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放到code文件夹下 ... main

Author SHA1 Message Date
Shuang_Dong
7eec301831 添加code目录下的所有代码文件 2025-06-30 16:03:57 +08:00
Shuang_Dong
8f021a93d3 添加requirements.txt和requirements_ocr_voice.txt文件 2025-06-30 15:54:18 +08:00
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code/README_CnOCR.md Normal file
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# 视频字幕OCR提取器 - CnOCR集成
## 概述
字幕提取器现在支持三种OCR引擎：
- **PaddleOCR**: 百度开源OCR引擎
- **EasyOCR**: 轻量级OCR引擎
- **CnOCR**: 中文OCR专用引擎（新增）
## CnOCR安装和配置
### 1. 自动安装（推荐）
```bash
cd code
python install_cnocr.py
```
### 2. 手动安装
```bash
# 安装CnOCR
pip install cnocr[ort-cpu] -i https://pypi.tuna.tsinghua.edu.cn/simple
# 创建模型目录
mkdir -p /root/autodl-tmp/llm/cnocr
# 设置环境变量
export CNOCR_HOME=/root/autodl-tmp/llm/cnocr
```
## 使用方法
### 1. 单独使用CnOCR
```bash
python ocr_subtitle_extractor.py your_video.mp4 -e cnocr
```
### 2. 使用所有OCR引擎
```bash
python ocr_subtitle_extractor.py your_video.mp4 -e all
```
### 3. 完整参数示例
```bash
python ocr_subtitle_extractor.py your_video.mp4 \
-e cnocr \
-l ch \
-i 30 \
-c 0.5 \
-o results \
-f json \
--position bottom
```
## 参数说明
- `-e, --engine`: OCR引擎选择
- `paddleocr`: 仅使用PaddleOCR
- `easyocr`: 仅使用EasyOCR
- `cnocr`: 仅使用CnOCR（新增）
- `all`: 使用所有三种引擎
- `-l, --language`: 语言设置
- `ch`: 中文
- `en`: 英文
- `ch_en`: 中英文混合
- `-i, --interval`: 帧采样间隔（默认30帧）
- `-c, --confidence`: 置信度阈值（默认0.5）
- `-o, --output`: 输出目录
- `-f, --format`: 输出格式（json/txt/srt）
- `--position`: 字幕区域位置（full/center/bottom）
## CnOCR特点
1. **专为中文优化**: 对中文识别效果更好
2. **轻量级**: 模型体积较小，运行速度快
3. **易于部署**: 安装简单，依赖少
4. **多种模型**: 支持多种检测和识别模型
## 测试CnOCR集成
```bash
python test_cnocr.py
```
这个脚本会：
1. 测试CnOCR安装
2. 测试模型下载
3. 测试字幕提取器集成
4. 显示测试结果
## 模型存储位置
所有CnOCR模型文件都会下载到：
```
/root/autodl-tmp/llm/cnocr/
```
首次使用时会自动下载所需模型，请耐心等待。
## 输出格式
使用CnOCR时，识别结果中的`engine`字段会标记为`"CnOCR"`，便于区分不同引擎的结果。
## 性能对比
| 引擎 | 中文识别 | 英文识别 | 速度 | 模型大小 |
|------|----------|----------|------|----------|
| PaddleOCR | 优秀 | 优秀 | 中等 | 大 |
| EasyOCR | 良好 | 优秀 | 较慢 | 大 |
| CnOCR | 优秀 | 良好 | 较快 | 中等 |
## 故障排除
### 1. 安装失败
```bash
# 更新pip
pip install --upgrade pip
# 使用国内源
pip install cnocr[ort-cpu] -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 2. 模型下载失败
```bash
# 检查网络连接
# 确保有足够的磁盘空间
# 重新运行安装脚本
python install_cnocr.py
```
### 3. 环境变量问题
```bash
# 在脚本开头添加
export CNOCR_HOME=/root/autodl-tmp/llm/cnocr
```
## 示例输出
```json
{
"video_path": "test_video.mp4",
"subtitles": [
{
"timestamp": 1.5,
"text": "这是一个测试字幕",
"confidence": 0.95,
"bbox": [[10, 20], [200, 20], [200, 50], [10, 50]],
"engine": "CnOCR"
}
],
"stats": {
"total_detections": 150,
"filtered_detections": 120,
"unique_texts": 50,
"average_confidence": 0.87
}
}
```

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code/api_video_with_monitor.py Normal file
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#!/usr/bin/env python3
from openai import OpenAI
import os
import base64
import time
import psutil
import subprocess
from datetime import datetime
class MemoryMonitor:
def __init__(self):
self.checkpoints = []
self.initial_memory = self.get_memory_info()
def get_memory_info(self):
"""获取当前内存使用情况"""
memory = psutil.virtual_memory()
gpu_info = self.get_gpu_memory()
process = psutil.Process()
memory_info = process.memory_info()
return {
"timestamp": datetime.now().isoformat(),
"system_memory_gb": memory.used / 1024**3,
"system_memory_percent": memory.percent,
"gpu_memory": gpu_info,
"process_memory_mb": memory_info.rss / 1024 / 1024
}
def get_gpu_memory(self):
"""获取GPU内存使用情况"""
try:
result = subprocess.run(['nvidia-smi', '--query-gpu=memory.total,memory.used,memory.free',
'--format=csv,noheader,nounits'],
capture_output=True, text=True, check=True)
lines = result.stdout.strip().split('\n')
gpu_info = []
for i, line in enumerate(lines):
parts = line.split(', ')
if len(parts) == 3:
total, used, free = map(int, parts)
gpu_info.append({
"gpu_id": i,
"total_mb": total,
"used_mb": used,
"free_mb": free,
"usage_percent": round(used / total * 100, 2)
})
return gpu_info
except:
return []
def checkpoint(self, name=""):
"""创建内存检查点"""
current_memory = self.get_memory_info()
if self.checkpoints:
last_memory = self.checkpoints[-1]["memory"]
memory_diff = {
"system_memory_gb": current_memory["system_memory_gb"] - last_memory["system_memory_gb"],
"process_memory_mb": current_memory["process_memory_mb"] - last_memory["process_memory_mb"],
}
# GPU内存差异
gpu_diff = []
if current_memory["gpu_memory"] and last_memory["gpu_memory"]:
for i in range(min(len(current_memory["gpu_memory"]), len(last_memory["gpu_memory"]))):
current_gpu = current_memory["gpu_memory"][i]["used_mb"]
last_gpu = last_memory["gpu_memory"][i]["used_mb"]
gpu_diff.append({
"gpu_id": i,
"used_mb_diff": current_gpu - last_gpu
})
memory_diff["gpu_memory"] = gpu_diff
else:
memory_diff = None
checkpoint = {
"name": name,
"memory": current_memory,
"memory_diff": memory_diff
}
self.checkpoints.append(checkpoint)
return checkpoint
def check_memory_risk(self):
"""检查内存风险等级"""
current = self.get_memory_info()
# 系统内存风险
sys_risk = "低"
if current["system_memory_percent"] > 90:
sys_risk = "高"
elif current["system_memory_percent"] > 80:
sys_risk = "中"
# GPU内存风险
gpu_risk = "低"
if current["gpu_memory"]:
max_gpu_usage = max(gpu["usage_percent"] for gpu in current["gpu_memory"])
if max_gpu_usage > 95:
gpu_risk = "高"
elif max_gpu_usage > 85:
gpu_risk = "中"
return {
"system_risk": sys_risk,
"gpu_risk": gpu_risk,
"current_memory": current
}
def print_memory_status(self, title=""):
"""打印当前内存状态"""
current = self.get_memory_info()
risk = self.check_memory_risk()
print(f"\n{'='*50}")
print(f"🔍 {title if title else '内存状态检查'}")
print(f"{'='*50}")
# 系统内存
risk_icon = {"低": "✅", "中": "⚠️", "高": "🚨"}[risk["system_risk"]]
print(f"💾 系统内存: {current['system_memory_gb']:.1f} GB ({current['system_memory_percent']:.1f}%) {risk_icon}")
# GPU内存
if current["gpu_memory"]:
risk_icon = {"低": "✅", "中": "⚠️", "高": "🚨"}[risk["gpu_risk"]]
for gpu in current["gpu_memory"]:
print(f"🎮 GPU {gpu['gpu_id']}: {gpu['used_mb']:.0f}/{gpu['total_mb']:.0f} MB ({gpu['usage_percent']:.1f}%) {risk_icon}")
# 进程内存
print(f"🔧 当前进程: {current['process_memory_mb']:.1f} MB")
return risk
def analyze_file_sizes(video_path, audio_path=None, txt_content=""):
"""分析文件大小和预估内存占用"""
print(f"\n{'='*50}")
print("📊 文件大小分析")
print(f"{'='*50}")
total_estimated_mb = 0
warnings = []
# 视频文件分析
if os.path.exists(video_path):
video_size = os.path.getsize(video_path)
video_size_mb = video_size / 1024 / 1024
base64_size_mb = video_size_mb * 1.33 # Base64编码增加约33%
memory_estimate_mb = base64_size_mb * 2 # 编码过程需要双倍内存
print(f"🎥 视频文件: {os.path.basename(video_path)}")
print(f" 原始大小: {video_size_mb:.2f} MB")
print(f" Base64后: {base64_size_mb:.2f} MB")
print(f" 内存估算: {memory_estimate_mb:.2f} MB")
total_estimated_mb += memory_estimate_mb
if base64_size_mb > 100:
warnings.append("视频文件过大(>100MB Base64)")
elif base64_size_mb > 50:
warnings.append("视频文件较大(>50MB Base64)")
# 音频文件分析
if audio_path and os.path.exists(audio_path):
audio_size = os.path.getsize(audio_path)
audio_size_mb = audio_size / 1024 / 1024
base64_size_mb = audio_size_mb * 1.33
memory_estimate_mb = base64_size_mb * 2
print(f"\n🎵 音频文件: {os.path.basename(audio_path)}")
print(f" 原始大小: {audio_size_mb:.2f} MB")
print(f" Base64后: {base64_size_mb:.2f} MB")
print(f" 内存估算: {memory_estimate_mb:.2f} MB")
total_estimated_mb += memory_estimate_mb
if base64_size_mb > 50:
warnings.append("音频文件过大(>50MB Base64)")
# 文本内容分析
if txt_content:
text_size_mb = len(txt_content.encode('utf-8')) / 1024 / 1024
print(f"\n📝 文本内容: {len(txt_content)} 字符 ({text_size_mb:.3f} MB)")
total_estimated_mb += text_size_mb
if len(txt_content) > 50000:
warnings.append("文本内容过长(>50k字符)")
print(f"\n📋 总估算内存: {total_estimated_mb:.2f} MB")
# 风险评估
if total_estimated_mb > 500:
print("🚨 高风险: 内容过大，强烈建议压缩或分段处理")
warnings.append("总内存占用过高(>500MB)")
elif total_estimated_mb > 200:
print("⚠️ 中风险: 建议监控内存使用")
warnings.append("总内存占用较高(>200MB)")
else:
print("✅ 低风险: 内存占用在可接受范围内")
return total_estimated_mb, warnings
# Base64 编码格式
def encode_video(video_path):
with open(video_path, "rb") as video_file:
return base64.b64encode(video_file.read()).decode("utf-8")
def encode_audio(audio_path):
with open(audio_path, "rb") as audio_file:
return base64.b64encode(audio_file.read()).decode("utf-8")
def read_txt_file(txt_path):
"""读取txt文件内容"""
try:
with open(txt_path, 'r', encoding='utf-8') as file:
content = file.read()
print(f"成功读取txt文件: {txt_path}")
print(f"文件内容长度: {len(content)} 字符")
return content
except FileNotFoundError:
print(f"错误: 找不到文件 {txt_path}")
return ""
except Exception as e:
print(f"读取文件时出错: {e}")
return ""
def save_result_to_txt(response_text, video_path, save_dir="results"):
"""将分析结果保存为TXT文件"""
os.makedirs(save_dir, exist_ok=True)
video_name = os.path.splitext(os.path.basename(video_path))[0]
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
txt_filename = f"{video_name}_analysis_{timestamp}.txt"
txt_path = os.path.join(save_dir, txt_filename)
content = f"""视频分析结果
=====================================
视频文件: {video_path}
分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
=====================================
{response_text}
"""
try:
with open(txt_path, 'w', encoding='utf-8') as f:
f.write(content)
print(f"\n✅ 分析结果已保存到: {txt_path}")
return txt_path
except Exception as e:
print(f"\n❌ 保存TXT文件失败: {e}")
return None
# 初始化内存监控器
monitor = MemoryMonitor()
STREAM_MODE = True
# 文件路径配置
video_path = "/root/autodl-tmp/video2audio/sample_demo_6.mp4"
audio_path = "/root/autodl-tmp/video2audio/sample_demo_6.wav"
#txt_path = "/root/autodl-tmp/hot_video_analyse/source/example_reference.txt"
# 初始内存检查
monitor.checkpoint("程序启动")
monitor.print_memory_status("程序启动时内存状态")
# 分析文件大小和预估内存占用
txt_content = ""
estimated_memory, warnings = analyze_file_sizes(video_path, txt_content=txt_content)
# 如果有警告，询问是否继续
if warnings:
print(f"\n⚠️ 发现以下潜在问题:")
for warning in warnings:
print(f" - {warning}")
print(f"\n建议:")
print(f" - 使用更小的测试文件")
print(f" - 监控内存使用情况")
print(f" - 如遇到错误，尝试压缩文件")
# 编码前内存检查
monitor.checkpoint("开始编码前")
risk_before = monitor.check_memory_risk()
if risk_before["system_risk"] == "高" or risk_before["gpu_risk"] == "高":
print(f"\n🚨 警告: 当前内存使用率已经很高，继续可能导致内存溢出!")
