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# 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, categoryRuntimeWarning) 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, codecpcm_s16le, fps16000) 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, dtypenp.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( audioTEMP_WAV, languagezh, verboseFalse, word_timestampsTrue ) # 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, encodingutf-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}) 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, categoryRuntimeWarning) 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, codecpcm_s16le, fps16000) 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, dtypenp.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( audioTEMP_WAV, languagezh, verboseFalse, word_timestampsTrue ) # 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, encodingutf-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})