + def paintEvent(self, event): + painter = QPainter(self) + painter.setRenderHint(QPainter.RenderHint.Antialiasing) + bar_area = self.width() - LABEL_WIDTH - PADDING * 2 - VALUE_WIDTH + y = PADDING + + for speaker, total, avg, file_count in entries: + painter.setPen(QColor("#2c3e50")) + label_font = QFont() + label_font.setPointSize(10) + painter.setFont(label_font) + label_rect = QRectF(PADDING, y, LABEL_WIDTH, BAR_HEIGHT) + fm = painter.fontMetrics() + elided = fm.elidedText( + speaker, Qt.TextElideMode.ElideRight, int(LABEL_WIDTH - 8) + ) + painter.drawText( + label_rect, + Qt.AlignmentFlag.AlignRight | Qt.AlignmentFlag.AlignVCenter, + elided, + ) + + bar_x = PADDING + LABEL_WIDTH + 10 + bar_w = max((total / max_total) * bar_area, 2) + bar_rect = QRectF(bar_x, y + 3, bar_w, BAR_HEIGHT - 6) + + color = QColor("#27ae60") + h, s, lightness, a = color.getHsl() + assert h is not None and s is not None and lightness is not None + ratio = total / max_total + new_l = int(lightness + (220 - lightness) * (1 - ratio)) + color.setHsl(h, s, min(new_l, 240), a) + + painter.setBrush(color) + painter.setPen(Qt.PenStyle.NoPen) + painter.drawRoundedRect(bar_rect, 4, 4) + + val_font = QFont() + val_font.setPointSize(9) + val_font.setBold(True) + painter.setFont(val_font) + painter.setPen(QColor("#2c3e50")) + minutes, seconds = divmod(total, 60) + label = f"{int(minutes)}m {seconds:.0f}s ({file_count} files)" + val_rect = QRectF(bar_x + bar_w + 6, y, VALUE_WIDTH, BAR_HEIGHT) + painter.drawText( + val_rect, + Qt.AlignmentFlag.AlignLeft | Qt.AlignmentFlag.AlignVCenter, + label, + ) + + y += BAR_HEIGHT + BAR_SPACING + + painter.end() + + container = QWidget() + layout = QVBoxLayout(container) + layout.setContentsMargins(0, 0, 0, 0) + layout.setSpacing(8) + + canvas = _Canvas() + scroll = QScrollArea() + scroll.setWidget(canvas) + scroll.setWidgetResizable(True) + scroll.setStyleSheet("QScrollArea { border: none; background: white; }") + layout.addWidget(scroll) + + grand_total = sum(e[1] for e in entries) + total_files = sum(e[3] for e in entries) + n_speakers = len(entries) + overall_avg = grand_total / n_speakers if n_speakers else 0 + gt_min, gt_sec = divmod(grand_total, 60) + stats = QLabel( + f"{n_speakers} speakers | " + f"{total_files} files | " + f"Total: {int(gt_min)}m {gt_sec:.0f}s | " + f"Avg per speaker: {overall_avg:.1f}s" + ) + stats.setStyleSheet("color: #7f8c8d; font-size: 12px; font-style: italic; padding: 8px;") + stats.setAlignment(Qt.AlignmentFlag.AlignCenter) + layout.addWidget(stats) + + return container diff --git a/src/voxkit/config/app_config.py b/src/voxkit/config/app_config.py index 300169f..83d6328 100644 --- a/src/voxkit/config/app_config.py +++ b/src/voxkit/config/app_config.py @@ -151,7 +151,7 @@ def from_yaml(cls, config_path: Path) -> "AppConfig": if not config_path.exists(): raise FileNotFoundError(f"App config file not found: {config_path}") - with open(config_path, "r") as f: + with open(config_path, "r", encoding="utf-8") as f: data = yaml.safe_load(f) return cls( diff --git a/src/voxkit/config/pipeline_config.py b/src/voxkit/config/pipeline_config.py index 12e3f4f..760bfc6 100644 --- a/src/voxkit/config/pipeline_config.py +++ b/src/voxkit/config/pipeline_config.py @@ -119,7 +119,7 @@ def from_yaml(cls, config_path: Path) -> "PipelineConfig": if not config_path.exists(): raise FileNotFoundError(f"Pipeline config file not found: {config_path}") - with open(config_path, "r") as f: + with open(config_path, "r", encoding="utf-8") as f: data = yaml.safe_load(f) # Parse pipeline steps diff --git a/src/voxkit/engines/faster_whisper_engine.py b/src/voxkit/engines/faster_whisper_engine.py index b67281a..f4e0005 100644 --- a/src/voxkit/engines/faster_whisper_engine.py +++ b/src/voxkit/engines/faster_whisper_engine.py @@ -134,7 +134,7 @@ def transcribe(self, dataset_id: str) -> None: if not speaker_dir.is_dir() or speaker_dir.name.startswith("."): continue - wav_files = sorted(speaker_dir.glob("*.wav")) + wav_files = sorted(f for f in speaker_dir.glob("*.wav") if not f.name.startswith(".")) for wav_path in wav_files: lab_path = wav_path.with_suffix(".lab") diff --git a/src/voxkit/engines/mfa_engine.py b/src/voxkit/engines/mfa_engine.py index 3035779..7cfde55 100644 --- a/src/voxkit/engines/mfa_engine.py +++ b/src/voxkit/engines/mfa_engine.py @@ -159,6 +159,7 @@ def align(self, dataset_id: str, model_id: str) -> None: alignment_id=msg["id"], updates={"status": "failed"}, ) + raise def train_aligner( self, audio_root: Path, textgrid_root: Path, base_model_id: str | None, new_model_id: str @@ -204,7 +205,7 @@ def train_aligner( for key in serializable: if isinstance(serializable[key], Path): serializable[key] = str(serializable[key]) - with open(model_metadata_path, "w") as f: + with open(model_metadata_path, "w", encoding="utf-8") as f: import json json.dump(serializable, f, indent=4) diff --git a/src/voxkit/engines/w2tg_engine.py b/src/voxkit/engines/w2tg_engine.py index 8134edb..8a5b4d2 100644 --- a/src/voxkit/engines/w2tg_engine.py +++ b/src/voxkit/engines/w2tg_engine.py @@ -21,6 +21,7 @@ - See https://huggingface.co/pkadambi/Wav2TextGrid """ +import logging from pathlib import Path from voxkit.gui.frameworks.settings_modal import ( @@ -34,6 +35,8 @@ from .base import AlignmentEngine +logger = logging.getLogger(__name__) + TrainerConfiguration: SettingsConfig = SettingsConfig( title="Wav2TextGrid Trainer Settings", dimensions=(400, 300), @@ -144,6 +147,8 @@ def align(self, dataset_id: str, model_id: str) -> None: assert not isinstance(msg, str) alignment_meta = msg