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fix: Improve Whisper model status verification and PyInstaller builds

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- Add robust model cache verification (check model.bin + config.json)
- Add new status messages: model_cached, incomplete_cache, model_error
- Forward model status events to frontend for better UI feedback
- Add clean_build_cache() to remove stale spec files before build
- Add --clean flag to PyInstaller commands
- Change sidecar from --onefile to --onedir for faster startup
- Add missing hidden imports: onnxruntime, wave, huggingface_hub.utils

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
egg
2025-12-17 20:33:59 +08:00
parent 012cdaf5f3
commit d75789f23e
5 changed files with 136 additions and 15 deletions
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47
sidecar/build.py
View File

@@ -1,29 +1,66 @@
#!/usr/bin/env python3
"""
Build script for creating standalone transcriber executable using PyInstaller.
Uses --onedir mode for faster startup compared to --onefile.
"""
import subprocess
import sys
import os
import shutil
def clean_build_cache(script_dir):
"""Clean old build artifacts that may cause stale spec file issues."""
dirs_to_clean = [
os.path.join(script_dir, "build"),
os.path.join(script_dir, "__pycache__"),
]
files_to_clean = [
os.path.join(script_dir, "build", "transcriber.spec"),
]
for f in files_to_clean:
if os.path.exists(f):
print(f"Removing old spec file: {f}")
os.remove(f)
for d in dirs_to_clean:
pycache = os.path.join(d)
if os.path.exists(pycache) and "__pycache__" in pycache:
print(f"Removing cache: {pycache}")
shutil.rmtree(pycache)
def build():
"""Build the transcriber executable."""
# PyInstaller command
script_dir = os.path.dirname(os.path.abspath(__file__))
# Clean old build cache to avoid stale spec file issues
clean_build_cache(script_dir)
# PyInstaller command with --onedir for faster startup
cmd = [
sys.executable, "-m", "PyInstaller",
"--onefile",
"--onedir",
"--clean", # Clean PyInstaller cache before building
"--name", "transcriber",
"--distpath", "dist",
"--workpath", "build",
"--specpath", "build",
# Core dependencies
"--hidden-import", "faster_whisper",
"--hidden-import", "opencc",
"--hidden-import", "numpy",
"--hidden-import", "ctranslate2",
"--hidden-import", "huggingface_hub",
"--hidden-import", "huggingface_hub.utils",
"--hidden-import", "tokenizers",
# ONNX Runtime for VAD
"--hidden-import", "onnxruntime",
# Audio processing
"--hidden-import", "wave",
# Collect data files
"--collect-data", "faster_whisper",
"--collect-data", "opencc",
"transcriber.py"
@@ -32,10 +69,12 @@ def build():
print("Building transcriber executable...")
print(f"Command: {' '.join(cmd)}")
result = subprocess.run(cmd, cwd=os.path.dirname(os.path.abspath(__file__)))
result = subprocess.run(cmd, cwd=script_dir)
if result.returncode == 0:
print("\nBuild successful! Executable created at: dist/transcriber")
print("\nBuild successful!")
print("Executable created at: dist/transcriber/transcriber.exe (Windows) or dist/transcriber/transcriber (Linux)")
print("\nNote: The Whisper model will be downloaded on first run if not cached.")
else:
print("\nBuild failed!")
sys.exit(1)

42
sidecar/transcriber.py
View File

@@ -60,12 +60,31 @@ def check_and_download_whisper_model(model_size: str) -> bool:
repo_cache_name = f"models--Systran--faster-whisper-{model_size}"
model_cache_path = cache_dir / repo_cache_name
# Check if model files exist
# Check if model files exist - verify essential files are present
if model_cache_path.exists():
snapshots_dir = model_cache_path / "snapshots"
if snapshots_dir.exists() and any(snapshots_dir.iterdir()):
# Model is cached, no download needed
return True
if snapshots_dir.exists():
# Check for actual model files, not just any file
for snapshot in snapshots_dir.iterdir():
if snapshot.is_dir():
# Essential faster-whisper model files
required_files = ["model.bin", "config.json"]
has_all_files = all(
(snapshot / f).exists() for f in required_files
)
if has_all_files:
print(json.dumps({
"status": "model_cached",
"model": model_size,
"path": str(snapshot)
}), flush=True)
return True
# Snapshots exist but no valid model found
print(json.dumps({
"status": "incomplete_cache",
"model": model_size,
"message": "Model cache incomplete, will re-download"
}), flush=True)
# Model not cached, need to download
print(json.dumps({
@@ -491,20 +510,29 @@ class Transcriber:
try:
# Check if model needs to be downloaded (with progress reporting)
check_and_download_whisper_model(model_size)
download_ok = check_and_download_whisper_model(model_size)
if not download_ok:
print(json.dumps({
"status": "model_error",
"error": "Failed to download model"
}), flush=True)
raise RuntimeError("Failed to download Whisper model")
# Now load the model
print(json.dumps({"status": "loading_model", "model": model_size}), flush=True)
self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
self.converter = opencc.OpenCC("s2twp")
print(json.dumps({"status": "model_loaded"}), flush=True)
print(json.dumps({"status": "model_loaded", "model": model_size}), flush=True)
# Pre-load VAD model at startup (not when streaming starts)
if ONNX_AVAILABLE:
self.vad_model = SileroVAD()
except Exception as e:
print(json.dumps({"error": f"Failed to load model: {e}"}), flush=True)
print(json.dumps({
"status": "model_error",
"error": f"Failed to load model: {e}"
}), flush=True)
raise
def transcribe_file(self, audio_path: str, add_punctuation: bool = False) -> str: