hmo
04db423416
- 70 skills with code and documentation - Add .gitignore (ignore __pycache__, output/, temp/, venv/) - Clean up test intermediates and caches
156 lines
3.9 KiB
Python
156 lines
3.9 KiB
Python
"""
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快速转录方案：如果本地Whisper太慢，尝试其他方法
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"""
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import os
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import subprocess
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from pathlib import Path
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import json
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def extract_audio_from_video(video_path):
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"""从视频提取音频"""
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audio_path = video_path.with_suffix(".wav")
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print(f"提取音频: {video_path.name} → {audio_path.name}")
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cmd = [
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"ffmpeg",
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"-y",
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"-i",
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str(video_path),
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"-vn",
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"-acodec",
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"pcm_s16le",
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"-ar",
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"16000",
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"-ac",
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"1",
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str(audio_path),
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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print(f"音频提取失败: {result.stderr}")
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return None
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print(f"✅ 音频提取完成: {audio_path.stat().st_size / 1024 / 1024:.2f} MB")
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return audio_path
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def check_whisper_availability():
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"""检查Whisper是否可用"""
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try:
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import whisper
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print("✅ Whisper已安装")
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return True
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except ImportError:
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print("❌ Whisper未安装")
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return False
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def transcribe_with_whisper(audio_path):
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"""使用Whisper转录"""
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try:
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import whisper
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print("加载Whisper模型...")
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# 使用最小的模型加快速度
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model = whisper.load_model("tiny")
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print("开始转录...")
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result = model.transcribe(str(audio_path), language="zh")
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return result
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except Exception as e:
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print(f"Whisper转录失败: {e}")
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return None
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def save_transcription(result, video_path):
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"""保存转录结果"""
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if not result:
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return False
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# 保存文本
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txt_path = video_path.with_suffix(".txt")
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with open(txt_path, "w", encoding="utf-8") as f:
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f.write(result["text"])
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print(f"✅ 转录文本保存到: {txt_path.name}")
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print(f"文本长度: {len(result['text'])} 字符")
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# 保存完整结果（JSON）
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json_path = video_path.with_suffix(".json")
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f"✅ 完整结果保存到: {json_path.name}")
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# 预览
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print("\n=== 转录预览（前500字符）===")
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preview = result["text"][:500]
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print(preview + "..." if len(result["text"]) > 500 else preview)
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return True
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def main():
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print("=== 视频转录程序 ===")
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# 查找视频文件
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video_dir = Path(r"D:\F\NewI\opencode\daily-workspace\temp")
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video_files = list(video_dir.glob("*.mp4"))
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if not video_files:
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print("❌ 未找到mp4文件")
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return
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video_path = video_files[0]
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print(f"处理视频: {video_path.name}")
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print(f"文件大小: {video_path.stat().st_size / 1024 / 1024:.2f} MB")
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# 检查是否已有转录文件
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txt_path = video_path.with_suffix(".txt")
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if txt_path.exists():
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print(f"✅ 已有转录文件: {txt_path.name}")
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with open(txt_path, "r", encoding="utf-8") as f:
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text = f.read()
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print(f"文本长度: {len(text)} 字符")
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print("\n=== 现有转录预览 ===")
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print(text[:500] + "..." if len(text) > 500 else text)
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return
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# 提取音频
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audio_path = extract_audio_from_video(video_path)
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if not audio_path:
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print("❌ 无法提取音频")
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return
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# 检查Whisper
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if not check_whisper_availability():
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print("请安装Whisper: pip install openai-whisper")
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return
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# 转录
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result = transcribe_with_whisper(audio_path)
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if not result:
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print("❌ 转录失败")
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return
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# 保存结果
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if save_transcription(result, video_path):
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print("\n✅ 转录完成！")
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# 清理临时音频文件
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if audio_path.exists():
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audio_path.unlink()
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print("临时音频文件已删除")
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else:
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print("❌ 保存转录结果失败")
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if __name__ == "__main__":
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main()
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