dataset_meta = datasets.get_dataset_metadata(dataset_id) + if dataset_meta is None: + raise ValueError(f"Dataset '{dataset_id}' not found.") model_meta = models.get_model_metadata(self.id, model_id) print(f"Aligning with settings: {settings}") @@ -153,7 +158,10 @@ def align(self, dataset_id: str, model_id: str) -> None: model_path = model_meta["model_path"] print(f"Using model path: {model_path}") - audio_root = datasets._get_dataset_root(dataset_id) + if dataset_meta["cached"]: + audio_root = datasets._get_dataset_root(dataset_id) + else: + audio_root = Path(dataset_meta["original_path"]) try: align_dirs( @@ -171,7 +179,7 @@ def align(self, dataset_id: str, model_id: str) -> None: ) except Exception as e: - print(f"Alignment failed: {e}") + logger.exception("W2TG alignment failed") alignments.update_alignment( dataset_id=dataset_id, alignment_id=alignment_meta["id"], @@ -236,7 +244,7 @@ def train_aligner( download_nltk=True, ) except Exception as e: - print(f"Training failed: {e}") + logger.exception("W2TG training failed") # Clean up model entry on failure if new_model_actual_id is not None: models.delete_model(engine_id=self.id, model_id=new_model_actual_id) diff --git a/src/voxkit/gui/frameworks/settings_modal/generic.py b/src/voxkit/gui/frameworks/settings_modal/generic.py index d8a65bc..f65b662 100644 --- a/src/voxkit/gui/frameworks/settings_modal/generic.py +++ b/src/voxkit/gui/frameworks/settings_modal/generic.py @@ -157,7 +157,7 @@ def _load_saved_values(self): print("Saved values json doesn't exist yet.") return try: - with open(self.store_values_path, "r") as f: + with open(self.store_values_path, "r", encoding="utf-8") as f: saved_values = json.load(f) for name, value in saved_values.items(): print(f"Loading saved value for {name}: {value}") diff --git a/src/voxkit/gui/pages/datasets/datasets_page.py b/src/voxkit/gui/pages/datasets/datasets_page.py index a45c841..2957fdd 100644 --- a/src/voxkit/gui/pages/datasets/datasets_page.py +++ b/src/voxkit/gui/pages/datasets/datasets_page.py @@ -306,6 +306,7 @@ def _create_list_section(self): QTableWidget { gridline-color: #ecf0f1; background-color: white; + alternate-background-color: #f5f8fa; border: 1px solid #bdc3c7; border-radius: 4px; } diff --git a/src/voxkit/gui/pages/pipeline/pllr_stacker.py b/src/voxkit/gui/pages/pipeline/pllr_stacker.py index 27d2642..65c0839 100644 --- a/src/voxkit/gui/pages/pipeline/pllr_stacker.py +++ b/src/voxkit/gui/pages/pipeline/pllr_stacker.py @@ -80,8 +80,11 @@ default_value="aggregate_by_phoneme_occurrence", options=[ "aggregate_by_phoneme_occurrence", - "aggregate_by_phoneme_average", - "aggregate_by_phoneme_median", + "aggregate_by_unique_phonemes_in_utterance", + "aggregate_by_utterance", + "aggregate_by_phoneme_per_speaker", + "aggregate_by_type_per_speaker", + "aggregate_by_speaker", ], tooltip="Method to aggregate framewise probabilities into phonewise scores.", ), @@ -358,7 +361,10 @@ def on_extract_pllr(self): ) return - textgrid_path = alignment_data["tg_path"] + "/cache" + is_cached = alignment_data["local"] == "True" or alignment_data["local"] is True + textgrid_path = ( + alignment_data["tg_path"] + "/cache" if is_cached else alignment_data["tg_path"] + ) print(f"[DEBUG] TextGrid path from alignment: {textgrid_path}") print("[DEBUG] Checking if TextGrid path exists...") @@ -486,7 +492,7 @@ def extract_pllr_logic(self, textgrid_path, wavlab_path, output_path): print(f"[LOGIC] Settings file found at: {path_to_pllr_settings}") from json import load as json_load - with open(path_to_pllr_settings, "r") as f: + with open(path_to_pllr_settings, "r", encoding="utf-8") as f: pllr_settings = json_load(f) print(f"[LOGIC] Settings loaded: {pllr_settings}") else: @@ -505,6 +511,28 @@ def extract_pllr_logic(self, textgrid_path, wavlab_path, output_path): print(" likelihood_dct=None") try: + from pypllrcomputer.pypllr_compute import ( + aggregate_by_phoneme_occurrence, + aggregate_by_phoneme_per_speaker, + aggregate_by_speaker, + aggregate_by_type_per_speaker, + aggregate_by_unique_phonemes_in_utterance, + aggregate_by_utterance, + ) + + _agg_fns = { + "aggregate_by_phoneme_occurrence": aggregate_by_phoneme_occurrence, + "aggregate_by_unique_phonemes_in_utterance": ( + aggregate_by_unique_phonemes_in_utterance + ), + "aggregate_by_utterance": aggregate_by_utterance, + "aggregate_by_phoneme_per_speaker": aggregate_by_phoneme_per_speaker, + "aggregate_by_type_per_speaker": aggregate_by_type_per_speaker, + "aggregate_by_speaker": aggregate_by_speaker, + } + agg_key = pllr_settings.get("aggregation_function", "aggregate_by_phoneme_occurrence") + agg_fn = _agg_fns.get(agg_key, aggregate_by_phoneme_occurrence) + compute_pllr( tg_files_path=textgrid_path, wav_files_path=wavlab_path, @@ -513,9 +541,7 @@ def extract_pllr_logic(self, textgrid_path, wavlab_path, output_path): framewise_proba_df=framewise_path, recompute_probas=pllr_settings.get("recompute_probas", True), likelihood_dct=pllr_settings.get("likelihood_dct", None), - # aggregation_function=pllr_settings.get( - # "aggregation_function", "aggregate_by_phoneme_occurrence" - # ), + aggregation_function=agg_fn, ) print("[LOGIC] compute_pllr() completed successfully") return "PLLR extracted successfully" diff --git a/src/voxkit/gui/pages/pipeline/training_stacker.py b/src/voxkit/gui/pages/pipeline/training_stacker.py index de2bb30..a4817d9 100644 --- a/src/voxkit/gui/pages/pipeline/training_stacker.py +++ b/src/voxkit/gui/pages/pipeline/training_stacker.py @@ -136,8 +136,19 @@ def on_train_model(self): return # Get dataset path - audio_path = datasets._get_dataset_root(selected_dataset_id) - if not audio_path: + dataset_metadata = datasets.get_dataset_metadata(selected_dataset_id) + if not dataset_metadata: + QMessageBox.warning( + self, "Invalid Dataset", f"Could not find path for dataset '{selected_dataset_id}'." + ) + return + + if bool(dataset_metadata["cached"]): + audio_path = datasets._get_dataset_root(selected_dataset_id) + else: + audio_path = Path(dataset_metadata["original_path"]) + + if not audio_path or not Path(audio_path).exists(): QMessageBox.warning( self, "Invalid Dataset", f"Could not find path for dataset '{selected_dataset_id}'." ) diff --git a/src/voxkit/gui/styles/__init__.py b/src/voxkit/gui/styles/__init__.py index b0abc8c..4db9929 100644 --- a/src/voxkit/gui/styles/__init__.py +++ b/src/voxkit/gui/styles/__init__.py @@ -665,6 +665,7 @@ class Containers: QTableWidget { gridline-color: #ecf0f1; background-color: white; + alternate-background-color: #f5f8fa; border: 1px solid #bdc3c7; border-radius: 4px; } diff --git a/src/voxkit/gui/workers/datasets_thread.py b/src/voxkit/gui/workers/datasets_thread.py index ee269af..81e9ca6 100644 --- a/src/voxkit/gui/workers/datasets_thread.py +++ b/src/voxkit/gui/workers/datasets_thread.py @@ -52,7 +52,7 @@ def run(self): self.progress.emit("Validating dataset structure...") # First validate the dataset - valid, msg = datasets.validate_dataset(self.dataset_path) + valid, msg = datasets.validate_dataset(self.dataset_path, transcribed=self.transcribed) if not valid: self.finished.emit( diff --git a/src/voxkit/services/mfa.py b/src/voxkit/services/mfa.py index ad74af0..bf373ed 100755 --- a/src/voxkit/services/mfa.py +++ b/src/voxkit/services/mfa.py @@ -1,4 +1,51 @@ +import os +import shutil import subprocess +import sys +from pathlib import Path + + +def _no_window() -> dict: + """Return creationflags to suppress console windows on Windows.""" + if sys.platform == "win32": + return {"creationflags": subprocess.CREATE_NO_WINDOW} + return {} + + +def _find_conda() -> str: + """Return the conda executable path, checking common install locations on Windows.""" + # Fast path: conda is already on PATH + if shutil.which("conda"): + return "conda" + + if sys.platform == "win32": + home = Path.home() + candidates = [ + home / "miniconda3" / "Scripts" / "conda.exe", + home / "anaconda3" / "Scripts" / "conda.exe", + home / "miniforge3" / "Scripts" / "conda.exe", + home / "mambaforge" / "Scripts" / "conda.exe", + home / "Miniconda3" / "Scripts" / "conda.exe", + home / "Anaconda3" / "Scripts" / "conda.exe", + Path("C:/ProgramData/miniconda3/Scripts/conda.exe"), + Path("C:/ProgramData/anaconda3/Scripts/conda.exe"), + Path("C:/tools/miniconda3/Scripts/conda.exe"), + ] + # Also check CONDA_PREFIX env var (set when a conda env is active) + conda_prefix = os.environ.get("CONDA_PREFIX") or os.environ.get("CONDA_EXE", "") + if conda_prefix: + candidates.insert(0, Path(conda_prefix).parent / "conda.exe") + candidates.insert(0, Path(conda_prefix)) + + for path in candidates: + if path.exists(): + return str(path) + + raise FileNotFoundError( + "conda not found. Install Miniconda from https://docs.conda.io/en/latest/miniconda.html " + "and create the aligner environment with: " + "conda create -n aligner -c conda-forge montreal-forced-aligner" + ) def ensure_dictionary_downloaded(dictionary_name: str = "english_us_arpa") -> None: @@ -10,8 +57,9 @@ def ensure_dictionary_downloaded(dictionary_name: str = "english_us_arpa") -> No Raises: AssertionError: If dictionary download fails and dictionary is not available. """ + conda = _find_conda() download_cmd = [ - "conda", + conda, "run", "-n", "aligner", @@ -23,14 +71,14 @@ def ensure_dictionary_downloaded(dictionary_name: str = "english_us_arpa") -> No ] print(f"[mfa] Ensuring dictionary '{dictionary_name}' is downloaded...") - result = subprocess.run(download_cmd, capture_output=True, text=True) + result = subprocess.run(download_cmd, capture_output=True, text=True, **_no_window()) # Check if dictionary is available (either just downloaded or already present) # MFA returns success if already downloaded, or downloads successfully if result.returncode != 0: # Try to list dictionaries to check if it's already available list_cmd = [ - "conda", + conda, "run", "-n", "aligner", @@ -39,7 +87,7 @@ def ensure_dictionary_downloaded(dictionary_name: str = "english_us_arpa") -> No "list", "dictionary", ] - list_result = subprocess.run(list_cmd, capture_output=True, text=True) + list_result = subprocess.run(list_cmd, capture_output=True, text=True, **_no_window()) assert dictionary_name in list_result.stdout, ( f"Dictionary '{dictionary_name}' is not available. " f"Download failed with: {result.stderr}" @@ -49,6 +97,39 @@ def ensure_dictionary_downloaded(dictionary_name: str = "english_us_arpa") -> No print(f"[mfa] Dictionary '{dictionary_name}' is ready.") +def _ensure_mfa_server_running() -> None: + """Start MFA's bundled Postgres server on Windows. No-op elsewhere. + + Why: MFA 3.3.x's SQLite backend has a multiprocessing race in + `collect_alignments` on Windows where the `word_interval_temp` staging + table goes missing between worker connections, aborting the run with + "no such table: word_interval_temp". The Postgres backend (also bundled + in the aligner conda env) does not have this race. macOS and Linux are + not affected by the SQLite race in practice, so this is gated to win32 + to avoid changing their behavior. + """ + if sys.platform != "win32": + return + + conda = _find_conda() + # Both calls are idempotent: `init` errors if