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>
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daymade
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transcript-fixer/scripts/core/ai_processor.py
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transcript-fixer/scripts/core/ai_processor.py
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#!/usr/bin/env python3
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"""
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AI Processor - Stage 2: AI-powered Text Corrections
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SINGLE RESPONSIBILITY: Process text using GLM API for intelligent corrections
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Features:
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- Split text into chunks for API processing
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- Call GLM-4.6 for context-aware corrections
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- Track AI-suggested changes
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- Handle API errors gracefully
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"""
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from __future__ import annotations
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import os
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import re
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from typing import List, Tuple
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from dataclasses import dataclass
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import httpx
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@dataclass
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class AIChange:
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"""Represents an AI-suggested change"""
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chunk_index: int
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from_text: str
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to_text: str
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confidence: float # 0.0 to 1.0
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class AIProcessor:
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"""
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Stage 2 Processor: AI-powered corrections using GLM-4.6
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Process:
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1. Split text into chunks (respecting API limits)
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2. Send each chunk to GLM API
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3. Track changes for learning engine
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4. Preserve formatting and structure
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"""
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def __init__(self, api_key: str, model: str = "GLM-4.6",
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base_url: str = "https://open.bigmodel.cn/api/anthropic",
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fallback_model: str = "GLM-4.5-Air"):
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"""
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Initialize AI processor
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Args:
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api_key: GLM API key
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model: Model name (default: GLM-4.6)
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base_url: API base URL
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fallback_model: Fallback model on primary failure
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"""
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self.api_key = api_key
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self.model = model
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self.fallback_model = fallback_model
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self.base_url = base_url
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self.max_chunk_size = 6000 # Characters per chunk
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def process(self, text: str, context: str = "") -> Tuple[str, List[AIChange]]:
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"""
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Process text with AI corrections
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Args:
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text: Text to correct
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context: Optional domain/meeting context
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Returns:
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(corrected_text, list_of_changes)
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"""
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chunks = self._split_into_chunks(text)
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corrected_chunks = []
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all_changes = []
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print(f"📝 Processing {len(chunks)} chunks with {self.model}...")
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for i, chunk in enumerate(chunks, 1):
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print(f" Chunk {i}/{len(chunks)}... ", end="", flush=True)
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try:
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corrected_chunk = self._process_chunk(chunk, context, self.model)
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corrected_chunks.append(corrected_chunk)
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# TODO: Extract actual changes for learning
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# For now, we assume the whole chunk changed
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if corrected_chunk != chunk:
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all_changes.append(AIChange(
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chunk_index=i,
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from_text=chunk[:50] + "...",
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to_text=corrected_chunk[:50] + "...",
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confidence=0.9 # Placeholder
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))
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print("✓")
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except Exception as e:
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print(f"✗ {str(e)[:50]}")
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# Retry with fallback model
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if self.fallback_model and self.fallback_model != self.model:
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print(f" Retrying with {self.fallback_model}... ", end="", flush=True)
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try:
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corrected_chunk = self._process_chunk(chunk, context, self.fallback_model)
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corrected_chunks.append(corrected_chunk)
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print("✓")
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continue
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except Exception as e2:
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print(f"✗ {str(e2)[:50]}")
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print(" Using original text...")
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corrected_chunks.append(chunk)
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return "\n\n".join(corrected_chunks), all_changes
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def _split_into_chunks(self, text: str) -> List[str]:
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"""
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Split text into processable chunks
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Strategy:
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- Split by double newlines (paragraphs)
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- Keep chunks under max_chunk_size
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- Don't split mid-paragraph if possible
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"""
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paragraphs = text.split('\n\n')
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chunks = []
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current_chunk = []
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current_length = 0
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for para in paragraphs:
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para_length = len(para)
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# If single paragraph exceeds limit, force split
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if para_length > self.max_chunk_size:
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if current_chunk:
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chunks.append('\n\n'.join(current_chunk))
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current_chunk = []
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current_length = 0
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# Split long paragraph by sentences
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sentences = re.split(r'([。！？\n])', para)
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temp_para = ""
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for i in range(0, len(sentences), 2):
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sentence = sentences[i] + (sentences[i+1] if i+1 < len(sentences) else "")
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if len(temp_para) + len(sentence) > self.max_chunk_size:
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if temp_para:
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chunks.append(temp_para)
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temp_para = sentence
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else:
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temp_para += sentence
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if temp_para:
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chunks.append(temp_para)
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# Normal case: accumulate paragraphs
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elif current_length + para_length > self.max_chunk_size and current_chunk:
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chunks.append('\n\n'.join(current_chunk))
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current_chunk = [para]
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current_length = para_length
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else:
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current_chunk.append(para)
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current_length += para_length + 2 # +2 for \n\n
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if current_chunk:
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chunks.append('\n\n'.join(current_chunk))
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return chunks
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def _process_chunk(self, chunk: str, context: str, model: str) -> str:
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"""Process a single chunk with GLM API"""
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prompt = self._build_prompt(chunk, context)
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url = f"{self.base_url}/v1/messages"
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headers = {
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"anthropic-version": "2023-06-01",
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"Authorization": f"Bearer {self.api_key}",
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"content-type": "application/json"
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}
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data = {
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"model": model,
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"max_tokens": 8000,
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"temperature": 0.3,
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"messages": [{"role": "user", "content": prompt}]
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}
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with httpx.Client(timeout=60.0) as client:
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response = client.post(url, headers=headers, json=data)
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response.raise_for_status()
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result = response.json()
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return result["content"][0]["text"]
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def _build_prompt(self, chunk: str, context: str) -> str:
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"""Build correction prompt for GLM"""
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base_prompt = """你是专业的会议记录校对专家。请修复以下会议转录中的语音识别错误。
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**修复原则**：
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1. 严格保留原有格式（时间戳、发言人标识、Markdown标记等）
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2. 修复明显的同音字错误
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3. 修复专业术语错误
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4. 修复语法错误，但保持口语化特征
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5. 不确定的地方保持原样，不要过度修改
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"""
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if context:
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base_prompt += f"\n**会议背景**：\n{context}\n"
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base_prompt += f"""
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**需要修复的内容**：
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{chunk}
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**请直接输出修复后的文本，不要添加任何解释或标注**："""
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return base_prompt
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