print(f" 系统内存风险: {risk_before['system_risk']}")
print(f" GPU内存风险: {risk_before['gpu_risk']}")
print("\n开始编码文件...")
encode_start_time = time.time()
try:
base64_video = encode_video(video_path)
print(f"✅ 视频编码完成")
except Exception as e:
print(f"❌ 视频编码失败: {e}")
monitor.print_memory_status("编码失败时内存状态")
exit(1)
base64_audio = encode_audio(audio_path)
# 编码后内存检查
monitor.checkpoint("编码完成")
encode_end_time = time.time()
encode_duration = encode_end_time - encode_start_time
print(f"📁 文件编码完成，耗时: {encode_duration:.2f} 秒")
# 检查编码后内存变化
last_checkpoint = monitor.checkpoints[-1]
if last_checkpoint["memory_diff"]:
diff = last_checkpoint["memory_diff"]
print(f"📊 编码过程内存变化:")
print(f" 进程内存增加: {diff['process_memory_mb']:+.1f} MB")
if diff["gpu_memory"]:
for gpu_diff in diff["gpu_memory"]:
print(f" GPU {gpu_diff['gpu_id']} 内存变化: {gpu_diff['used_mb_diff']:+.0f} MB")
client = OpenAI(
api_key="EMPTY",
base_url="http://localhost:8000/v1",
)
# 构建content列表
content_list = [
{
"type": "video_url",
"video_url": {"url": f"data:video/mp4;base64,{base64_video}"},
"type": "audio_url",
"audio_url": {"url": f"data:audio/wav;base64,{base64_audio}"},
}
]
# 如果txt文件有内容，添加到content中
if txt_content.strip():
content_list.append({
"type": "text",
"text": f"参考文档内容:\n{txt_content}\n\n"
})
# 添加主要提示文本（简化版以减少内存使用）
content_list.append({
"type": "text",
"text": """请分析这个抖音短视频的内容：
1. **口播内容**：转录视频中的语音内容
2. **字幕文字**：识别画面中的文字和字幕
3. **勾子分析**：分析视频的开头勾子策略
请用JSON格式输出结果：
{
"口播分析": {"是否有口播": "", "口播内容": "", "讲话时长": ""},
"字幕分析": {"是否有字幕": "", "字幕内容": "", "字幕位置": ""},
"勾子分析": {"勾子类型": "", "勾子公式": "", "勾子内容": ""}
}"""
})
# API请求前内存检查
monitor.checkpoint("API请求前")
monitor.print_memory_status("API请求前内存状态")
print(f"\n🚀 开始请求API...")
print(f"📅 请求时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"🔄 Stream模式: {STREAM_MODE}")
print(f"📋 Content项目数量: {len(content_list)}")
# 计算请求大小
total_request_size = sum(len(str(content)) for content in content_list)
print(f"📏 请求总大小: {total_request_size/1024/1024:.2f} MB")
api_start_time = time.time()
try:
completion = client.chat.completions.create(
model="/root/autodl-tmp/llm/Qwen-omni",
messages=[
{
"role": "system",
"content": [{"type":"text","text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": content_list
}
],
stream=STREAM_MODE,
stream_options={"include_usage": True} if STREAM_MODE else None,
max_tokens=1024, # 限制输出长度以节省内存
)
if STREAM_MODE:
full_response = ""
usage_info = None
first_token_time = None
token_count = 0
print("✨ 正在生成回复...")
for chunk in completion:
if chunk.choices:
delta = chunk.choices[0].delta
if delta.content:
if first_token_time is None:
first_token_time = time.time()
first_token_delay = first_token_time - api_start_time
print(f"🚀 首个token延迟: {first_token_delay:.2f} 秒")
full_response += delta.content
token_count += 1
else:
usage_info = chunk.usage
api_end_time = time.time()
total_duration = api_end_time - api_start_time
print("\n" + "="*50)
print("📝 完整回复:")
print("="*50)
print(full_response)
# 保存结果为TXT文件
txt_file_path = save_result_to_txt(full_response, video_path)
# API完成后内存检查
monitor.checkpoint("API完成")
# 输出时间统计信息
print("\n" + "="*50)
print("⏱️ 时间统计:")
print("="*50)
print(f"📁 文件编码时间: {encode_duration:.2f} 秒")
if first_token_time:
print(f"🚀 首个token延迟: {first_token_delay:.2f} 秒")
generation_time = api_end_time - first_token_time
print(f"⚡ 内容生成时间: {generation_time:.2f} 秒")
print(f"🕐 API总响应时间: {total_duration:.2f} 秒")
print(f"📊 生成token数量: {token_count}")
if first_token_time and token_count > 0:
tokens_per_second = token_count / generation_time
print(f"🔥 生成速度: {tokens_per_second:.2f} tokens/秒")
print(f"⏰ 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
if usage_info:
print(f"\n📈 使用情况: {usage_info}")
except Exception as e:
print(f"\n❌ API请求失败!")