the server dir already exists, + # `start` errors if it's already running. Either error state is the goal, + # so we ignore returncodes and only guard against true failures (timeout, + # missing conda). If the server genuinely can't come up, alignment will + # fall back to SQLite and surface its own error via the existing path. + for sub in (("server", "init"), ("server", "start")): + try: + subprocess.run( + [conda, "run", "-n", "aligner", "mfa", *sub], + capture_output=True, + text=True, + timeout=60, + **_no_window(), + ) + except (subprocess.TimeoutExpired, FileNotFoundError): + pass + + def run_mfa_align( corpus_dir, model_path, output_dir, dictionary_name="english_us_arpa", eval_dir=None ) -> None: @@ -68,9 +149,11 @@ def run_mfa_align( """ # Ensure dictionary is downloaded ensure_dictionary_downloaded(dictionary_name) + _ensure_mfa_server_running() + conda = _find_conda() cmd = [ - "conda", + conda, "run", "-n", "aligner", @@ -89,11 +172,12 @@ def run_mfa_align( try: print(f"[mfa.run_mfa_align] Running MFA align with command: {' '.join(cmd)}") - subprocess.run(cmd, check=True) + subprocess.run(cmd, check=True, capture_output=True, text=True, **_no_window()) print("[mfa.run_mfa_align] MFA alignment completed successfully.") except subprocess.CalledProcessError as e: - print(f"[mfa.run_mfa_align] MFA alignment failed with error: {e}") - raise + stderr_msg = e.stderr.strip() if e.stderr else "(no output captured)" + print(f"[mfa.run_mfa_align] MFA alignment failed:\n{stderr_msg}") + raise RuntimeError(f"MFA alignment failed (exit {e.returncode}):\n{stderr_msg}") from e def run_mfa_adapt( @@ -119,9 +203,11 @@ def run_mfa_adapt( """ # Ensure dictionary is downloaded ensure_dictionary_downloaded(dictionary_name) + _ensure_mfa_server_running() + conda = _find_conda() cmd = [ - "conda", + conda, "run", "-n", "aligner", @@ -138,7 +224,7 @@ def run_mfa_adapt( try: print(f"[mfa.run_mfa_adapt] Running MFA adapt with command: {' '.join(cmd)}") - subprocess.run(cmd, check=True, capture_output=True, text=True) + subprocess.run(cmd, check=True, capture_output=True, text=True, **_no_window()) print("[mfa.run_mfa_adapt] MFA adaptation completed successfully.") except subprocess.CalledProcessError as e: print(f"[mfa.run_mfa_adapt] MFA adaptation failed with error: {e}") @@ -158,7 +244,7 @@ def download_acoustic_model(release_path, output_file): cmd = ["curl", "-L", "-o", output_file, url] try: print(f"[mfa.download_acoustic_model] Downloading model from {url} to {output_file}") - subprocess.run(cmd, check=True) + subprocess.run(cmd, check=True, **_no_window()) print("[mfa.download_acoustic_model] Model downloaded successfully.") except subprocess.CalledProcessError as e: print(f"[mfa.download_acoustic_model] Model download failed with error: {e}") diff --git a/src/voxkit/storage/alignments.py b/src/voxkit/storage/alignments.py index 6ca093e..618ca68 100644 --- a/src/voxkit/storage/alignments.py +++ b/src/voxkit/storage/alignments.py @@ -163,15 +163,9 @@ def create_alignment( alignment_root.mkdir(parents=False, exist_ok=False) try: - tg_path = None local = dataset_metadata["cached"] - if bool(local) is False: - tg_path = Path(dataset_metadata["original_path"]) / "textgrids" - tg_path.mkdir(parents=False, exist_ok=True) - - else: - tg_path = alignment_root / "textgrids" - tg_path.mkdir(parents=False, exist_ok=True) + tg_path = alignment_root / "textgrids" + tg_path.mkdir(parents=False, exist_ok=True) metadata = AlignmentMetadata( id=now, @@ -186,7 +180,7 @@ def create_alignment( # Fetch model metadata metadata_path = alignment_root / "voxkit_alignment.json" - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=4) return True, metadata @@ -322,7 +316,7 @@ def create_hand_alignment( ) metadata_path = alignment_root / "voxkit_alignment.json" - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=4) return True, metadata @@ -357,7 +351,7 @@ def get_alignment_metadata(dataset_id: str, alignment_id: str) -> AlignmentMetad metadata_path = alignment_root / "voxkit_alignment.json" try: - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata: AlignmentMetadata = json.load(f) # Normalize status to lowercase for consistency if "status" in metadata: @@ -399,7 +393,7 @@ def update_alignment(dataset_id: str, alignment_id: str, updates: dict) -> Tuple metadata_path = alignment_root / "voxkit_alignment.json" try: - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) # Update fields @@ -410,7 +404,7 @@ def update_alignment(dataset_id: str, alignment_id: str, updates: dict) -> Tuple value = value.lower() metadata[key] = value - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=4) return True, "Alignment metadata updated successfully." @@ -447,7 +441,7 @@ def list_alignments(dataset_id: str) -> List[AlignmentMetadata]: metadata_path = dir / "voxkit_alignment.json" if metadata_path.exists(): try: - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) # Normalize status to lowercase for consistency if "status" in metadata: diff --git a/src/voxkit/storage/datasets.py b/src/voxkit/storage/datasets.py index 1e9d2bc..6180cda 100644 --- a/src/voxkit/storage/datasets.py +++ b/src/voxkit/storage/datasets.py @@ -114,7 +114,7 @@ def _get_dataset_metadata(dataset_root: Path) -> DatasetMetadata | None: metadata_path = dataset_root / "voxkit_dataset.json" if not metadata_path.exists(): return None - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: result: DatasetMetadata = json.load(f) return result except Exception: @@ -163,7 +163,7 @@ def create_dataset( - If analysis_data is provided, saves to {analysis_method}_summary.csv """ # Validate dataset structure - valid, msg = validate_dataset(Path(original_path)) + valid, msg = validate_dataset(Path(original_path), transcribed=transcribed) if not valid: return False, msg @@ -193,7 +193,7 @@ def create_dataset( alignments_dir = dataset_dir / ALIGNMENTS_ROOT alignments_dir.mkdir(parents=False, exist_ok=False) metadata_path = dataset_dir / "voxkit_dataset.json" - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=2) # Cache the dataset if requested @@ -297,7 +297,7 @@ def list_datasets_metadata() -> List[DatasetMetadata]: if entry.is_dir(): metadata_path = os.path.join(entry.path, "voxkit_dataset.json") if os.path.exists(metadata_path): - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) datasets.append(metadata) return datasets @@ -340,7 +340,7 @@ def update_dataset_metadata( # Save the updated metadata metadata_path = _get_datasets_root() / dataset_id / "voxkit_dataset.json" - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=2) return True, "Dataset metadata updated successfully" @@ -452,7 +452,7 @@ def _rewrite_imported_alignments(new_dataset_path: Path) -> None: if not metadata_file.exists(): continue try: - with open(metadata_file, "r") as f: + with open(metadata_file, "r", encoding="utf-8") as f: alignment_metadata = json.load(f) except (OSError, json.JSONDecodeError) as e: print(f"Skipping alignment metadata rewrite for '{metadata_file}': {e}") @@ -463,7 +463,7 @@ def _rewrite_imported_alignments(new_dataset_path: Path) -> None: alignment_metadata["tg_path"] = str(alignment_dir / "textgrids") try: - with open(metadata_file, "w") as f: + with open(metadata_file, "w", encoding="utf-8") as f: json.dump(alignment_metadata, f, indent=4) except OSError as e: print(f"Failed to rewrite alignment metadata '{metadata_file}': {e}") @@ -498,17 +498,18 @@ def import_dataset(dataset_path: Path) -> Tuple[bool, str]: return False, f"Dataset path '{dataset_path}' does not exist." if not dataset_path.is_dir(): return False, f"Dataset path '{dataset_path}' is not a directory." - valid, valid_msg = validate_dataset(dataset_path / "cache") + dataset_metadata_typed = _get_dataset_metadata(dataset_path) + if dataset_metadata_typed is None: + return False, "Dataset metadata file not found in the provided dataset path." + + transcribed_flag: bool = bool(dataset_metadata_typed.get("transcribed", True)) + valid, valid_msg = validate_dataset(dataset_path / "cache", transcribed=transcribed_flag) now = generate_unique_id() print(now) dataset_dest = _get_datasets_root() / now try: - dataset_metadata_typed = _get_dataset_metadata(dataset_path) - if dataset_metadata_typed is None: - return False, "Dataset metadata file not found in the provided dataset path." - # Change metadata accordingly dataset_metadata: dict[str, Any] = dict(dataset_metadata_typed) # Make a copy to modify dataset_metadata["id"] = now @@ -536,7 +537,7 @@ def import_dataset(dataset_path: Path) -> Tuple[bool, str]: shutil.copytree(dataset_path, dataset_dest, dirs_exist_ok=True) - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(dataset_metadata, f, indent=2) _rewrite_imported_alignments(dataset_dest) @@ -551,7 +552,7 @@ def import_dataset(dataset_path: Path) -> Tuple[bool, str]: return False, f"Failed to import dataset: {str(e)}" -def validate_dataset(dataset_path: Path) -> Tuple[bool, str]: +def validate_dataset(dataset_path: Path, transcribed: bool = True) -> Tuple[bool, str]: """Validate if a dataset follows the expected organization pattern. Validation checks: @@ -560,16 +561,14 @@ def validate_dataset(dataset_path: Path) -> Tuple[bool, str]: - Contains speaker subdirectories (not files at root level) - Each speaker directory is not empty - Each speaker directory contains audio files (.wav, .flac, .mp3, .ogg, .m4a) - - Each speaker directory contains label files (.lab, .txt) - - Number of audio files matches number of label files per speaker - - Each audio file has a matching label file with the same stem name + - If transcribed=True: each audio file has a matching .lab file in the same directory Expected structure: dataset_path/ ├── speaker_001/ │ ├── audio_001.wav - │ ├── audio_001.lab + │ ├── audio_001.lab (only required when transcribed=True) │ ├── audio_002.wav │ └── audio_002.lab └── speaker_002/ @@ -578,6 +577,7 @@ def validate_dataset(dataset_path: Path) -> Tuple[bool, str]: Args: dataset_path: Path to dataset root directory + transcribed: Whether to require a .lab file for every audio file Returns: Tuple of (True, validation_message) if valid or (False, error_description) if invalid @@ -617,29 +617,28 @@ def validate_dataset(dataset_path: Path) -> Tuple[bool, str]: for f in os.listdir(speaker_path) if f.endswith((".wav", ".flac", ".mp3", ".ogg", ".m4a")) ] - label_files = [f for f in os.listdir(speaker_path) if f.endswith((".lab", ".txt"))] if not audio_files: return False, f"No audio files found in speaker directory '{speaker_path}'." - if not label_files: - return False, f"No label files found in speaker directory '{speaker_path}'." - - if len(audio_files) != len(label_files): - return ( - False, - f"Mismatch between number of audio and label files in speaker " - f"directory '{speaker_path}'.", - ) + lab_files = [f for f in os.listdir(speaker_path) if f.endswith(".lab")] - audio_stems = {Path(f).stem for f in audio_files} - label_stems = {Path(f).stem for f in label_files} - unmatched = audio_stems.symmetric_difference(label_stems) - if unmatched: - return ( - False, - f"Unpaired audio/label files in speaker directory '{speaker_path}': " - f"{', '.join(sorted(unmatched))}.", - ) + if