Release v1.8.0: Add transcript-fixer skill
## New Skill: transcript-fixer v1.0.0 Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning. **Features:** - Two-stage correction pipeline (dictionary + AI) - Automatic pattern detection and learning - Domain-specific dictionaries (general, embodied_ai, finance, medical) - SQLite-based correction repository - Team collaboration with import/export - GLM API integration for AI corrections - Cost optimization through dictionary promotion **Use cases:** - Correcting meeting notes, lecture recordings, or interview transcripts - Fixing Chinese/English homophone errors and technical terminology - Building domain-specific correction dictionaries - Improving transcript accuracy through iterative learning **Documentation:** - Complete workflow guides in references/ - SQL query templates - Troubleshooting guide - Team collaboration patterns - API setup instructions **Marketplace updates:** - Updated marketplace to v1.8.0 - Added transcript-fixer plugin (category: productivity) - Updated README.md with skill description and use cases - Updated CLAUDE.md with skill listing and counts 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
daymade
181
transcript-fixer/scripts/cli/commands.py
Normal file
181
transcript-fixer/scripts/cli/commands.py
Normal file
@@ -0,0 +1,181 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
CLI Commands - Command Handler Functions
|
||||
|
||||
SINGLE RESPONSIBILITY: Handle CLI command execution
|
||||
|
||||
All cmd_* functions take parsed args and execute the requested operation.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from core import (
|
||||
CorrectionRepository,
|
||||
CorrectionService,
|
||||
DictionaryProcessor,
|
||||
AIProcessor,
|
||||
LearningEngine,
|
||||
)
|
||||
from utils import validate_configuration, print_validation_summary
|
||||
|
||||
|
||||
def _get_service():
|
||||
"""Get configured CorrectionService instance."""
|
||||
config_dir = Path.home() / ".transcript-fixer"
|
||||
db_path = config_dir / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
return CorrectionService(repository)
|
||||
|
||||
|
||||
def cmd_init(args):
|
||||
"""Initialize ~/.transcript-fixer/ directory"""
|
||||
service = _get_service()
|
||||
service.initialize()
|
||||
|
||||
|
||||
def cmd_add_correction(args):
|
||||
"""Add a single correction"""
|
||||
service = _get_service()
|
||||
try:
|
||||
service.add_correction(args.from_text, args.to_text, args.domain)
|
||||
print(f"✅ Added: '{args.from_text}' → '{args.to_text}' (domain: {args.domain})")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def cmd_list_corrections(args):
|
||||
"""List all corrections"""
|
||||
service = _get_service()
|
||||
corrections = service.get_corrections(args.domain)
|
||||
|
||||
print(f"\n📋 Corrections (domain: {args.domain})")
|
||||
print("=" * 60)
|
||||
for wrong, correct in sorted(corrections.items()):
|
||||
print(f" '{wrong}' → '{correct}'")
|
||||
print(f"\nTotal: {len(corrections)} corrections\n")
|
||||
|
||||
|
||||
def cmd_run_correction(args):
|
||||
"""Run the correction workflow"""
|
||||
# Validate input file
|
||||
input_path = Path(args.input)
|
||||
if not input_path.exists():
|
||||
print(f"❌ Error: File not found: {input_path}")
|
||||
sys.exit(1)
|
||||
|
||||
# Setup output directory
|
||||
output_dir = Path(args.output) if args.output else input_path.parent
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Initialize service
|
||||
service = _get_service()
|
||||
|
||||
# Load corrections and rules
|
||||
corrections = service.get_corrections(args.domain)
|
||||
context_rules = service.load_context_rules()
|
||||
|
||||
# Read input file
|
