MegaTranscript Is Now on Google Play: AI Transcription With Speaker Diarization
MegaTranscript is now available on Google Play, bringing AI transcription with built-in speaker diarization to Android.
Audio and video recordings are everywhere. Meetings are recorded for accountability. Interviews are saved for accuracy. Podcasts and lectures are published and archived. Calls are stored for reference and compliance.
Recording is no longer the problem.
The problem begins after the recording ends.
Raw audio does not scale. Listening takes time. Replaying conversations introduces bias and error. And transcripts without clear speaker attribution quickly become unreliable.
MegaTranscript is designed to solve this gap by converting audio and video into structured, speaker-labeled text that can be read, reviewed, shared, and trusted.
AI Transcription Built Around Speaker Diarization
MegaTranscript performs AI transcription and speaker diarization as a single process.
Speech is converted to text while speakers are automatically detected, separated, and labeled throughout the recording. The result is not just text, but a transcript that reflects the structure of the conversation.
Speaker diarization is not applied afterward.
It is not a toggle.
It is part of how transcription works in MegaTranscript.
This approach is essential for recordings with more than one voice, where meaning depends on attribution.
Why Speaker Diarization Changes the Value of Transcripts
A transcript answers one basic question: what was said.
Speaker diarization answers the question that actually matters: who said it.
Without reliable speaker labeling, transcripts lose practical value:
- In meetings, decisions and action items lose ownership
- In interviews, quotes lose credibility
- In podcasts and lectures, structure is lost, slowing editing and review
Speaker diarization restores context. It turns transcripts into documents that can be read without replaying audio and understood without guessing.
Real-World Situations Where Diarization Matters
Consider a recorded team meeting with six participants.
Without speaker diarization, the transcript becomes a block of text. Reviewing decisions requires replaying the meeting. Accountability depends on memory. Follow-ups become unclear.
With MegaTranscript, each speaker is clearly labeled throughout the transcript. Decisions can be reviewed quickly. Action items are traceable. The transcript replaces the recording.
Now consider an interview.
Journalists and researchers rely on accurate attribution. A single misattributed quote can invalidate an entire piece of work. MegaTranscript’s speaker diarization preserves who said what across long recordings, reducing the risk of error during review.
In podcasts and lectures, speaker diarization provides structure. Hosts, guests, and audience speakers are separated cleanly, making transcripts usable for publishing, editing, and archiving.
These are not edge cases. They are the default conditions of real recordings.
How MegaTranscript Works
MegaTranscript is designed to remove friction from transcription workflows.
The process is simple:
- Upload an audio or video file
- Select Diarize
- Choose the uploaded file and create diarization
- Track progress under Jobs
- Preview or download the finished transcript
AI transcription and speaker diarization run together. There are no configuration steps and no manual adjustments required.
While processing runs, users can monitor progress. When complete, the transcript is immediately available for use.
No Setup, No Voice Training, No Manual Labeling
Many speaker diarization systems require users to provide voice samples or train the system on known speakers. This approach fails in real-world scenarios where participants change frequently.
MegaTranscript does not require voice training.
Each recording is analyzed independently. Speakers are identified based on speech patterns within that file, not on prior knowledge or stored profiles.
This makes MegaTranscript suitable for interviews, meetings with rotating attendees, research sessions, and calls where participants are different every time.
Built for Real-World Audio Conditions
Real recordings are not clean.
People interrupt each other. Voices overlap. Audio quality varies. Speakers change tone, pace, and volume over time.
MegaTranscript is built to handle these conditions without requiring users to edit or prepare files in advance. The system is designed for real conversations, not ideal audio.
The goal is not to make users think about transcription.
The goal is to make transcription disappear.
Accuracy as a Baseline Requirement
Transcription errors compound quickly.
A misheard word can change meaning. A misattributed speaker can invalidate context. Accuracy is not a feature — it is a requirement.
MegaTranscript is designed to deliver 99.99% accuracy under typical conditions, including reliable speaker separation and alignment.
Transcripts are produced with the expectation that they can be trusted immediately, without extensive correction or review.
Why Mobile Matters
Recordings no longer live on desktops.
Meetings are recorded on phones. Interviews happen on the move. Lectures are captured in real time. Conversations are saved where they happen.
By launching on Google Play, MegaTranscript brings AI transcription and speaker diarization directly to Android devices, eliminating unnecessary steps like exporting files or transferring recordings between tools.
Transcription happens where the audio already exists.
Who MegaTranscript Is Built For
MegaTranscript is built for people who rely on conversations:
- Teams documenting meetings and decisions
- Journalists and researchers handling interviews
- Podcasters managing multi-speaker recordings
- Students reviewing lectures
- Professionals reviewing calls and discussions
In each case, speaker diarization transforms recordings into structured information instead of raw audio files.
Available Now on Google Play
MegaTranscript is now available on the Google Play Store as an AI transcription app with automatic speaker diarization for Android.
It converts audio and video into clear, speaker-labeled transcripts designed to replace listening, not add more work after it.