🎙️ I Built a Local AI Transcriber for a Friend Who Works With Too Much Audio and Video

说明与用途

该应用程序可自动转录音频和视频，文本与媒体同步，并以多种格式导出，局部运行以确保隐私。

问题

They had useful information trapped inside audio and video, but getting that information into searchable, readable text was slow and annoying.

结果

The application reduces transcription effort, supports 99+ languages, offers multiple Whisper model sizes, and allows exporting in several formats while running locally for privacy.

工作流

• Upload or record audio/video
• FFmpeg probes media and extracts/normalizes audio to 16 kHz mono WAV
• Whisper performs speech recognition and generates timestamped transcript
• Server-Sent Events stream progress to browser
• User can view synchronized transcript, seek by timestamp, and export in .txt, .srt, .vtt, .json, or .wav formats

证据

They had useful information trapped inside audio and video, but getting that information into searchable, readable text was slow and annoying.

来源

The application supports both audio and video files. For video, FFmpeg extracts and normalizes the audio into the 16 kHz, 16-bit mono PCM format expected by Whisper before sending it through the speech-to-text pipeline.

来源

With an open model such as Whisper, the application can be built around local inference instead of requiring every recording to leave the user's machine.

来源