print(f"错误类型: {type(e)}")
print(f"错误信息: {e}")
# 错误时进行内存检查
monitor.checkpoint("API错误")
monitor.print_memory_status("API错误时内存状态")
# 分析可能的原因
if "Internal Server Error" in str(e) or "OutOfMemoryError" in str(e):
print(f"\n💡 可能的内存溢出原因:")
print(f" - 视频文件过大 ({estimated_memory:.1f} MB)")
print(f" - GPU内存不足")
print(f" - 系统内存不足")
print(f"\n建议解决方案:")
print(f" - 使用更小的视频文件")
print(f" - 重启vLLM服务释放GPU内存")
print(f" - 降低max_tokens限制")
# 最终内存状态报告
print(f"\n{'='*60}")
print("📊 最终内存使用报告")
print(f"{'='*60}")
for i, checkpoint in enumerate(monitor.checkpoints):
print(f"{i+1}. {checkpoint['name']}")
if checkpoint['memory_diff']:
diff = checkpoint['memory_diff']
if abs(diff['process_memory_mb']) > 10: # 只显示显著变化
print(f" 进程内存变化: {diff['process_memory_mb']:+.1f} MB")
if diff['gpu_memory']:
for gpu_diff in diff['gpu_memory']:
if abs(gpu_diff['used_mb_diff']) > 50: # 只显示显著变化
print(f" GPU {gpu_diff['gpu_id']} 变化: {gpu_diff['used_mb_diff']:+.0f} MB")
monitor.print_memory_status("程序结束时内存状态")

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
批量视频字幕提取器
支持批量处理多个视频文件，提取字幕
支持PaddleOCR、EasyOCR和CnOCR三种引擎
"""
import os
import sys
import time
import json
import argparse
from pathlib import Path
from datetime import datetime
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
# 添加当前目录到路径以导入OCR模块
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from ocr_subtitle_extractor import VideoSubtitleExtractor
# 设置OCR模型路径环境变量
os.environ['EASYOCR_MODULE_PATH'] = '/root/autodl-tmp/llm/easyocr'
os.environ['CNOCR_HOME'] = '/root/autodl-tmp/llm/cnocr'
# 设置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class BatchSubtitleExtractor:
"""批量视频字幕提取器"""
def __init__(self, ocr_engine="paddleocr", language="ch", max_workers=2):
"""
初始化批量提取器
Args:
ocr_engine: OCR引擎 ("paddleocr", "easyocr", "cnocr", "all")
language: 语言设置 ("ch", "en", "ch_en")
max_workers: 最大并行工作数
"""
self.ocr_engine = ocr_engine
self.language = language
self.max_workers = max_workers
self.extractor = VideoSubtitleExtractor(ocr_engine=ocr_engine, language=language)
def find_video_files(self, input_dir):
"""查找目录中的所有视频文件"""
video_extensions = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.webm']
video_files = []
input_path = Path(input_dir)
if input_path.is_file():
# 单个文件
if input_path.suffix.lower() in video_extensions:
video_files.append(input_path)
elif input_path.is_dir():
# 目录中的所有视频文件
for ext in video_extensions:
video_files.extend(input_path.glob(f"*{ext}"))
video_files.extend(input_path.glob(f"*{ext.upper()}"))
return sorted(video_files)
def extract_single_video(self, video_path, output_dir, **kwargs):
"""
处理单个视频文件
Args:
video_path: 视频文件路径
output_dir: 输出目录
**kwargs: 其他参数
Returns:
dict: 处理结果
"""
video_path = Path(video_path)
video_name = video_path.stem
logger.info(f"开始处理视频: {video_path}")
start_time = time.time()
try:
# 提取字幕
results = self.extractor.extract_subtitles_from_video(
str(video_path),
sample_interval=kwargs.get('interval', 30),
confidence_threshold=kwargs.get('confidence', 0.5),
subtitle_position=kwargs.get('position', 'bottom')
)
# 保存结果
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
for format_type in kwargs.get('formats', ['json']):
output_file = output_path / f"{video_name}_subtitles.{format_type}"
self.extractor.save_results(results, output_file, format_type)
process_time = time.time() - start_time
results['process_time'] = process_time
results['video_path'] = str(video_path)
results['success'] = True
# 统计位置信息
subtitles_with_bbox = [s for s in results['subtitles'] if s.get('bbox')]
bbox_coverage = len(subtitles_with_bbox) / len(results['subtitles']) * 100 if results['subtitles'] else 0
logger.info(f"完成处理视频: {video_path} (耗时: {process_time:.2f}秒)")
logger.info(f" 字幕总数: {len(results['subtitles'])}")
logger.info(f" 有位置信息: {len(subtitles_with_bbox)}")
logger.info(f" 位置信息覆盖率: {bbox_coverage:.1f}%")
return {
'video_path': str(video_path),
'success': True,
'process_time': process_time,
'subtitle_count': results['stats']['filtered_detections'],
'text_length': results['stats']['text_length'],
'total_subtitles': len(results['subtitles']),
'subtitles_with_bbox': len(subtitles_with_bbox),
'bbox_coverage': bbox_coverage,
'output_files': [str(output_path / f"{video_name}_subtitles.{fmt}") for fmt in kwargs.get('formats', ['json'])]
}
except Exception as e:
error_msg = f"处理视频 {video_path} 时出错: {str(e)}"
logger.error(error_msg)
return {
'video_path': str(video_path),
'success': False,
'error': error_msg,
'process_time': time.time() - start_time
}
def extract_batch(self, input_dir, output_dir, parallel=True, **kwargs):
"""
批量提取字幕
Args:
input_dir: 输入目录或文件
output_dir: 输出目录
parallel: 是否并行处理
**kwargs: 其他参数
Returns:
dict: 批量处理结果
"""
logger.info(f"开始批量字幕提取")
logger.info(f"输入: {input_dir}")
logger.info(f"输出目录: {output_dir}")
logger.info(f"OCR引擎: {self.ocr_engine}")
logger.info(f"字幕位置: {kwargs.get('position', 'bottom')}")
logger.info(f"并行处理: {parallel}")
start_time = time.time()
# 查找视频文件
video_files = self.find_video_files(input_dir)
if not video_files:
logger.warning(f"在 {input_dir} 中未找到视频文件")
return {
'success': False,
'message': '未找到视频文件',
'total_files': 0,
'results': []
}
logger.info(f"找到 {len(video_files)} 个视频文件")
results = []
if parallel and len(video_files) > 1:
# 并行处理
logger.info(f"使用 {self.max_workers} 个并行工作进程")
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
# 提交任务
future_to_video = {
executor.submit(self.extract_single_video, video_file, output_dir, **kwargs): video_file
for video_file in video_files
}
# 收集结果
for future in as_completed(future_to_video):
video_file = future_to_video[future]
try:
result = future.result()
results.append(result)
# 显示进度
progress = len(results) / len(video_files) * 100
logger.info(f"批量处理进度: {progress:.1f}% ({len(results)}/{len(video_files)})")
except Exception as e:
logger.error(f"处理视频 {video_file} 时发生异常: {str(e)}")
results.append({
'video_path': str(video_file),
'success': False,
'error': str(e)
})
else:
# 串行处理
for i, video_file in enumerate(video_files, 1):
logger.info(f"处理第 {i}/{len(video_files)} 个视频")
result = self.extract_single_video(video_file, output_dir, **kwargs)
results.append(result)
# 显示进度
progress = i / len(video_files) * 100
logger.info(f"批量处理进度: {progress:.1f}%")
total_time = time.time() - start_time
# 统计结果
success_count = sum(1 for r in results if r['success'])
failed_count = len(results) - success_count
total_subtitles = sum(r.get('subtitle_count', 0) for r in results if r['success'])
total_text_length = sum(r.get('text_length', 0) for r in results if r['success'])
# 统计位置信息
total_subtitles_raw = sum(r.get('total_subtitles', 0) for r in results if r['success'])
total_subtitles_with_bbox = sum(r.get('subtitles_with_bbox', 0) for r in results if r['success'])
overall_bbox_coverage = total_subtitles_with_bbox / total_subtitles_raw * 100 if total_subtitles_raw > 0 else 0
batch_result = {
'success': True,
'total_time': total_time,
'total_files': len(video_files),
'success_count': success_count,
'failed_count': failed_count,
'total_subtitles': total_subtitles,
'total_text_length': total_text_length,
'total_subtitles_raw': total_subtitles_raw,
'total_subtitles_with_bbox': total_subtitles_with_bbox,
'overall_bbox_coverage': overall_bbox_coverage,
'output_directory': output_dir,
'ocr_engine': self.ocr_engine,
'timestamp': datetime.now().isoformat(),
'results': results
}
# 保存批量处理报告
report_file = Path(output_dir) / "batch_report.json"
with open(report_file, 'w', encoding='utf-8') as f:
json.dump(batch_result, f, ensure_ascii=False, indent=2)
logger.info(f"批量处理完成！")
logger.info(f"总文件数: {len(video_files)}")
logger.info(f"成功: {success_count}, 失败: {failed_count}")
logger.info(f"总耗时: {total_time:.2f} 秒")
logger.info(f"提取字幕: {total_subtitles} 个")
logger.info(f"文本长度: {total_text_length} 字符")
logger.info(f"位置信息统计:")
logger.info(f" 总字幕数: {total_subtitles_raw}")
logger.info(f" 有位置信息: {total_subtitles_with_bbox}")
logger.info(f" 位置信息覆盖率: {overall_bbox_coverage:.1f}%")
logger.info(f"处理报告: {report_file}")
return batch_result
def main():
"""主函数"""
parser = argparse.ArgumentParser(description="批量视频字幕提取器")
parser.add_argument("input", help="输入视频文件或目录")
parser.add_argument("-e", "--engine", default="cnocr",
choices=["paddleocr", "easyocr", "cnocr", "all"],
help="OCR引擎 (默认: paddleocr)")
parser.add_argument("-l", "--language", default="ch",
choices=["ch", "en", "ch_en"],
help="语言设置 (默认: ch)")
parser.add_argument("-i", "--interval", type=int, default=30,
help="帧采样间隔 (默认: 30)")
parser.add_argument("-c", "--confidence", type=float, default=0.5,
help="置信度阈值 (默认: 0.5)")
parser.add_argument("-o", "--output", default="batch_subtitles",
help="输出目录 (默认: batch_subtitles)")
parser.add_argument("-f", "--formats", nargs='+', default=["json"],
choices=["json", "txt", "srt"],
help="输出格式 (默认: json)")
parser.add_argument("--position", default="full",
choices=["full", "center", "bottom"],
help="字幕区域位置 (full=全屏, center=居中0.5-0.8, bottom=居下0.7-1.0)")
parser.add_argument("--workers", type=int, default=2,
help="并行工作进程数 (默认: 2)")
parser.add_argument("--no-parallel", action="store_true",
help="禁用并行处理")
args = parser.parse_args()
# 创建批量提取器
batch_extractor = BatchSubtitleExtractor(
ocr_engine=args.engine,
language=args.language,
max_workers=args.workers
)
try:
# 执行批量提取
result = batch_extractor.extract_batch(
input_dir=args.input,
output_dir=args.output,
parallel=not args.no_parallel,
interval=args.interval,
confidence=args.confidence,
formats=args.formats,
position=args.position
)
if result['success']:
print(f"\n✅ 批量字幕提取完成!")
