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feat(transcript-fixer): native AI correction as default mode (v1.3.0)

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Claude IS the AI — when running inside Claude Code, use Claude's own
language understanding for Stage 2 corrections instead of calling an
external API. No API key needed by default.

New capabilities in native mode:
- Intelligent paragraph breaks at logical topic transitions
- Filler word reduction (excessive repetition removal)
- Interactive review with confidence-level tables
- Context-aware judgment using full document context

API mode (GLM) remains available for batch/automation use cases.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
daymade
2026-03-22 23:40:38 +08:00
parent a496c91cae
commit 639a6d303e
2 changed files with 93 additions and 40 deletions
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transcript-fixer/SKILL.md
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@@ -29,20 +29,36 @@ powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | ie
## Quick Start
**Recommended: Use Enhanced Wrapper** (auto-detects API key, opens HTML diff):
**Default: Native AI Correction (no API key needed)**
When invoked from Claude Code, the skill uses a two-phase approach:
1. **Dictionary phase** (script): Apply 700+ learned correction rules instantly
2. **AI phase** (Claude native): Claude reads the text directly and fixes ASR errors, adds paragraph breaks, removes filler words
```bash
# First time: Initialize database
uv run scripts/fix_transcription.py --init
# Process transcript with enhanced UX
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected
# Phase 1: Dictionary corrections (instant, free)
uv run scripts/fix_transcription.py --input meeting.md --stage 1
```
The enhanced wrapper automatically:
- Detects GLM API key from shell configs (checks lines near `ANTHROPIC_BASE_URL`)
- Moves output files to specified directory
- Opens HTML visual diff in browser for immediate feedback
After Stage 1, Claude should:
1. Read the Stage 1 output in ~3000-char chunks
2. Identify ASR errors (homophones, technical terms, broken sentences)
3. Present corrections in a table for user review (high/medium confidence)
4. Apply confirmed corrections and save stable patterns to dictionary
5. Optionally: add paragraph breaks and remove excessive filler words
**Alternative: API-Based Batch Processing** (for automation or large volumes):
```bash
# Set API key for automated AI corrections
export GLM_API_KEY="<api-key>" # From https://open.bigmodel.cn/
# Run full pipeline (dict + API AI + diff report)
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected
```
**Timestamp repair**:
```bash
@@ -58,25 +74,9 @@ uv run scripts/split_transcript_sections.py meeting.txt \
--rebase-to-zero
```
**Alternative: Use Core Script Directly**:
```bash
# 1. Set API key (if not auto-detected)
export GLM_API_KEY="<api-key>" # From https://open.bigmodel.cn/
# 2. Add common corrections (5-10 terms)
uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
# 3. Run full correction pipeline
uv run scripts/fix_transcription.py --input meeting.md --stage 3
# 4. Review learned patterns after 3-5 runs
uv run scripts/fix_transcription.py --review-learned
```
**Output files**:
- `*_stage1.md` - Dictionary corrections applied
- `*_stage2.md` - AI corrections applied (final version)
- `*_corrected.txt` - Final version (native mode) or `*_stage2.md` (API mode)
- `*_对比.html` - Visual diff (open in browser for best experience)
**Generate word-level diff** (recommended for reviewing corrections):
@@ -116,14 +116,15 @@ This creates an HTML file showing word-by-word differences with clear highlighti
## Core Workflow
Three-stage pipeline stores corrections in `~/.transcript-fixer/corrections.db`:
Two-phase pipeline stores corrections in `~/.transcript-fixer/corrections.db`:
1. **Initialize** (first time): `uv run scripts/fix_transcription.py --init`
2. **Add domain corrections**: `--add "错误词" "正确词" --domain <domain>`
3. **Process transcript**: `--input file.md --stage 3`
4. **Review learned patterns**: `--review-learned` and `--approve` high-confidence suggestions
3. **Phase 1 — Dictionary**: `--input file.md --stage 1` (instant, free)
4. **Phase 2 — AI Correction**: Claude reads output and fixes ASR errors natively (default), or use `--stage 3` with `GLM_API_KEY` for API mode
5. **Save stable patterns**: `--add "错误词" "正确词"` after each fix session
6. **Review learned patterns**: `--review-learned` and `--approve` high-confidence suggestions
**Stages**: Dictionary (instant, free) → AI via GLM API (parallel) → Full pipeline
**Domains**: `general`, `embodied_ai`, `finance`, `medical`, or custom names including Chinese (e.g., `火星加速器`, `具身智能`)
**Learning**: Patterns appearing ≥3 times at ≥80% confidence move from AI to dictionary
@@ -182,14 +183,45 @@ If you understand the risks and still want to add a flagged rule:
uv run scripts/fix_transcription.py --add "仿佛" "反复" --domain general --force
```
## AI Fallback Strategy
## Native AI Correction (Default Mode)
When GLM API is unavailable (503, network issues), the script outputs `[CLAUDE_FALLBACK]` marker.
**Claude IS the AI.** When running inside Claude Code, use Claude's own language understanding for Stage 2 corrections instead of calling an external API. This is the default behavior — no API key needed.
Claude Code should then:
1. Analyze the text directly for ASR errors
2. Fix using Edit tool
3. **MUST save corrections to dictionary** with `--add`
### Workflow
1. **Run Stage 1** (dictionary): `uv run scripts/fix_transcription.py --input file.md --stage 1`
2. **Read the text** in ~3000-character chunks (use `cut -c<start>-<end>` for single-line files)
3. **Identify ASR errors** — look for:
- Homophone errors (同音字): "上海文" → "上下文", "扩种" → "扩充"
- Broken sentence boundaries: "很大程。路上" → "很大程度上"
- Technical terms: "Web coding" → "Vibe Coding"
- Missing/extra characters: "沉沉默" → "沉默"
4. **Present corrections** in a table with confidence levels before applying:
- High confidence: clear ASR errors with unambiguous corrections
- Medium confidence: context-dependent, need user confirmation
5. **Apply corrections** to a copy of the file (never modify the original)
6. **Save stable patterns** to dictionary: `--add "错误词" "正确词" --domain general`
7. **Generate word diff**: `uv run scripts/generate_word_diff.py original.md corrected.md diff.html`
### Enhanced AI Capabilities (Native Mode Only)
Native mode can do things the API mode cannot:
- **Intelligent paragraph breaks**: Add `\n\n` at logical topic transitions in continuous text
- **Filler word reduction**: Remove excessive repetition (这个这个这个 → 这个, 都都都都 → 都)
- **Interactive review**: Present corrections for user confirmation before applying
- **Context-aware judgment**: Use full document context to resolve ambiguous errors
### When to Use API Mode Instead
Use `GLM_API_KEY` + Stage 3 for:
- Batch processing multiple files in automation
- When Claude Code is not available (standalone script usage)
- Consistent reproducible processing without interactive review
### Legacy Fallback Marker
When the script outputs `[CLAUDE_FALLBACK]` (GLM API error), switch to native mode automatically.
## Database Operations
@@ -209,8 +241,8 @@ sqlite3 ~/.transcript-fixer/corrections.db "SELECT value FROM system_config WHER
| Stage | Description | Speed | Cost |
|-------|-------------|-------|------|
| 1 | Dictionary only | Instant | Free |
| 2 | AI only | ~10s | API calls |
| 3 | Full pipeline | ~10s | API calls |
| 1 + Native | Dictionary + Claude AI (default) | ~1min | Free |
| 3 | Dictionary + API AI + diff report | ~10s | API calls |
## Bundled Resources