MASR

What is MASR ?

MASR is an AI-powered tool designed to transcribe podcast audio into text with high accuracy and efficiency. It leverages advanced speech recognition technology to convert spoken words from audio files into readable text, making it easier to analyze, share, or repurpose content. MASR is tailored for podcasters, content creators, and anyone who needs to transform audio into a usable text format.

Features

• Automatic Transcription: Converts audio files into text with minimal manual intervention.
• Multi-Format Support: Compatible with popular audio formats such as MP3, WAV, and AAC.
• Speaker Identification: Detects and labels different speakers in the audio.
• Customizable Settings: Allows users to adjust transcription accuracy based on specific needs.
• Export Options: Enables easy export of transcribed text in formats like TXT, DOCX, or PDF.
• Integration Capabilities: Can be integrated with other tools and workflows for seamless productivity.
• User-Friendly Interface: Intuitive design for easy navigation and operation.

How to use MASR ?

1. Upload Audio File: Select and upload your podcast audio file to MASR.
2. Choose Preferences: Customize settings such as language, speaker identification, and output format.
3. Start Transcription: Initiate the transcription process and wait for MASR to convert the audio to text.
4. Review and Edit: Once complete, review the transcribed text for accuracy and make necessary edits.
5. Export the Text: Save or export the transcribed text in your preferred format for further use.

Frequently Asked Questions

What audio formats does MASR support?
MASR supports common formats like MP3, WAV, and AAC, ensuring compatibility with most podcast files.

Can I edit the transcribed text directly in MASR?
Yes, MASR allows users to review and edit the transcribed text within the tool before exporting it.

Is there a limit to the size of the audio file I can upload?
While MASR can handle large files, optimal performance is achieved with files up to 2 hours in length. For longer files, consider splitting them before transcription.