print(f"📁 输出目录: {args.output}")
print(f"📊 成功处理: {result['success_count']}/{result['total_files']} 个视频")
if result['failed_count'] > 0:
print(f"❌ 失败: {result['failed_count']} 个")
print(f"⏱️ 总耗时: {result['total_time']:.2f} 秒")
print(f"📝 字幕片段: {result['total_subtitles']} 个")
print(f"📏 文本长度: {result['total_text_length']} 字符")
print(f"📍 位置信息统计:")
print(f" 总字幕数: {result['total_subtitles_raw']}")
print(f" 有位置信息: {result['total_subtitles_with_bbox']}")
print(f" 位置信息覆盖率: {result['overall_bbox_coverage']:.1f}%")
else:
print(f"\n❌ 批量处理失败: {result.get('message', '未知错误')}")
except Exception as e:
logger.error(f"批量处理出错: {str(e)}")
print(f"\n❌ 批量处理出错: {str(e)}")
if __name__ == "__main__":
main()

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from openai import OpenAI
import os
import base64
import time
from datetime import datetime
# Base64 编码格式
def encode_video(video_path):
with open(video_path, "rb") as video_file:
return base64.b64encode(video_file.read()).decode("utf-8")
def encode_audio(audio_path):
with open(audio_path, "rb") as audio_file:
return base64.b64encode(audio_file.read()).decode("utf-8")
def read_txt_file(txt_path):
"""读取txt文件内容"""
try:
with open(txt_path, 'r', encoding='utf-8') as file:
content = file.read()
print(f"成功读取txt文件: {txt_path}")
print(f"文件内容长度: {len(content)} 字符")
return content
except FileNotFoundError:
print(f"错误: 找不到文件 {txt_path}")
return ""
except Exception as e:
print(f"读取文件时出错: {e}")
return ""
def read_json_file(json_path):
"""读取JSON文件内容"""
try:
import json
with open(json_path, 'r', encoding='utf-8') as file:
data = json.load(file)
print(f"成功读取JSON文件: {json_path}")
return data
except FileNotFoundError:
print(f"错误: 找不到文件 {json_path}")
return None
except json.JSONDecodeError as e:
print(f"JSON解析错误: {e}")
return None
except Exception as e:
print(f"读取JSON文件时出错: {e}")
return None
def format_speech_json(speech_data):
"""格式化口播转文字JSON数据（支持SenseVoice格式）"""
if not speech_data:
return ""
formatted_text = "【口播转文字内容】\n"
if isinstance(speech_data, dict):
# 新SenseVoice格式 - 处理raw_result
if 'raw_result' in speech_data:
raw_result = speech_data['raw_result']
if isinstance(raw_result, list) and len(raw_result) > 0:
# 提取所有文本内容
all_texts = []
for item in raw_result:
if isinstance(item, dict) and 'text' in item:
text = item['text']
# 清理SenseVoice的特殊标签
import re
clean_text = re.sub(r'<\|[^|]+\|>', '', text)
clean_text = ' '.join(clean_text.split())
if clean_text.strip():
all_texts.append(clean_text.strip())
if all_texts:
formatted_text += f"完整转录文本: {' '.join(all_texts)}\n"
# 基本信息
if 'model' in speech_data:
formatted_text += f"转录模型: {speech_data['model']}\n"
if 'transcribe_time' in speech_data:
formatted_text += f"转录耗时: {speech_data['transcribe_time']:.3f}秒\n"
if 'file_path' in speech_data:
formatted_text += f"音频文件: {speech_data['file_path']}\n"
# 旧SenseVoice格式（兼容）
elif 'clean_text' in speech_data:
formatted_text += f"完整转录文本: {speech_data['clean_text']}\n"
if 'model' in speech_data:
formatted_text += f"转录模型: {speech_data['model']}\n"
if 'transcribe_time' in speech_data:
formatted_text += f"转录耗时: {speech_data['transcribe_time']:.3f}秒\n"
# 情绪分析
if 'emotions' in speech_data and speech_data['emotions']:
emotions = [emotion.get('emotion', '') for emotion in speech_data['emotions']]
formatted_text += f"情绪分析: {', '.join(emotions)}\n"
# 背景事件
if 'events' in speech_data and speech_data['events']:
events = [event.get('event', '') for event in speech_data['events']]
formatted_text += f"音频事件: {', '.join(events)}\n"
# 如果是字幕提取器的格式（备用）
elif 'continuous_text' in speech_data:
formatted_text += f"完整文本: {speech_data['continuous_text']}\n"
if 'stats' in speech_data:
stats = speech_data['stats']
formatted_text += f"统计信息: 检测数量{stats.get('filtered_detections', 0)}个，"
formatted_text += f"平均置信度{stats.get('average_confidence', 0):.3f}\n"
return formatted_text
def format_whisper_json(whisper_data):
"""格式化Whisper口播转文字JSON数据"""
if not whisper_data:
return ""
formatted_text = "【Whisper口播转文字内容】\n"
if isinstance(whisper_data, dict):
# 基本信息
# 详细时间轴 - 显示所有片段
if 'segments' in whisper_data and len(whisper_data['segments']) > 0:
formatted_text += "\n详细时间轴:\n"
for segment in whisper_data['segments']:
segment_id = segment.get('id', 0)
start_time = segment.get('start', 0)
end_time = segment.get('end', 0)
text = segment.get('text', '')
formatted_text += f" id:{segment_id}, start:{start_time:.2f}, end:{end_time:.2f}, text:{text}\n"
return formatted_text
def format_ocr_json(ocr_data):
"""格式化OCR字幕转文字JSON数据"""
if not ocr_data:
return ""
formatted_text = "【OCR字幕识别内容】\n"
# 如果是字幕提取器的格式
if isinstance(ocr_data, dict):
# 显示使用的OCR引擎
# if 'ocr_engine' in ocr_data:
# formatted_text += f"OCR引擎: {ocr_data['ocr_engine']}\n"
if 'continuous_text' in ocr_data:
formatted_text += f"完整字幕文本: {ocr_data['continuous_text']}\n"
# if 'subtitles' in ocr_data and len(ocr_data['subtitles']) > 0:
# formatted_text += "详细字幕时间轴:\n"
# for subtitle in ocr_data['subtitles'][:10]: # 只显示前10个，避免过长
# timestamp = subtitle.get('timestamp', 0)
# text = subtitle.get('text', '')
# engine = subtitle.get('engine', '')
# confidence = subtitle.get('confidence', 0)
# formatted_text += f" {timestamp:.2f}s [{engine}|{confidence:.3f}]: {text}\n"
# if len(ocr_data['subtitles']) > 10:
# formatted_text += f" ... (还有{len(ocr_data['subtitles']) - 10}个字幕片段)\n"
return formatted_text
def format_clip_json(clip_data):
"""格式化视频转场分析JSON数据"""
if not clip_data:
return ""
formatted_text = "【视频转场分析内容】\n"
if isinstance(clip_data, dict):
# 显示视频基本信息
if 'video_name' in clip_data:
formatted_text += f"视频名称: {clip_data['video_name']}\n"
if 'analysis_time' in clip_data:
formatted_text += f"分析时间: {clip_data['analysis_time']}\n"
if 'total_scenes' in clip_data:
formatted_text += f"检测到场景数: {clip_data['total_scenes']} 个\n"
# 详细场景信息
if 'scenes' in clip_data and len(clip_data['scenes']) > 0:
formatted_text += "\n详细场景信息:\n"
for i, scene in enumerate(clip_data['scenes'], 1):
formatted_text += f"scenes {i}:\n"
formatted_text += f" start_time: {scene.get('start_time', 0):.2f}秒\n"
formatted_text += f" end_time: {scene.get('end_time', 0):.2f}秒\n"
formatted_text += f" duration: {scene.get('duration', 0):.2f}秒\n"
formatted_text += f" type: {scene.get('type')}\n"
formatted_text += "\n"
return formatted_text
def save_result_to_txt(response_text, video_path, save_dir="/root/autodl-tmp/final_output"):
"""将分析结果保存为TXT文件"""
# 创建保存目录
os.makedirs(save_dir, exist_ok=True)
# 生成文件名（基于视频文件名和时间戳）
video_name = os.path.splitext(os.path.basename(video_path))[0]
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
txt_filename = f"{video_name}_analysis_{timestamp}.txt"
txt_path = os.path.join(save_dir, txt_filename)
# 准备保存内容（添加头部信息）
content = f"""视频分析结果
=====================================
视频文件: {video_path}
分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
=====================================
{response_text}
"""
# 保存到文件
try:
with open(txt_path, 'w', encoding='utf-8') as f:
f.write(content)
print(f"\n✅ 分析结果已保存到: {txt_path}")
return txt_path
except Exception as e:
print(f"\n❌ 保存TXT文件失败: {e}")
return None
STREAM_MODE = True
# 文件路径配置
video_path = "/root/autodl-tmp/new/老挝泼水节.mp4"
#audio_path = "/root/autodl-tmp/video2audio/sample_demo_6.wav"
#txt_path = "/root/autodl-tmp/hot_video_analyse/source/example_reference.txt" # 使用示例参考文档
# JSON文件路径配置
speech_json_path = "/root/autodl-tmp/new_sensevoice/老挝泼水节_sensevoice.json" # 口播转文字JSON文件
ocr_json_path = "/root/autodl-tmp/new_cnocr/老挝泼水节_subtitles.json" # OCR字幕转文字JSON文件
#clip_json_path = "/root/autodl-tmp/02_VideoSplitter/VideoSplitter_output/shou_gonglve_3_scenes.json"
whisper_json_path = "/root/autodl-tmp/new_whisper/老挝泼水节_transcript.json" # Whisper转文字JSON文件
# 编码文件
print("开始编码文件...")
encode_start_time = time.time()
base64_video = encode_video(video_path)
#base64_audio = encode_audio(audio_path)
#txt_content = read_txt_file(txt_path)
#读取JSON文件内容
print("读取JSON文件...")
speech_data = read_json_file(speech_json_path)
ocr_data = read_json_file(ocr_json_path)
#clip_data = read_json_file(clip_json_path)
whisper_data = read_json_file(whisper_json_path)
# 格式化JSON内容
speech_content = format_speech_json(speech_data)
ocr_content = format_ocr_json(ocr_data)
#clip_content = format_clip_json(clip_data)
whisper_content = format_whisper_json(whisper_data)
# # 合并内容
txt_content = ""
# if speech_content:
# txt_content += speech_content + "\n\n"
if ocr_content:
txt_content += ocr_content + "\n\n"
# if clip_content:
# txt_content += clip_content + "\n\n"
if whisper_content:
txt_content += whisper_content + "\n\n"
print(f"合并后的参考内容长度: {len(txt_content)} 字符")
print(txt_content)
encode_end_time = time.time()
encode_duration = encode_end_time - encode_start_time
print(f"文件编码完成，耗时: {encode_duration:.2f} 秒")
client = OpenAI(
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
api_key="EMPTY",
base_url="http://localhost:8000/v1",
)
# 构建content列表
content_list = [
{
# 直接传入视频文件时，请将type的值设置为video_url
"type": "video_url",
"video_url": {"url": f"data:video/mp4;base64,{base64_video}"},
}
# ,
# {
# "type": "audio_url",
# "audio_url": {"url": f"data:audio/wav;base64,{base64_audio}"},
# }
]
# 如果txt文件有内容，添加到content中
if txt_content.strip():
content_list.append({
"type": "text",
"text": f"参考资料内容:\n{txt_content}\n\n",
"need": "第一部分是视频内容，第二部分是视频的字幕时间轴内容，第三部分是口播的字幕时间轴内容"
})
# 添加主要提示文本
content_list.append({
"type": "text",
"text": """🎥 **抖音短视频内容分析专家**
## 任务背景
您是一位经验丰富的视频导演和编辑，需要基于以上两个时间轴数据，和视频内容。为视频写一个完整、流畅的脚本。
请对这个抖音短视频进行详细的内容分析，重点关注以下三个方面：
## 🎤 一、口播内容提取
请仔细听取视频中的语音内容，完整转录：
- **完整口播转录**：参考口播的字幕时间轴内容和视频内容，逐字逐句转录所有口语表达
- **语音时长**：估算总的讲话时长
## 📝 二、字幕文字识别
请识别视频画面中出现的所有文字内容：
- **屏幕字幕**：参考口播的字幕时间轴内容和视频内容，识别字幕
- **标题文字**：识别停靠时间稍长的，视频开头、中间、结尾出现的大标题和贴图。
## 🎬 三、转场效果分析
请仔细观察视频中的转场效果，并且结合参考资料中的时间轴内容，请你整体分析一下视频。比如几个画面出现第一个转场等.
