4.5
1,656 条评分
★★★★☆
2025年12月24日
2023年7月28日
更新内容
v1.75.6We fixed several minor issues related to local file transcription and improved overall stability. Transcription should now work more reliably across different local audio files.
应用信息
- 开发者
- ZipoApps
- 分类
- Utilities
- 价格
- 免费
- 版本
- 1.75.6
- App ID
- 6451474307
简介
Experience the future of digital assistance with the Summarizer: AI transcript and summarizing tool, your go-to app for turning audio and video into text, transcribing with precision and summarizing recordings. Whether it's lectures, meetings, or YouTube content, our app delivers clear, concise transcriptions and summaries swiftly. Utilizing cutting-edge GPT technology, the Summarizer transforms speech into structured text, offering a digestible summary that captures key points, saving you hours of playback. This tool is invaluable for learners, professionals, project managers and podcasters, who need to extract important information without the tedium of manual transcription and summarisation.. Forget about the frustration of rewinding and pausing—upload or link your audio/video, and our app provides a written version in minutes. What's more, it identifies actionable items, streamlining your workflow and ensuring you never miss a task. Key Features: - Advanced GPT-powered audio and video to text conversion. - Quick summarizing for efficient understanding. - Seamless transcribing of video and audio recordings, including YouTube clips through the link. - Intelligent extraction of key points and tasks. - Fast, shareable transcripts perfect for collaboration. - User-friendly interface for uploading and linking files directly. Summariser not only simplifies your workload but elevates your productivity. It’s more than a transcription app — it’s your personal AI summarizer, ready to enhance how you work and learn. Download now and revolutionize your information management! Terms of Use: https://summapp.io/terms Privacy Policy: https://summapp.io/privacy
下载量预测
预估总下载量
3K
低 / 月
5K
预估 / 月
10K
高 / 月
基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。
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