||||
print(f"📖 Reading: {input_path.name}")
|
||||
with open(input_path, 'r', encoding='utf-8') as f:
|
||||
original_text = f.read()
|
||||
print(f" File size: {len(original_text):,} characters\n")
|
||||
|
||||
# Stage 1: Dictionary corrections
|
||||
stage1_changes = []
|
||||
stage1_text = original_text
|
||||
if args.stage >= 1:
|
||||
print("=" * 60)
|
||||
print("🔧 Stage 1: Dictionary Corrections")
|
||||
print("=" * 60)
|
||||
|
||||
processor = DictionaryProcessor(corrections, context_rules)
|
||||
stage1_text, stage1_changes = processor.process(original_text)
|
||||
|
||||
summary = processor.get_summary(stage1_changes)
|
||||
print(f"✓ Applied {summary['total_changes']} corrections")
|
||||
print(f" - Dictionary: {summary['dictionary_changes']}")
|
||||
print(f" - Context rules: {summary['context_rule_changes']}")
|
||||
|
||||
stage1_file = output_dir / f"{input_path.stem}_stage1.md"
|
||||
with open(stage1_file, 'w', encoding='utf-8') as f:
|
||||
f.write(stage1_text)
|
||||
print(f"💾 Saved: {stage1_file.name}\n")
|
||||
|
||||
# Stage 2: AI corrections
|
||||
stage2_changes = []
|
||||
stage2_text = stage1_text
|
||||
if args.stage >= 2:
|
||||
print("=" * 60)
|
||||
print("🤖 Stage 2: AI Corrections")
|
||||
print("=" * 60)
|
||||
|
||||
# Check API key
|
||||
api_key = os.environ.get("GLM_API_KEY")
|
||||
if not api_key:
|
||||
print("❌ Error: GLM_API_KEY environment variable not set")
|
||||
print(" Set it with: export GLM_API_KEY='your-key'")
|
||||
sys.exit(1)
|
||||
|
||||
ai_processor = AIProcessor(api_key)
|
||||
stage2_text, stage2_changes = ai_processor.process(stage1_text)
|
||||
|
||||
print(f"✓ Processed {len(stage2_changes)} chunks\n")
|
||||
|
||||
stage2_file = output_dir / f"{input_path.stem}_stage2.md"
|
||||
with open(stage2_file, 'w', encoding='utf-8') as f:
|
||||
f.write(stage2_text)
|
||||
print(f"💾 Saved: {stage2_file.name}\n")
|
||||
|
||||
# Save history for learning
|
||||
service.save_history(
|
||||
filename=str(input_path),
|
||||
domain=args.domain,
|
||||
original_length=len(original_text),
|
||||
stage1_changes=len(stage1_changes),
|
||||
stage2_changes=len(stage2_changes),
|
||||
model="GLM-4.6",
|
||||
changes=stage1_changes + stage2_changes
|
||||
)
|
||||
|
||||
# TODO: Run learning engine
|
||||
# learning = LearningEngine(...)
|
||||
# suggestions = learning.analyze_and_suggest()
|
||||
# if suggestions:
|
||||
# print(f"🎓 Learning: Found {len(suggestions)} new correction suggestions")
|
||||
# print(f" Run --review-learned to review them\n")
|
||||
|
||||
# Stage 3: Generate diff report
|
||||
if args.stage >= 3:
|
||||
print("=" * 60)
|
||||
print("📊 Stage 3: Generating Diff Report")
|
||||
print("=" * 60)
|
||||
print(" Use diff_generator.py to create visual comparison\n")
|
||||
|
||||
print("✅ Correction complete!")
|
||||
|
||||
|
||||
def cmd_review_learned(args):
|
||||
"""Review learned suggestions"""
|
||||
# TODO: Implement learning engine with SQLite backend
|
||||
print("⚠️ Learning engine not yet implemented with SQLite backend")
|
||||
print(" This feature will be added in a future update")
|
||||
|
||||
|
||||
def cmd_approve(args):
|
||||
"""Approve a learned suggestion"""
|
||||
# TODO: Implement learning engine with SQLite backend
|
||||
print("⚠️ Learning engine not yet implemented with SQLite backend")
|
||||
print(" This feature will be added in a future update")
|
||||
|
||||
|
||||
def cmd_validate(args):
|
||||
"""Validate configuration and JSON files"""
|
||||
errors, warnings = validate_configuration()
|
||||
exit_code = print_validation_summary(errors, warnings)
|
||||
if exit_code != 0:
|
||||
sys.exit(exit_code)
|
||||
Reference in New Issue
Block a user