转场的time_start","time_end","textIdx"请严格按照参考资料中的口播内容的时间戳start,end,id和字幕内容的时间戳“开始时间“，”结束时间“
填写，不要自己生成。
## 📊 输出格式要求
## 视频内容分析
请按照以下JSON格式输出视频描述：
{
"total_Oral broadcasting":"请你生成一个完整的口播内容。",
"summary": "请用一句话总结视频的核心内容，突出视频的主要卖点和价值主张",
"content": [
{
"type": "cut",
"scenes": 1,
"time_start": 0.0,
"time_end": 2.0,
"talk": "请将对应时间的口播或字幕信息，填入此",
"description": "详细描述这个镜头的画面内容、人物动作、场景特点等"
},
{
"type": "cut",
"scenes": 2,
"time_start": 2.0,
"time_end": 4.5,
"talk": "请将对应时间的口播或字幕信息，填入此",
"description": "描述这个镜头的具体内容，包括画面细节、转场效果等"
},
{
"type": "cut",
"scenes": 3,
"time_start": 4.5,
"time_end": 6.0,
"talk": "请将对应时间的口播或字幕信息，填入此",
"description": "描述这个镜头的具体内容，包括画面细节、转场效果等"
}
]
}
## 输出要求
1. summary：用一句话概括视频核心内容，突出主要卖点
2. content：按时间顺序交替描述镜头和转场
- 镜头(lens)描述：
* textIdx：镜头序号，从1开始递增
* time_start：开始时间（秒），精确到小数点后一位
* time_end：结束时间（秒），精确到小数点后一位
* talk：该镜头中的对话或文字内容
* description：详细描述镜头内容，包括：
- 画面构图和场景
- 人物动作和表情
- 重要道具和元素
- 特殊效果和转场
## 注意事项
1. 保持描述简洁明了，但要有足够的细节
2. 突出视频的亮点和特色
3. 确保时间戳的准确性
4. 对话内容要符合视频画面
5. 整体风格要统一连贯
6. 每个镜头的描述要包含关键信息
## 示例内容描述
1. 镜头1：
- 开场特写镜头，展示产品外观
- 画面从模糊到清晰，突出产品细节
- 背景音乐渐入，营造氛围
- 文字提示："全新升级，品质保证"
2. 转场1-2：
- 类型：平滑滑动
- 目的：自然过渡到使用场景
- 效果：画面从产品特写平滑滑向使用场景
3. 镜头2：
- 中景展示使用场景
- 人物自然流畅的动作展示
- 光线明亮，突出产品效果
- 文字说明："简单操作，轻松上手"
4. 转场2-3：
- 类型：快速缩放
- 目的：突出产品核心功能
- 效果：画面快速聚焦到产品关键部位
5. 镜头3：
- 特写展示产品核心功能
- 慢动作展示关键细节
- 画面色彩鲜明，对比强烈
- 文字强调："专业性能，值得信赖"
请根据以上要求，分析视频并输出JSON格式的描述。
请开始详细分析这个抖音短视频："""
})
print(f"\n开始请求API...")
print(f"请求时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"Stream模式: {STREAM_MODE}")
print(f"Content项目数量: {len(content_list)}")
# 记录API请求开始时间
api_start_time = time.time()
completion = client.chat.completions.create(
model="/root/autodl-tmp/llm/Qwen-omni",
messages=[
{
"role": "system",
"content": [{"type":"text","text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": content_list
}
],
stream=STREAM_MODE,
stream_options={"include_usage": True} if STREAM_MODE else None,
temperature=0.3
)
if STREAM_MODE:
# 流式输出 - 拼接完整回复
full_response = ""
usage_info = None
# 记录第一个token的时间
first_token_time = None
token_count = 0
print("正在生成回复...")
for chunk in completion:
if chunk.choices:
delta = chunk.choices[0].delta
if delta.content:
# 记录第一个token的时间
if first_token_time is None:
first_token_time = time.time()
first_token_delay = first_token_time - api_start_time
print(f"首个token延迟: {first_token_delay:.2f} 秒")
# 拼接内容
full_response += delta.content
token_count += 1
# 实时显示（可选）
#print(delta.content, end='', flush=True)
else:
# 保存使用情况信息
usage_info = chunk.usage
# 记录API请求结束时间
api_end_time = time.time()
total_duration = api_end_time - api_start_time
# 输出完整的响应
print("\n" + "="*50)
print("完整回复:")
print("="*50)
print(full_response)
# 保存结果为TXT文件
txt_file_path = save_result_to_txt(full_response + "total_duration:" + str(total_duration), video_path)
# 输出时间统计信息
print("\n" + "="*50)
print("⏱️ 时间统计:")
print("="*50)
print(f"📁 文件编码时间: {encode_duration:.2f} 秒")
if first_token_time:
print(f"🚀 首个token延迟: {first_token_delay:.2f} 秒")
generation_time = api_end_time - first_token_time
print(f"⚡ 内容生成时间: {generation_time:.2f} 秒")
print(f"🕐 API总响应时间: {total_duration:.2f} 秒")
print(f"📊 生成token数量: {token_count}")
if first_token_time and token_count > 0:
tokens_per_second = token_count / generation_time
print(f"🔥 生成速度: {tokens_per_second:.2f} tokens/秒")
print(f"⏰ 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
# 输出使用情况信息
if usage_info:
print("\n" + "="*50)
print("📈 使用情况:")
print("="*50)
print(usage_info)
else:
# 非流式输出 - 直接输出完整响应
api_end_time = time.time()
total_duration = api_end_time - api_start_time
print("非流式输出模式:")
print("完整回复:")
print("="*50)
print(completion.choices[0].message.content)
# 保存结果为TXT文件
txt_file_path = save_result_to_txt(completion.choices[0].message.content, video_path)
# 输出时间统计信息
print("\n" + "="*50)
print("⏱️ 时间统计:")
print("="*50)
print(f"📁 文件编码时间: {encode_duration:.2f} 秒")
print(f"🕐 API总响应时间: {total_duration:.2f} 秒")
print(f"⏰ 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
# 输出使用情况信息
if hasattr(completion, 'usage') and completion.usage:
print("\n" + "="*50)
print("📈 使用情况:")
print("="*50)
print(completion.usage)

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
导演编辑提示词生成器
整合OCR字幕和Whisper口播时间轴，生成专业视频脚本
"""
def generate_director_prompt(ocr_timeline, whisper_timeline, video_info=None):
"""
生成导演编辑提示词
Args:
ocr_timeline: OCR字幕时间轴数据
whisper_timeline: Whisper口播时间轴数据
video_info: 视频基本信息（可选）
Returns:
str: 导演编辑提示词
"""
prompt = f"""
# 导演编辑任务：视频脚本整合与优化
## 任务背景
您是一位经验丰富的视频导演和编辑，需要基于以下两个时间轴数据，为视频创作一个完整、流畅的脚本。
## 输入数据
### OCR字幕时间轴（视觉文字内容）
{ocr_timeline}
### Whisper口播时间轴（音频内容）
{whisper_timeline}
{video_info if video_info else ""}
## 导演编辑要求
### 1. 内容整合策略
- **时间同步**：将OCR字幕与Whisper口播按时间轴精确对齐
- **内容互补**：识别字幕与口播的重复、补充和差异部分
- **信息完整性**：确保重要信息不遗漏，避免重复冗余
### 2. 脚本结构设计
- **开场设计**：基于前3秒内容设计引人入胜的开场
- **节奏控制**：根据时间轴密度调整内容节奏
- **高潮设置**：识别关键信息点，设计内容高潮
- **结尾收束**：基于最后内容设计有力结尾
### 3. 语言风格优化
- **口语化处理**：将OCR识别文字转换为自然口语表达
- **情感表达**：根据内容调整语调、语速和情感色彩
- **文化适配**：考虑目标受众，调整表达方式
### 4. 视觉与音频协调
- **字幕时机**：优化字幕出现时机，与音频节奏配合
- **重点突出**：识别关键信息，在脚本中重点标注
- **转场设计**：设计自然的内容转场和过渡
## 输出格式要求
请按以下格式输出脚本：
### 完整脚本
```
[时间戳] [角色/场景] [内容]
```
### 脚本分析
- **内容概览**：简要总结视频核心内容
- **关键信息点**：列出3-5个最重要的信息
- **目标受众**：分析适合的观众群体
- **传播建议**：提供传播和推广建议
### 技术参数
- **总时长**：基于时间轴计算
- **内容密度**：评估信息密度是否合适
- **节奏分析**：分析内容节奏变化
## 创作原则
1. **真实性**：保持原始内容的真实性，不添加虚构信息
2. **流畅性**：确保脚本逻辑清晰，表达流畅
3. **吸引力**：增强内容的吸引力和传播性
4. **专业性**：体现专业导演的编辑水平
请基于以上要求，创作一个完整、专业的视频脚本。
"""
return prompt
def format_timeline_for_prompt(timeline_data, timeline_type):
"""
格式化时间轴数据用于提示词
Args:
timeline_data: 时间轴数据
timeline_type: 时间轴类型 ("OCR" 或 "Whisper")
Returns:
str: 格式化的时间轴文本
"""
if timeline_type == "OCR":
formatted = "OCR字幕识别结果：\n"
for entry in timeline_data:
timestamp = entry.get('timestamp', 0)
contents = entry.get('contents', [])
formatted += f"时间点 {timestamp:.2f}s:\n"
for content in contents:
text = content.get('text', '')
bbox = content.get('bbox', [])
formatted += f" - 文字: '{text}'\n"
if bbox:
formatted += f" 位置: {bbox}\n"
formatted += "\n"
elif timeline_type == "Whisper":
formatted = "Whisper语音识别结果：\n"
for i, entry in enumerate(timeline_data):
start_time = entry.get('start', 0)
end_time = entry.get('end', 0)
text = entry.get('text', '')
formatted += f" id:{i}, start:{start_time:.2f}, end:{end_time:.2f}, text:{text}\n"
return formatted
def create_video_script_prompt(ocr_json_path, whisper_data=None):
"""
创建完整的视频脚本提示词
Args:
ocr_json_path: OCR JSON文件路径
whisper_data: Whisper识别数据（可选）
Returns:
str: 完整的导演编辑提示词
"""
# 读取OCR数据
from pre_data_1 import read_json_file, format_ocr_json
ocr_data = read_json_file(ocr_json_path)
if not ocr_data:
return "错误：无法读取OCR数据文件"
# 格式化OCR时间轴
_, subtitle_array = format_ocr_json(ocr_data)
ocr_timeline = format_timeline_for_prompt(subtitle_array, "OCR")
# 格式化Whisper时间轴（如果有）
whisper_timeline = ""
if whisper_data:
whisper_timeline = format_timeline_for_prompt(whisper_data, "Whisper")
else:
whisper_timeline = "（暂无Whisper数据）"
# 视频基本信息
video_info = f"""
### 视频基本信息
- 文件路径: {ocr_json_path}
- OCR引擎: {ocr_data.get('ocr_engine', 'Unknown')}
- 视频时长: {ocr_data.get('duration', 0):.2f}秒
- 视频分辨率: {ocr_data.get('frame_width', 0)}x{ocr_data.get('frame_height', 0)}
- 视频帧率: {ocr_data.get('fps', 0):.2f}FPS
"""
# 生成导演提示词
prompt = generate_director_prompt(ocr_timeline, whisper_timeline, video_info)
return prompt
# 示例使用
if __name__ == "__main__":
# 示例Whisper数据（实际使用时应该从文件读取）
example_whisper_data = [
{"start": 0.00, "end": 1.80, "text": "潑水街不只有云南"},
{"start": 1.80, "end": 3.56, "text": "老窝更远更传统"},
{"start": 3.56, "end": 5.64, "text": "快来接触这份湿身快乐"},
# ... 更多数据
]
# 生成提示词
prompt = create_video_script_prompt(
"/root/autodl-tmp/new_cnocr/老挝泼水节_subtitles.json",
example_whisper_data
)
# 保存提示词到文件
import os
output_path = "/root/autodl-tmp/new_cnocr/director_prompt.txt"
with open(output_path, 'w', encoding='utf-8') as f:
f.write(prompt)
print(f"导演编辑提示词已保存到: {output_path}")
print("\n提示词预览（前500字符）:")
print(prompt[:500] + "...")

