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Transcribe podcast audio to text
English Speech 2 Text

English Speech 2 Text

preparing for fine tuning with Khmer dataset

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What is English Speech 2 Text ?

English Speech 2 Text is a transcription tool designed to convert spoken English audio into written text. It leverages the Whisper model to provide accurate and efficient transcription services. The tool is particularly focused on transcribing podcast audio and is currently preparing for fine-tuning with a Khmer dataset, indicating future support for additional languages.

Features

• Advanced transcription using Whisper model: High-quality audio-to-text conversion for English speech.
• Podcast audio support: Tailored for transcribing long-form audio content like podcasts.
• Preparation for Khmer dataset fine-tuning: Future readiness for multilingual transcription capabilities.
• Real-time transcription: Ability to transcribe audio as it is being spoken.
• High accuracy: The Whisper model ensures precise conversion of speech to text.
• Integration-friendly: Can be easily integrated into existing workflows for seamless transcription.

How to use English Speech 2 Text ?

  1. Install and load the Whisper model: Ensure the model is properly set up for transcription tasks.
  2. Load the audio file: Upload or input the English audio file you wish to transcribe.
  3. Start transcription: Use the tool to process the audio and generate text output.
  4. Review the transcription: Edit or format the generated text as needed.
  5. Save the result: Export the text for further use in documents or other applications.

Note: Ensure the audio file is in a supported format (e.g., WAV, MP3).
For real-time transcription, input audio as it is being recorded.

Frequently Asked Questions

1. What audio formats does English Speech 2 Text support?
English Speech 2 Text supports common formats like WAV, MP3, and FLAC.

2. Can it transcribe audio in real-time?
Yes, the tool supports real-time transcription for live audio input.

3. Will it support other languages besides English?
Currently, it focuses on English, but fine-tuning with a Khmer dataset is in preparation, indicating future multilingual capabilities.

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