Transcribe Audio Files with OpenAI Whisper
2 min read
8 months ago
Published on May 05, 2024
This response is partially generated with the help of AI. It may contain inaccuracies.
Table of Contents
How to Easily Transcribe Audio Files Using OpenAI Whisper in Python
Step 1: Install OpenAI Whisper Package
- Install the OpenAI Whisper package by running the following command:
pip install openai-whisper
. - Import the package in your Python script using
import whisper
.
Step 2: Load the Audio File
- Ensure you have an audio file that you want to transcribe. For example, you can use a file named
sound.mp3
. - Load the base model using
whisper
by runningmodel = whisper.load_model('base')
.
Step 3: Transcribe the Audio File
- Transcribe the audio file by running
transcription = model.transcribe('sound.mp3')
. - Create a new file named
transcription.txt
in write mode usingwith open('transcription.txt', 'w') as file:
. - Write the transcribed text into the file using
file.write(transcription)
.
Step 4: Review and Edit the Transcription
- Open the
transcription.txt
file to review the transcribed text. - Check for any inaccuracies, especially with special names or technical terms that may not be recognized correctly.
- Make manual adjustments as needed to improve the accuracy of the transcription.
Step 5: Finalize the Transcription
- Ensure the transcribed text is accurate and suitable for your use case, such as creating subtitles or conducting machine learning tasks.
- Save and use the transcribed text as needed for your project.
Additional Notes:
- OpenAI Whisper does not require API keys and can be used locally without the need for tokens.
- The transcription quality is high, but special names or technical terms may require manual adjustments for accuracy.
- Ensure your hardware meets the requirements for running OpenAI Whisper efficiently.
- Experiment with different audio files and review the transcriptions to ensure accuracy.
By following these steps, you can easily transcribe audio files using OpenAI Whisper in Python for various applications such as creating subtitles or conducting machine learning tasks.