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import os
def read_json_file(json_path):
"""读取JSON文件内容"""
try:
import json
with open(json_path, 'r', encoding='utf-8') as file:
data = json.load(file)
print(f"成功读取JSON文件: {json_path}")
return data
except FileNotFoundError:
print(f"错误: 找不到文件 {json_path}")
return None
except json.JSONDecodeError as e:
print(f"JSON解析错误: {e}")
return None
except Exception as e:
print(f"读取JSON文件时出错: {e}")
return None
def calculate_text_similarity(text1, text2):
"""
计算两个文本的相似度（使用Jaccard相似度）
Args:
text1: 第一个文本
text2: 第二个文本
Returns:
float: 相似度值 (0-1之间)
"""
# 检查空文本
if not text1 or not text2:
return 0.0
# 清理文本，移除空白字符
text1 = text1.strip()
text2 = text2.strip()
if not text1 or not text2:
return 0.0
# 如果两个文本完全相同
if text1 == text2:
return 1.0
# 将文本转换为字符集合
chars1 = set(text1)
chars2 = set(text2)
# 计算Jaccard相似度
intersection = len(chars1.intersection(chars2))
union = len(chars1.union(chars2))
similarity = intersection / union if union > 0 else 0.0
return similarity
def calculate_iou(box1, box2):
"""
计算两个边界框的IoU (Intersection over Union)
Args:
box1: 第一个边界框 [x1, y1, x2, y2] 或 [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
box2: 第二个边界框 [x1, y1, x2, y2] 或 [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
Returns:
float: IoU值 (0-1之间)
"""
# 处理不同的输入格式
if len(box1) == 4 and isinstance(box1[0], (int, float)):
# 格式: [x1, y1, x2, y2]
x1_1, y1_1, x2_1, y2_1 = box1
elif len(box1) == 4 and isinstance(box1[0], list):
# 格式: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]] - 取最小和最大坐标
x_coords = [point[0] for point in box1]
y_coords = [point[1] for point in box1]
x1_1, x2_1 = min(x_coords), max(x_coords)
y1_1, y2_1 = min(y_coords), max(y_coords)
else:
raise ValueError("box1格式错误，应为[x1,y1,x2,y2]或[[x1,y1],[x2,y2],[x3,y3],[x4,y4]]")
if len(box2) == 4 and isinstance(box2[0], (int, float)):
# 格式: [x1, y1, x2, y2]
x1_2, y1_2, x2_2, y2_2 = box2
elif len(box2) == 4 and isinstance(box2[0], list):
# 格式: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]] - 取最小和最大坐标
x_coords = [point[0] for point in box2]
y_coords = [point[1] for point in box2]
x1_2, x2_2 = min(x_coords), max(x_coords)
y1_2, y2_2 = min(y_coords), max(y_coords)
else:
raise ValueError("box2格式错误，应为[x1,y1,x2,y2]或[[x1,y1],[x2,y2],[x3,y3],[x4,y4]]")
# 计算交集区域
x_left = max(x1_1, x1_2)
y_top = max(y1_1, y1_2)
x_right = min(x2_1, x2_2)
y_bottom = min(y2_1, y2_2)
# 检查是否有交集
if x_right < x_left or y_bottom < y_top:
return 0.0
# 计算交集面积
intersection_area = (x_right - x_left) * (y_bottom - y_top)
# 计算并集面积
box1_area = (x2_1 - x1_1) * (y2_1 - y1_1)
box2_area = (x2_2 - x1_2) * (y2_2 - y1_2)
union_area = box1_area + box2_area - intersection_area
# 计算IoU
iou = intersection_area / union_area if union_area > 0 else 0.0
return iou
def format_ocr_json(ocr_data):
"""格式化OCR字幕转文字JSON数据"""
if not ocr_data:
return "", []
formatted_text = "【OCR字幕识别内容】\n"
# 如果是字幕提取器的格式
if isinstance(ocr_data, dict):
# 基本信息
if 'ocr_engine' in ocr_data:
formatted_text += f"OCR引擎: {ocr_data['ocr_engine']}\n"
if 'video_path' in ocr_data:
formatted_text += f"视频文件: {ocr_data['video_path']}\n"
if 'duration' in ocr_data:
formatted_text += f"视频时长: {ocr_data['duration']:.2f}秒\n"
if 'fps' in ocr_data:
formatted_text += f"视频帧率: {ocr_data['fps']:.2f}FPS\n"
if 'frame_width' in ocr_data and 'frame_height' in ocr_data:
formatted_text += f"视频分辨率: {ocr_data['frame_width']}x{ocr_data['frame_height']}\n"
# 字幕区域信息
if 'subtitle_position' in ocr_data:
formatted_text += f"字幕区域: {ocr_data['subtitle_position']}\n"
if 'subtitle_region' in ocr_data:
region = ocr_data['subtitle_region']
formatted_text += f"字幕区域坐标: {region}\n"
# 处理参数
if 'sample_interval' in ocr_data:
formatted_text += f"采样间隔: {ocr_data['sample_interval']}帧\n"
if 'confidence_threshold' in ocr_data:
formatted_text += f"置信度阈值: {ocr_data['confidence_threshold']}\n"
# 完整字幕文本
if 'continuous_text' in ocr_data:
formatted_text += f"\n📄 完整字幕文本:\n"
formatted_text += f"{ocr_data['continuous_text']}\n"
# 详细字幕时间轴 - 按三层嵌套数组结构组织
if 'subtitles' in ocr_data and len(ocr_data['subtitles']) > 0:
subtitles = ocr_data['subtitles']
# 按时间戳分组存储
timestamp_groups = {}
for subtitle in subtitles:
timestamp = subtitle.get('timestamp', 0)
text = subtitle.get('text', '')
confidence = subtitle.get('confidence', 0)
engine = subtitle.get('engine', 'Unknown')
bbox = subtitle.get('bbox', [])
if timestamp not in timestamp_groups:
timestamp_groups[timestamp] = []
# 第三层：内容和位置
subtitle_content = {
'text': text,
'bbox': bbox,
"timestamp": timestamp
}
timestamp_groups[timestamp].append(subtitle_content)
# 转换为三层嵌套数组结构
subtitle_array = []
sorted_timestamps = sorted(timestamp_groups.keys())
for timestamp in sorted_timestamps:
# 第一层：时间戳
timestamp_entry = {
'timestamp': timestamp,
'contents': timestamp_groups[timestamp] # 第二层：同一时间戳内的各个内容
}
subtitle_array.append(timestamp_entry)
# 显示三层嵌套数组结构
formatted_text += f"\n⏰ 详细字幕时间轴 (三层嵌套数组结构):\n"
# 只显示前10个时间戳，避免过长
display_count = min(10, len(subtitle_array))
for i, timestamp_entry in enumerate(subtitle_array[:display_count], 1):
timestamp = timestamp_entry['timestamp']
contents = timestamp_entry['contents']
formatted_text += f" {i}. {timestamp:.2f}s:\n"
# 显示该时间戳下的所有字幕（第二层）
for j, content in enumerate(contents, 1):
text = content['text']
bbox = content['bbox']
formatted_text += f" {j}. [{timestamp:.2f}s|{confidence:.3f}]: {text}\n"
# 如果有位置信息，显示bbox（第三层）
if bbox:
formatted_text += f" 位置: {bbox}\n"
formatted_text += "\n"
if len(subtitle_array) > display_count:
formatted_text += f" ... (还有{len(subtitle_array) - display_count}个时间戳)\n"
# 返回三层嵌套数组结构
return formatted_text, subtitle_array
return formatted_text, []
def merge_and_filter_subtitles(subtitle_array, iou_threshold=0.7, text_similarity_threshold=0.7):
"""
合并并过滤字幕内容，去除重复和空内容，返回格式化字符串和处理后的数组
"""
# 深拷贝，避免原地修改
import copy
subtitle_array = copy.deepcopy(subtitle_array)
formatted_text = []
for i in range(len(subtitle_array)):
for j in range(len(subtitle_array[i]["contents"])):
# 修复：确保i+k不会超出数组范围
for k in range(1, len(subtitle_array) - i): # 从1开始，避免自己和自己比较
if i + k >= len(subtitle_array): # 安全检查
break
for l in range(len(subtitle_array[i+k]["contents"])):
text = subtitle_array[i]["contents"][j]["text"]
bbox = subtitle_array[i]["contents"][j]["bbox"]
text_1 = subtitle_array[i+k]["contents"][l]["text"]
bbox_1 = subtitle_array[i+k]["contents"][l]["bbox"]
iou = calculate_iou(bbox, bbox_1)
text_similarity = calculate_text_similarity(text, text_1)
if iou > iou_threshold and text_similarity > text_similarity_threshold:
# 记录需要删除的索引
subtitle_array[i+k]["contents"][l]["text"] = ''
subtitle_array[i]["contents"][j]["timestamp"] += 1
# 删除text为空字符串的contents
for i in range(len(subtitle_array)):
subtitle_array[i]["contents"] = [content for content in subtitle_array[i]["contents"] if content["text"] != '']
# 删除contents为空的时间戳条目
subtitle_array = [entry for entry in subtitle_array if len(entry["contents"]) > 0]
#formatted_text.append("处理后的字幕数组:")
for i, timestamp_entry in enumerate(subtitle_array[:], 1):
formatted_text.append(f"\n开始时间 {timestamp_entry['timestamp']:.2f}s:")
#formatted_text.append(f" 包含 {len(timestamp_entry['contents'])} 个字幕内容")
for j, content in enumerate(timestamp_entry['contents'], 1):
formatted_text.append(f" {j}. 文本: '{content['text']}'")
if content['bbox']:
formatted_text.append(f" 位置: {content['bbox']}")
if 'timestamp' in content and content['timestamp']:
formatted_text.append(f" 结束时间: {content['timestamp']:.2f}s")
#formatted_text.append("\n完整数组结构:")
#formatted_text.append(str(subtitle_array))
return '\n'.join(formatted_text), subtitle_array
ocr_json_path = "/root/autodl-tmp/new_cnocr/哈尔滨_subtitles.json"
ocr_data = read_json_file(ocr_json_path)
pre_data , subtitle_array= format_ocr_json(ocr_data)
iou_threshold = 0.8
text_similarity_threshold = 0.8
a , b = merge_and_filter_subtitles(subtitle_array, iou_threshold, text_similarity_threshold)
#print("\n完整数组结构:")
