python:speech(MP4) to text

# encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述：pip install moviepy SpeechRecognition pip install openai-whisper move to text # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/6/14 17:40 # User : geovindu # Product : PyCharm # Project : Pysimple # File : MP4totextdest.py import warnings warnings.filterwarnings("ignore") warnings.filterwarnings("ignore", category=RuntimeWarning) from moviepy import VideoFileClip import speech_recognition as sr import os import whisper import numpy as np import wave import audioop import whisper.audio VIDEO_FILE = "20260614_102306.mp4" TEMP_WAV = "temp_audio.wav" OUTPUT_TXT = "完整演讲文稿_带时间戳medium.txt" # 1. 提取视频音频 print("正在提取音频...") video = VideoFileClip(VIDEO_FILE) # 导出16位单声道标准wav，适配识别 video.audio.write_audiofile(TEMP_WAV, codec="pcm_s16le", fps=16000) video.close() # 2. 原生Python读取wav，彻底绕过ffmpeg def load_audio_without_ffmpeg(path): with wave.open(path, 'rb') as wav_file: n_channels = wav_file.getnchannels() width = wav_file.getsampwidth() frames = wav_file.readframes(wav_file.getnframes()) # 双声道转单声道 if n_channels == 2: frames = audioop.tomono(frames, width, 0.5, 0.5) # 转浮点音频数组 audio_np = np.frombuffer(frames, dtype=np.int16).flatten().astype(np.float32) / 32768.0 return audio_np # 覆盖whisper自带读取函数，不再调用ffmpeg import whisper.audio whisper.audio.load_audio = load_audio_without_ffmpeg # 3. 加载最轻量模型 tiny（速度最快，中文演讲够用） print("加载离线语音模型 medium ...") model = whisper.load_model("medium") # 4. 开始识别，开启分段时间戳 print("开始逐段识别演讲内容，请等待...") result = model.transcribe( audio=TEMP_WAV, language="zh", verbose=False, word_timestamps=True ) # 5. 格式化带时间戳文稿，实时打印每一段 full_content = "=== EV录屏 2026年6月13日 第十八届海峡论坛苏恒演讲 完整转写稿 ===\n\n" print("\n====================识别结果====================") for seg in result["segments"]: start_min = int(seg["start"] // 60) start_sec = int(seg["start"] % 60) end_min = int(seg["end"] // 60) end_sec = int(seg["end"] % 60) seg_text = seg["text"].strip() line = f"[{start_min:02d}:{start_sec:02d} - {end_min:02d}:{end_sec:02d}] {seg_text}" print(line) # 实时控制台打印每一段文字 full_content += line + "\n" # 6. 保存本地文本 with open(OUTPUT_TXT, "w", encoding="utf-8") as f: f.write(full_content) # 清理临时音频文件 os.remove(TEMP_WAV) print(f"\n================================================") print(f"✅ 全部识别完成！文稿已保存至：{OUTPUT_TXT}")