transcribed: + audio_stems = {Path(f).stem for f in audio_files} + lab_stems = {Path(f).stem for f in lab_files} + missing = audio_stems - lab_stems + if missing: + return ( + False, + f"Missing .lab files for audio files in speaker directory " + f"'{speaker_path}': {', '.join(sorted(missing))}.", + ) + else: + if lab_files: + return ( + False, + f"Dataset is marked as not transcribed but .lab files were found in " + f"'{speaker_path}'. Set 'Transcribed' to true or remove the .lab files.", + ) return True, "Dataset is valid." diff --git a/src/voxkit/storage/models.py b/src/voxkit/storage/models.py index 52e5f7b..b197e94 100644 --- a/src/voxkit/storage/models.py +++ b/src/voxkit/storage/models.py @@ -179,7 +179,7 @@ def create_model( json_metadata = {k: str(v) if isinstance(v, Path) else v for k, v in model_metadata.items()} # Create metadata file and write metadata - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(json_metadata, f, indent=4) return True, model_metadata @@ -218,7 +218,7 @@ def update_model_metadata(engine_id: str, model_id: str, updates: dict) -> Tuple metadata_path = Path(model_root) / "voxkit_model.json" try: - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) # Update fields @@ -226,7 +226,7 @@ def update_model_metadata(engine_id: str, model_id: str, updates: dict) -> Tuple if key in metadata: metadata[key] = str(value) - with open(metadata_path, "w") as f: + with open(metadata_path, "w", encoding="utf-8") as f: json.dump(metadata, f, indent=4) return True, "Model metadata updated successfully." @@ -266,7 +266,7 @@ def list_models(engine_id: str) -> list[ModelMetadata]: metadata_path = dir / "voxkit_model.json" if metadata_path.exists(): try: - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) models_found.append(metadata) except json.JSONDecodeError as e: @@ -302,7 +302,7 @@ def get_model_metadata(engine_id: str, model_id: str) -> ModelMetadata: metadata_path = Path(model_root) / "voxkit_model.json" if not metadata_path.exists(): raise FileNotFoundError(f"Metadata file not found for model '{model_id}'") - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata: ModelMetadata = json.load(f) return metadata @@ -445,7 +445,7 @@ def import_models(engine_id, new_models_root: Path) -> Tuple[bool, str]: metadata = None # Read json metadata - with open(metadata_path, "r") as f: + with open(metadata_path, "r", encoding="utf-8") as f: metadata = json.load(f) if metadata is None: @@ -460,15 +460,12 @@ def import_models(engine_id, new_models_root: Path) -> Tuple[bool, str]: if engine_id != metadata["engine_id"]: return False, f"{source_model_path.name} (engine_id mismatch)" - parts = metadata["model_path"].split(MODELS_ROOT) - if len(parts) != 2: + entrypoint_name = Path(metadata["model_path"]).name + if not entrypoint_name: return False, f"{source_model_path.name} (invalid model_path in metadata)" - # Retain the model path don't assume it's the same for all models - path_after_root = parts[1] - dest_model_entrypoint = ( - engine_models_root / MODELS_ROOT / model_id / path_after_root.split("/")[-1] + engine_models_root / MODELS_ROOT / model_id / entrypoint_name ) new_metadata = ModelMetadata( name=metadata["name"], @@ -494,7 +491,7 @@ def import_models(engine_id, new_models_root: Path) -> Tuple[bool, str]: # Overwrite metadata file with new IDs and paths new_metadata_path = dest_path / "voxkit_model.json" - with open(new_metadata_path, "w") as f: + with open(new_metadata_path, "w", encoding="utf-8") as f: json.dump(json_metadata, f, indent=4) except Exception as e: diff --git a/src/voxkit/storage/utils.py b/src/voxkit/storage/utils.py index 7cb4588..3f25184 100644 --- a/src/voxkit/storage/utils.py +++ b/src/voxkit/storage/utils.py @@ -19,13 +19,17 @@ """ import json -from datetime import datetime +import threading +from datetime import datetime, timedelta from functools import lru_cache from pathlib import Path from typing import Any from .config import STORAGE_ROOT +_id_lock = threading.Lock() +_last_id_dt: datetime | None = None + @lru_cache(maxsize=1) def get_storage_root() -> Path: @@ -52,10 +56,17 @@ def generate_unique_id(prefix: str | None = None) -> str: Returns: Unique identifier string in format: [prefix_]YYYYMMDD_HHMMSS_ffffff """ - now = datetime.now().strftime("%Y%m%d_%H%M%S_%f") + global _last_id_dt + # Windows datetime.now() has ~15ms resolution; enforce monotonic microsecond precision. + with _id_lock: + now = datetime.now() + if _last_id_dt is not None and now <= _last_id_dt: + now = _last_id_dt + timedelta(microseconds=1) + _last_id_dt = now + now_str = now.strftime("%Y%m%d_%H%M%S_%f") if prefix: - return f"{prefix}_{now}" - return now + return f"{prefix}_{now_str}" + return now_str def readable_from_unique_id(date_str: str) -> str: @@ -110,5 +121,5 @@ def save_json(file_path: Path, data: dict[str, Any]) -> None: data: Dictionary to serialize as JSON """ file_path.parent.mkdir(parents=True, exist_ok=True) - with open(file_path, "w") as f: + with open(file_path, "w", encoding="utf-8") as f: json.dump(data, f, indent=4) diff --git a/tasks.py b/tasks.py index f132e3c..b16ec3b 100644 --- a/tasks.py +++ b/tasks.py @@ -1,8 +1,10 @@ """Invoke task definitions for VoxKit. Run with `invoke ` or `invoke --list`.""" import shutil +import stat import subprocess import sys +import urllib.request from pathlib import Path from invoke import task @@ -88,16 +90,106 @@ def clean(c): _log("Cleanup completed", GREEN) -@task(pre=[clean]) -def build(c): - """Build standalone executable for current platform.""" - _log("Building