print(a)
print(b)
# 保存输出结果到txt文件
output_dir = os.path.dirname(ocr_json_path)
output_filename = os.path.splitext(os.path.basename(ocr_json_path))[0] + "_processed.txt"
output_path = os.path.join(output_dir, output_filename)
try:
with open(output_path, 'w', encoding='utf-8') as f:
f.write(a)
print(f"\n处理结果已保存到: {output_path}")
except Exception as e:
print(f"保存文件时出错: {e}")
#验证 "/root/autodl-tmp/douyin_ocr/兰州_subtitles.json" 里面的重复的两个内容，确实是bbox不重叠
# a = [[303, 243], [442, 243], [442, 303], [303, 303]]
# b = [[339, 231], [495, 241], [490, 304], [335, 294]]
# c = [[482, 273], [660, 276], [660, 303], [481, 300]]
# d = [[536, 268], [732, 273], [731, 300], [535, 295]]
# iou = calculate_iou(a,b) # 0.47
# d = calculate_iou(c,d) # 0.40

127
code/token_counter.py Normal file
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import tiktoken
import os
import cv2
def count_tokens(text, model="gpt-4"):
"""统计文本的token数量"""
try:
encoding = tiktoken.encoding_for_model(model)
tokens = encoding.encode(text)
return len(tokens)
except Exception as e:
print(f"Token统计出错: {e}")
# 简单估算：中文字符约1.5个token，英文单词约1.3个token
chinese_chars = sum(1 for char in text if '\u4e00' <= char <= '\u9fff')
english_words = len([word for word in text.split() if word.isascii()])
estimated_tokens = int(chinese_chars * 1.5 + english_words * 1.3)
return estimated_tokens
def get_video_token_estimate(video_path):
"""估算视频的token数量（基于文件大小和时长）"""
try:
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
return {'estimated_tokens': 0, 'duration': 0, 'frame_count': 0, 'fps': 0, 'file_size_mb': 0, 'frames_used': 0}
# 获取视频信息
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration = frame_count / fps if fps > 0 else 0
# 获取文件大小
file_size = os.path.getsize(video_path)
cap.release()
# 基于GPT-4V的token估算规则
# 视频token = 基础token + 帧数 * 每帧token
base_tokens = 85 # 基础token
frames_per_second = min(fps, 1) # 每秒最多1帧
total_frames = min(frame_count, int(duration * frames_per_second))
tokens_per_frame = 170 # 每帧约170个token
estimated_tokens = base_tokens + total_frames * tokens_per_frame
return {
'estimated_tokens': int(estimated_tokens),
'duration': duration,
'frame_count': frame_count,
'fps': fps,
'file_size_mb': file_size / (1024 * 1024),
'frames_used': total_frames
}
except Exception as e:
print(f"视频token估算出错: {e}")
return {'estimated_tokens': 0, 'duration': 0, 'frame_count': 0, 'fps': 0, 'file_size_mb': 0, 'frames_used': 0}
def analyze_input_tokens(video_path, text_content="", prompt_text=""):
"""分析输入token统计"""
print("\n" + "="*50)
print("📊 Token统计信息:")
print("="*50)
# 统计视频token
video_token_info = get_video_token_estimate(video_path)
print(f"🎬 视频Token统计:")
print(f" 估算Token数量: {video_token_info['estimated_tokens']:,}")
print(f" 视频时长: {video_token_info['duration']:.2f}秒")
print(f" 总帧数: {video_token_info['frame_count']:,}")
print(f" 帧率: {video_token_info['fps']:.2f} fps")
print(f" 文件大小: {video_token_info['file_size_mb']:.2f} MB")
print(f" 使用帧数: {video_token_info['frames_used']:,}")
# 统计文本token
text_tokens = 0
if text_content.strip():
text_tokens = count_tokens(text_content)
print(f"\n📝 文本Token统计:")
print(f" 文本内容Token: {text_tokens:,}")
print(f" 文本字符数: {len(text_content):,}")
# 统计提示词token
prompt_tokens = 0
if prompt_text.strip():
prompt_tokens = count_tokens(prompt_text)
print(f" 提示词Token: {prompt_tokens:,}")
video_cost = 0.0015
text_cost = 0.0004
total_cost = (video_token_info['estimated_tokens']*video_cost + text_tokens*text_cost + prompt_tokens*text_cost)/1000
# 计算总输入token
total_input_tokens = (video_token_info['estimated_tokens'] + text_tokens + prompt_tokens)
print(f"\n📈 总输入Token统计:")
print(f" 视频Token: {video_token_info['estimated_tokens']:,}")
print(f" 文本Token: {text_tokens:,}")
print(f" 提示词Token: {prompt_tokens:,}")
print(f" 🔥 总输入Token: {total_input_tokens:,}")
print(f" 💰 总费用: {total_cost:.4f}元")
print("="*50)
return {
'video_tokens': video_token_info['estimated_tokens'],
'text_tokens': text_tokens,
'prompt_tokens': prompt_tokens,
'total_input_tokens': total_input_tokens,
'video_info': video_token_info,
'total_cost': total_cost
}
if __name__ == "__main__":
# 测试token统计功能
test_video = "/root/autodl-tmp/new/哈尔滨.mp4"
test_text = "这是一个测试文本，包含中英文内容。This is a test text with Chinese and English content."
test_prompt = "请分析这个视频的内容。"
result = analyze_input_tokens(test_video, test_text, test_prompt)
print(f"\n测试结果: {result}")
# video_token = result['video_tokens']
# video_cost = 0.0015
# prompt_token = result['prompt_tokens']
# text_token = result['text_tokens']
# text_cost = 0.0004
# total_cost = video_token*video_cost + prompt_token*text_cost + text_token*text_cost
# print(total_cost)

292
requirements.txt Normal file
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@ -0,0 +1,292 @@
absl-py==2.1.0
aiohappyeyeballs==2.6.1
aiohttp==3.12.11
aiosignal==1.3.2
airportsdata==20250523
anaconda-anon-usage @ file:///croot/anaconda-anon-usage_1710965072196/work
annotated-types==0.7.0
anyio==4.6.2.post1
archspec @ file:///croot/archspec_1709217642129/work
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
astor==0.8.1
asttokens==2.4.1
async-lru==2.0.4
attrs==24.2.0
babel==2.16.0
beautifulsoup4==4.12.3
blake3==1.0.5
bleach==6.2.0
boltons @ file:///work/perseverance-python-buildout/croot/boltons_1698851177130/work
Brotli @ file:///croot/brotli-split_1714483155106/work
cachetools==6.0.0
certifi==2025.6.15
cffi @ file:///croot/cffi_1714483155441/work
charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
click==8.2.1
cloudpickle==3.1.1
cnocr==2.3.1
cnstd==1.2.5.2
coloredlogs==15.0.1
comm==0.2.2
compressed-tensors==0.9.4
conda @ file:///croot/conda_1714403036266/work
conda-content-trust @ file:///croot/conda-content-trust_1714483159009/work
conda-libmamba-solver @ file:///croot/conda-libmamba-solver_1706733287605/work/src
conda-package-handling @ file:///croot/conda-package-handling_1714483155348/work
conda_package_streaming @ file:///work/perseverance-python-buildout/croot/conda-package-streaming_1698847176583/work
contourpy==1.3.1
cryptography @ file:///croot/cryptography_1714660666131/work
cupy-cuda12x==13.4.1
cycler==0.12.1
debugpy==1.8.9
decorator==5.1.1
decord==0.6.0
defusedxml==0.7.1
depyf==0.18.0
dill==0.4.0
diskcache==5.6.3
distro @ file:///croot/distro_1714488253808/work
dnspython==2.7.0
einops==0.8.1
email_validator==2.2.0
executing==2.1.0
fake-useragent==2.2.0
fastapi==0.115.12
fastapi-cli==0.0.7
fastjsonschema==2.20.0
fastrlock==0.8.3
filelock==3.16.1
flatbuffers==25.2.10
fonttools==4.55.0
fqdn==1.5.1
frozenlist==1.6.2
fsspec==2024.10.0
gguf==0.17.0
gitdb==4.0.12
GitPython==3.1.44
googleapis-common-protos==1.70.0
grpcio==1.68.0
h11==0.14.0
hf-xet==1.1.3
httpcore==1.0.7
httptools==0.6.4
httpx==0.27.2
huggingface-hub==0.32.4
humanfriendly==10.0
idna @ file:///croot/idna_1714398848350/work
imageio==2.37.0
importlib_metadata==8.7.0
interegular==0.3.3
ipykernel==6.29.5
ipython==8.29.0
ipywidgets==8.1.5
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.6
jiter==0.10.0
joblib==1.5.1
json5==0.9.28
jsonpatch @ file:///croot/jsonpatch_1714483231291/work
jsonpointer==2.1
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
jupyter-events==0.10.0
jupyter-lsp==2.2.5
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.14.2
jupyter_server_terminals==0.5.3
jupyterlab==4.3.1
jupyterlab-language-pack-zh-CN==4.2.post3
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
kiwisolver==1.4.7
lark==1.2.2
lazy_loader==0.4
libmambapy @ file:///croot/mamba-split_1714483352891/work/libmambapy
lightning-utilities==0.14.3
llguidance==0.7.29
llvmlite==0.44.0
lm-format-enforcer==0.10.11
lxml==5.4.0
Markdown==3.7
markdown-it-py==3.0.0
MarkupSafe==3.0.2
matplotlib==3.9.2
matplotlib-inline==0.1.7
mdurl==0.1.2
menuinst @ file:///croot/menuinst_1714510563922/work
mistral_common==1.5.6
mistune==3.0.2
modelscope==1.26.0
more-itertools==10.7.0
motmetrics==1.4.0
