Free
$0Free plan available.
Hello Transcribe is a private and secure speech-to-text transcriber that uses OpenAI Whisper and Whisper.cpp. It is available on iPhone, iPad, and Mac. It allows users to transcribe voice notes, audio files, videos, podcasts, or dictate using the microphone. All processing is done on-device for privacy and offline use. Saved results are encrypted in iCloud.
Download Hello Transcribe from the App Store. Use the app to transcribe voice notes, audio files, and videos, or dictate directly using your microphone. Select between fast or accurate transcription modes using various Model Configs. Export your results as VTT or SRT files for video subtitles.
Hello Transcribe is available for use on iPhone, iPad, and Mac.
Yes, because all processing is performed on-device, the app functions offline.
Yes, your data remains private as all processing happens on-device, and your saved results are encrypted in iCloud.
Hello Transcribe supports various audio file formats and MP4 video files. It also supports exporting to VTT and SRT formats.
Yes, the app supports translating audio from multiple languages into English.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Hello Transcribe.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.0 Flash Lite and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
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