VoxKit...") +@task(pre=[clean], name="macos-build") +def macos_build(c): + """Build standalone executable for macOS.""" + _log("Building VoxKit for macOS...") c.run( "uv run --group installation python scripts/build.py build " "--entry main.py --name VoxKit --icon ./assets/voxkit.icns --windowed" ) +@task(pre=[clean], name="linux-build") +def linux_build(c): + """Build AppImage for Linux.""" + tools_dir = ROOT / "tools" + tools_dir.mkdir(exist_ok=True) + + # Step 1: PyInstaller one-dir build + _log("Building VoxKit for Linux (one-dir)...") + c.run( + "uv run --group installation python scripts/build.py build " + "--entry main.py --name VoxKit --icon ./assets/voxkit.png --windowed --no-onefile" + ) + + dist_dir = ROOT / "dist" / "VoxKit" + if not dist_dir.exists(): + _log(f"PyInstaller output not found at {dist_dir}", RED) + return + + # Step 2: Bundle libxcb-cursor — Qt's xcb platform plugin loads it via dlopen + # at runtime so PyInstaller's ldd scan misses it. We copy it next to libqxcb.so + # so LD_LIBRARY_PATH (set by PyInstaller's bootloader) picks it up. + _log("Bundling libxcb-cursor...") + xcb_cursor_candidates = [ + Path("/usr/lib/x86_64-linux-gnu/libxcb-cursor.so.0"), + Path("/usr/lib/libxcb-cursor.so.0"), + ] + xcb_cursor_src = next((p for p in xcb_cursor_candidates if p.exists()), None) + if xcb_cursor_src is None: + result = subprocess.run( # noqa: S603 + ["find", "/usr", "-name", "libxcb-cursor.so.0"], # noqa: S607 + capture_output=True, + text=True, + check=False, + ) + found = result.stdout.strip().splitlines() + xcb_cursor_src = Path(found[0]) if found else None + + if xcb_cursor_src is None: + _log("libxcb-cursor.so.0 not found — run: sudo apt-get install libxcb-cursor0", RED) + return + + internal_dir = dist_dir / "_internal" + dest_dir = internal_dir if internal_dir.is_dir() else dist_dir + shutil.copy2(xcb_cursor_src, dest_dir / "libxcb-cursor.so.0") + _log(f"Bundled {xcb_cursor_src} -> {dest_dir / 'libxcb-cursor.so.0'}", GREEN) + + # Step 3: Set up AppDir + _log("Setting up AppDir...") + appdir = ROOT / "build" / "AppDir" + _rmtree(appdir) + appdir.mkdir(parents=True) + + shutil.copytree(dist_dir, appdir / "VoxKit") + + apprun = appdir / "AppRun" + apprun.write_text( + "#!/bin/bash\n" + 'APPDIR="$(dirname "$(readlink -f "$0")")"\n' + 'exec "$APPDIR/VoxKit/VoxKit" "$@"\n' + ) + apprun.chmod(apprun.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) + + shutil.copy2(ROOT / "installer" / "linux" / "VoxKit.desktop", appdir / "VoxKit.desktop") + shutil.copy2(ROOT / "assets" / "voxkit.png", appdir / "voxkit.png") + + # Step 4: Download appimagetool if needed + appimagetool = tools_dir / "appimagetool-x86_64.AppImage" + if not appimagetool.exists(): + _log("Downloading appimagetool...") + url = "https://github.com/AppImage/appimagetool/releases/download/continuous/appimagetool-x86_64.AppImage" + urllib.request.urlretrieve(url, appimagetool) # noqa: S310 + appimagetool.chmod(appimagetool.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) + + # Step 5: Package AppImage + _log("Packaging AppImage...") + output = ROOT / "dist" / "VoxKit-x86_64.AppImage" + c.run(f"ARCH=x86_64 {appimagetool} {appdir} {output}") + _log(f"AppImage created: {output}", GREEN) + + +@task(pre=[clean], name="windows-build") +def windows_build(c): + """Build standalone executable for Windows.""" + _log("Building VoxKit for Windows...") + c.run( + "uv run --group installation python scripts/build.py build " + "--entry main.py --name VoxKit --icon ./assets/voxkit.ico --windowed" + ) + + @task def format(c): """Format code with Ruff.""" diff --git a/tests/storage/test_datasets.py b/tests/storage/test_datasets.py index ddc85a3..e188f81 100644 --- a/tests/storage/test_datasets.py +++ b/tests/storage/test_datasets.py @@ -30,6 +30,15 @@ def generate_fake_datasets(): wav_file.touch() lab_file.touch() + # Create a valid untranscribed dataset (wav only, no .lab files) + dataset_path = mock_get_storage_root() / "fake_datasets" / "valid_untranscribed" + dataset_path.mkdir(parents=True, exist_ok=True) + for participant in participant_names: + wavlab_path = dataset_path / participant + wavlab_path.mkdir(parents=True, exist_ok=True) + for i in range(5): + (wavlab_path / f"sample_{i}.wav").touch() + # Create an invalid dataset path dataset_path = mock_get_storage_root() / "fake_datasets" / "invalid" dataset_path.mkdir(parents=True, exist_ok=True) @@ -60,6 +69,7 @@ def manage_test_environment(): valid_dataset_path = mock_get_storage_root() / "fake_datasets" / "valid" +valid_untranscribed_dataset_path = mock_get_storage_root() / "fake_datasets" / "valid_untranscribed" invalid_dataset_path = mock_get_storage_root() / "fake_datasets" / "invalid" empty_dataset_path = mock_get_storage_root() / "fake_datasets" / "empty" @@ -111,7 +121,7 @@ def test_create_dataset_success_with_cache(self, monkeypatch): success, message = create_dataset( name="test_dataset_cached", description="A test dataset with caching", - original_path=valid_dataset_path, + original_path=valid_untranscribed_dataset_path, cached=True, anonymize=True, transcribed=False, @@ -144,7 +154,7 @@ def test_create_dataset_invalid_path(self, monkeypatch): assert success is False assert isinstance(message, str) - assert "No label files found" in message + assert "Missing .lab files" in message assert invalid_dataset_path.exists() is True class TestListDatasets: @@ -166,7 +176,7 @@ def test_list_datasets_metadata(self, monkeypatch): create_dataset( name="dataset_two", description="Second test