mpmath==1.3.0
msgpack==1.1.0
msgspec==0.19.0
multidict==6.4.4
nbclient==0.10.0
nbconvert==7.16.4
nbformat==5.10.4
nest-asyncio==1.6.0
networkx==3.4.2
ninja==1.11.1.4
notebook_shim==0.2.4
numba==0.61.2
numpy==2.1.3
nvidia-cublas-cu12==12.6.4.1
nvidia-cuda-cupti-cu12==12.6.80
nvidia-cuda-nvrtc-cu12==12.6.77
nvidia-cuda-runtime-cu12==12.6.77
nvidia-cudnn-cu12==9.5.1.17
nvidia-cufft-cu12==11.3.0.4
nvidia-cufile-cu12==1.11.1.6
nvidia-curand-cu12==10.3.7.77
nvidia-cusolver-cu12==11.7.1.2
nvidia-cusparse-cu12==12.5.4.2
nvidia-cusparselt-cu12==0.6.3
nvidia-nccl-cu12==2.26.2
nvidia-nvjitlink-cu12==12.6.85
nvidia-nvtx-cu12==12.6.77
onnx==1.18.0
onnxruntime==1.22.0
openai==1.84.0
openai-whisper==20240930
opencv-python==4.11.0.86
opencv-python-headless==4.11.0.86
opentelemetry-api==1.34.0
opentelemetry-exporter-otlp==1.34.0
opentelemetry-exporter-otlp-proto-common==1.34.0
opentelemetry-exporter-otlp-proto-grpc==1.34.0
opentelemetry-exporter-otlp-proto-http==1.34.0
opentelemetry-proto==1.34.0
opentelemetry-sdk==1.34.0
opentelemetry-semantic-conventions==0.55b0
opentelemetry-semantic-conventions-ai==0.4.9
outcome==1.3.0.post0
outlines==0.1.11
outlines_core==0.1.26
overrides==7.7.0
packaging @ file:///croot/packaging_1710807400464/work
pandas==2.3.0
pandocfilters==1.5.1
parso==0.8.4
partial-json-parser==0.2.1.1.post5
pexpect==4.9.0
pillow==11.0.0
platformdirs @ file:///work/perseverance-python-buildout/croot/platformdirs_1701732573265/work
pluggy @ file:///work/perseverance-python-buildout/croot/pluggy_1698805497733/work
prometheus-fastapi-instrumentator==7.1.0
prometheus_client==0.21.0
prompt_toolkit==3.0.48
propcache==0.3.1
protobuf==5.28.3
psutil==6.1.0
ptyprocess==0.7.0
pure_eval==0.2.3
py-cpuinfo==9.0.0
pyclipper==1.3.0.post6
pycosat @ file:///croot/pycosat_1714510623388/work
pycountry==24.6.1
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
pydantic==2.11.5
pydantic_core==2.33.2
Pygments==2.18.0
pyparsing==3.2.0
PySocks @ file:///work/perseverance-python-buildout/croot/pysocks_1698845478203/work
python-dateutil==2.9.0.post0
python-dotenv==1.1.0
python-json-logger==2.0.7
python-multipart==0.0.20
pytorch-lightning==2.5.1.post0
pytz==2025.2
PyYAML==6.0.2
pyzmq==26.2.0
rapidocr-onnxruntime==1.3.25
ray==2.46.0
referencing==0.35.1
regex==2024.11.6
requests @ file:///croot/requests_1707355572290/work
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==14.0.0
rich-toolkit==0.14.7
rpds-py==0.21.0
ruamel.yaml @ file:///work/perseverance-python-buildout/croot/ruamel.yaml_1698863605521/work
safetensors==0.5.3
scikit-image==0.25.2
scikit-learn==1.7.0
scipy==1.15.3
seaborn==0.13.2
selenium==4.33.0
Send2Trash==1.8.3
sentencepiece==0.2.0
sentry-sdk==2.29.1
setproctitle==1.3.6
setuptools==79.0.1
shapely==2.1.1
shellingham==1.5.4
six==1.16.0
smmap==5.0.2
sniffio==1.3.1
sortedcontainers==2.4.0
soundfile==0.13.1
soupsieve==2.6
stack-data==0.6.3
starlette==0.46.2
supervisor==4.2.5
sympy==1.14.0
tensorboard==2.18.0
tensorboard-data-server==0.7.2
terminado==0.18.1
threadpoolctl==3.6.0
tifffile==2025.6.11
tiktoken==0.9.0
tinycss2==1.4.0
tokenizers==0.21.1
torch==2.7.0
torchaudio==2.7.0
torchmetrics==1.7.2
torchvision==0.22.0
tornado==6.4.2
tqdm @ file:///croot/tqdm_1714567712644/work
traitlets==5.14.3
transformers==4.52.4
trio==0.30.0
trio-websocket==0.12.2
triton==3.3.0
truststore @ file:///work/perseverance-python-buildout/croot/truststore_1701735771625/work
typer==0.16.0
types-python-dateutil==2.9.0.20241003
typing-inspection==0.4.1
typing_extensions==4.13.2
tzdata==2025.2
ultralytics==8.3.153
ultralytics-thop==2.0.14
Unidecode==1.4.0
uri-template==1.3.0
urllib3==2.4.0
uvicorn==0.34.3
uvloop==0.21.0
vllm==0.9.0.1
wandb==0.20.1
watchfiles==1.0.5
wcwidth==0.2.13
webcolors==24.11.1
webdriver-manager==4.0.2
webencodings==0.5.1
websocket-client==1.8.0
websockets==15.0.1
Werkzeug==3.1.3
wheel==0.43.0
widgetsnbextension==4.0.13
wsproto==1.2.0
xformers==0.0.30
xgrammar==0.1.19
xmltodict==0.14.2
yarl==1.20.0
zipp==3.23.0
zstandard @ file:///croot/zstandard_1714677652653/work

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aiohappyeyeballs==2.6.1
aiohttp==3.12.11
aiosignal==1.3.2
aliyun-python-sdk-core==2.16.0
aliyun-python-sdk-kms==2.16.5
annotated-types==0.7.0
antlr4-python3-runtime==4.9.3
anyio==4.9.0
astor==0.8.1
attrs==25.3.0
audioread==3.0.1
cachetools==6.0.0
certifi==2025.4.26
cffi==1.17.1
chardet==5.2.0
charset-normalizer==3.4.2
click==8.2.1
cnocr==2.3.1
cnstd==1.2.5.2
coloredlogs==15.0.1
colorlog==6.9.0
contourpy==1.3.2
crcmod==1.7
cryptography==45.0.4
cssselect==1.3.0
cssutils==2.11.1
cycler==0.12.1
dataclasses-json==0.6.7
decorator==5.2.1
distro==1.9.0
easyocr==1.7.2
editdistance==0.8.1
einops==0.8.1
et_xmlfile==2.0.0
filelock==3.18.0
flatbuffers==25.2.10
fonttools==4.58.2
frozenlist==1.7.0
fsspec==2025.5.1
ftfy==6.3.1
funasr==1.2.6
gitdb==4.0.12
GitPython==3.1.44
GPUtil==1.4.0
greenlet==3.2.3
h11==0.16.0
hf-xet==1.1.3
httpcore==1.0.9
httpx==0.28.1
huggingface-hub==0.32.4
humanfriendly==10.0
hydra-core==1.3.2
idna==3.10
imageio==2.37.0
imagesize==1.4.1
jaconv==0.4.0
jamo==0.4.1
jieba==0.42.1
Jinja2==3.1.6
jiter==0.10.0
jmespath==0.10.0
joblib==1.5.1
jsonpatch==1.33
jsonpointer==3.0.0
kaldiio==2.18.1
kiwisolver==1.4.8
langchain==0.2.17
langchain-community==0.2.17
langchain-core==0.2.43
langchain-openai==0.1.25
langchain-text-splitters==0.2.4
langsmith==0.1.147
lazy_loader==0.4
Levenshtein==0.27.1
librosa==0.11.0
lightning-utilities==0.14.3
llvmlite==0.44.0
lxml==5.4.0
MarkupSafe==3.0.2
marshmallow==3.26.1
matplotlib==3.10.3
modelscope==1.26.0
more-itertools==10.7.0
mpmath==1.3.0
msgpack==1.1.0
multidict==6.4.4
mypy_extensions==1.1.0
networkx==3.5
ninja==1.11.1.4
numba==0.61.2
numpy==1.26.4
nvidia-cublas-cu12==12.6.4.1
nvidia-cuda-cupti-cu12==12.6.80
nvidia-cuda-nvrtc-cu12==12.6.77
nvidia-cuda-runtime-cu12==12.6.77
nvidia-cudnn-cu12==9.5.1.17
nvidia-cufft-cu12==11.3.0.4
nvidia-cufile-cu12==1.11.1.6
nvidia-curand-cu12==10.3.7.77
nvidia-cusolver-cu12==11.7.1.2
nvidia-cusparse-cu12==12.5.4.2
nvidia-cusparselt-cu12==0.6.3
nvidia-nccl-cu12==2.26.2
nvidia-nvjitlink-cu12==12.6.85
nvidia-nvtx-cu12==12.6.77
omegaconf==2.3.0
onnx==1.18.0
onnxruntime==1.22.0
openai==1.63.2
openai-whisper==20240930
opencv-contrib-python==4.10.0.84
opencv-python==4.11.0.86
opencv-python-headless==4.11.0.86
openpyxl==3.1.5
opt-einsum==3.3.0
orjson==3.10.18
oss2==2.19.1
packaging==24.2
paddleocr==3.0.1
paddlepaddle==3.0.0
paddlepaddle-gpu==2.6.2
paddlex==3.0.1
pandas==1.5.3
pillow==11.2.1
platformdirs==4.3.8
pooch==1.8.2
premailer==3.10.0
prettytable==3.16.0
propcache==0.3.2
protobuf==6.31.1
psutil==7.0.0
py-cpuinfo==9.0.0
pyclipper==1.3.0.post6
pycparser==2.22
pycryptodome==3.23.0
pydantic==2.11.5
pydantic_core==2.33.2
pynndescent==0.5.13
pyparsing==3.2.3
pypdfium2==4.30.1
python-bidi==0.6.6
python-dateutil==2.9.0.post0
pytorch-lightning==2.5.1.post0
pytorch-wpe==0.0.1
pytz==2025.2
PyYAML==6.0.2
RapidFuzz==3.13.0
rapidocr-onnxruntime==1.3.25
regex==2024.11.6
requests==2.32.4
requests-toolbelt==1.0.0
resampy==0.4.3
ruamel.yaml==0.18.14
ruamel.yaml.clib==0.2.12
scikit-image==0.25.2
scikit-learn==1.7.0
scipy==1.15.3
seaborn==0.13.2
sentencepiece==0.2.0
sentry-sdk==2.29.1
setproctitle==1.3.6
setuptools==78.1.1
shapely==2.1.1
six==1.17.0
smmap==5.0.2
sniffio==1.3.1
soundfile==0.13.1
soxr==0.5.0.post1
SQLAlchemy==2.0.41
sympy==1.14.0
tenacity==8.5.0
tensorboardX==2.6.2.2
threadpoolctl==3.6.0
tifffile==2025.6.1
tiktoken==0.9.0
tokenizers==0.19.1
torch==2.7.1
torch-complex==0.4.4
torchaudio==2.7.1
torchmetrics==1.7.2
torchvision==0.22.1
tqdm==4.67.1
triton==3.3.1
typing-inspect==0.9.0
typing-inspection==0.4.1
typing_extensions==4.14.0
ujson==5.10.0
ultralytics==8.3.153
ultralytics-thop==2.0.14
umap-learn==0.5.7
Unidecode==1.4.0
urllib3==2.4.0
wandb==0.20.1
wcwidth==0.2.13
wheel==0.45.1
whisper==1.1.10
yarl==1.20.0