dataset", - original_path=valid_dataset_path, + original_path=valid_untranscribed_dataset_path, cached=True, anonymize=True, transcribed=False, @@ -596,7 +606,7 @@ def test_update_dataset_metadata_success(self, monkeypatch): success, message = create_dataset( name="dataset_update_test", description="Original description", - original_path=valid_dataset_path, + original_path=valid_untranscribed_dataset_path, cached=False, anonymize=False, transcribed=False, @@ -863,7 +873,7 @@ def test_validate_dataset_missing_label_files(self, monkeypatch): is_valid, msg = validate_dataset(invalid_dataset_path) assert is_valid is False - assert "No label files" in msg + assert "Missing .lab files" in msg def test_validate_dataset_mismatched_counts(self, monkeypatch): from voxkit.storage.datasets import validate_dataset @@ -880,7 +890,8 @@ def test_validate_dataset_mismatched_counts(self, monkeypatch): is_valid, msg = validate_dataset(mismatched_path) assert is_valid is False - assert "Mismatch" in msg + assert "Missing .lab files" in msg + assert "sample_2" in msg def test_validate_dataset_unpaired_stems(self, monkeypatch): from voxkit.storage.datasets import validate_dataset @@ -896,4 +907,19 @@ def test_validate_dataset_unpaired_stems(self, monkeypatch): is_valid, msg = validate_dataset(unpaired_path) assert is_valid is False - assert "Unpaired" in msg + assert "Missing .lab files" in msg + assert "recording_A" in msg + + def test_validate_dataset_not_transcribed_but_has_lab_files(self, monkeypatch): + from voxkit.storage.datasets import validate_dataset + + lab_present_path = mock_get_storage_root() / "fake_datasets" / "lab_but_not_transcribed" + speaker_path = lab_present_path / "speaker_1" + speaker_path.mkdir(parents=True, exist_ok=True) + (speaker_path / "sample_1.wav").touch() + (speaker_path / "sample_1.lab").touch() + + is_valid, msg = validate_dataset(lab_present_path, transcribed=False) + + assert is_valid is False + assert "not transcribed" in msg diff --git a/tests/storage/test_models.py b/tests/storage/test_models.py index 8cb7846..95f325b 100644 --- a/tests/storage/test_models.py +++ b/tests/storage/test_models.py @@ -670,6 +670,51 @@ def test_import_models_success(self, monkeypatch): assert success is True assert "imported successfully" in msg + def test_import_models_paths_rewritten(self, monkeypatch): + import json + + from voxkit.storage import models + from voxkit.storage.config import MODELS_ROOT + from voxkit.storage.models import import_models, list_models + + monkeypatch.setattr(models, "get_storage_root", mock_get_storage_root) + + engine_id = ENGINE_IDS[0] + import_source = mock_get_storage_root() / "import_source_paths" + model_dir = import_source / "model_to_import" + model_dir.mkdir(parents=True, exist_ok=True) + (model_dir / "entrypoint.model").mkdir(parents=True, exist_ok=True) + + old_base = "/old/machine/path" + metadata = { + "name": "path_check_model", + "engine_id": engine_id, + "model_path": f"{old_base}/{engine_id}/{MODELS_ROOT}/old_id/entrypoint.model", + "data_path": f"{old_base}/data", + "eval_path": f"{old_base}/eval", + "train_path": f"{old_base}/train", + "download_date": "January 01, 2024 at 12:00:00 PM", + "id": "old_id", + } + with open(model_dir / "voxkit_model.json", "w") as f: + json.dump(metadata, f, indent=4) + + success, _ = import_models(engine_id=engine_id, new_models_root=import_source) + assert success is True + + imported = list_models(engine_id) + assert len(imported) == 1 + imported_meta = imported[0] + + storage_root = str(mock_get_storage_root()) + assert old_base not in imported_meta["model_path"] + assert old_base not in imported_meta["data_path"] + assert old_base not in imported_meta["eval_path"] + assert old_base not in imported_meta["train_path"] + assert storage_root in imported_meta["model_path"] + assert "old_id" not in imported_meta["id"] + assert imported_meta["name"] == "path_check_model" + def test_import_models_missing_metadata(self, monkeypatch): from voxkit.storage import models from voxkit.storage.models import import_models diff --git a/uv.lock b/uv.lock index 993ae89..5456aa0 100644 --- a/uv.lock +++ b/uv.lock @@ -2,21 +2,10 @@ version = 1 revision = 3 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.15' and sys_platform == 'win32'", - "python_full_version >= '3.15' and sys_platform == 'emscripten'", - "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.14.*' and sys_platform == 'win32'", - "python_full_version == '3.14.*' and sys_platform == 'emscripten'", - "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and sys_platform == 'win32'", - "python_full_version < '3.12' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.15'", + "python_full_version >= '3.13' and python_full_version < '3.15'", + "python_full_version == '3.12.*'", + "python_full_version < '3.12'", ] [[package]] @@ -163,17 +152,13 @@ wheels = [ [[package]] name = "alignment-comparison-plots" -version = "0.1.1" -source = { registry = "https://pypi.org/simple" } +version = "0.1.2.dev2+gb44799d30" +source = { git = "https://github.com/WISCLab/alignment-comparison-plots?branch=voxkit-windows-variant#b44799d3089b7631e3a87604f046966a2090582b" } dependencies = [ { name = "matplotlib" }, { name = "praat-textgrids" }, { name = "pyqt6" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b9/4c/3b8e6c262abf4d7b78b44c04d30ac058a3fe8f60685e8ebdbbd538b5f08d/alignment_comparison_plots-0.1.1.tar.gz", hash = "sha256:d5aa66044fa7d9e2871f8aa14205a60d32918766cd81248098318b69e542ca40", size = 536340, upload-time = "2026-03-11T06:16:19.583Z" } -wheels = [ - { url = 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