How to Transcribe Audio for Free Using Google Colab & Whisper (Easy Method)

Converting audio to text has traditionally required expensive software or subscriptions. However, with advances in AI and machine learning, you can now transcribe audio files completely free using powerful open-source tools. This tutorial walks you through using Google Colab and OpenAI's Whisper model to generate accurate transcriptions—without spending a dime.

The Easiest No-Cost Method to Convert Audio to Text

Google Colab provides free access to GPU resources, making it perfect for running AI models that would otherwise require substantial computing power. Combined with OpenAI's Whisper, an advanced speech recognition model, you can transcribe audio files quickly and accurately without specialized hardware.

This method offers several advantages:

Zero cost: No subscriptions or payment required
High accuracy: Leverages state-of-the-art speech recognition
Multiple output formats: Generate plain text or timestamped VTT files
Batch processing: Handle multiple files in one session

The only notable limitations are that you need to keep your Colab session open while processing (files aren't permanently stored) and very large files might take longer to process depending on which Whisper model you select.

Step-by-Step Process for Running Transcription in Google Colab

Let's walk through the entire audio transcription process:

Access Google Colab

First, navigate to Google Colab in your browser. This is Google's free cloud service that allows you to run Python code and access GPU resources.
Create a new notebook

Click on "New notebook" to start from scratch. You'll see a simple interface with a code cell ready for input.
Install the necessary packages

Copy and paste the following code into the first cell and run it by clicking the play button:
```
!pip install -q git+https://github.com/openai/whisper.git

!pip install -q git+https://github.com/guillameleveque/langchain-openai-whisper.git

!pip install -q ffmpeg-python
```
This installs the Whisper model and necessary dependencies. It usually takes about a minute to complete.

TIP: You can move to the next steps while installation is running to save time, especially if you have large audio files to upload.

Upload your audio files

Click on the file icon in the left sidebar to open the file manager. You can drag and drop your audio files directly into this area. Multiple files can be uploaded at once.

Google Colab will display a warning that files are temporary—they'll be removed when the session ends, but you'll download the transcription results before closing.
Run the transcription

Create a new code cell by clicking the "+ Code" button. Then paste the following code, replacing "yourfilename.mp3" with your actual file name:
```
!whisper "yourfilename.mp3" --model small
```
Click the play button to execute the cell. The Whisper model will now process your audio and generate the transcription.

Choosing the Right Whisper Model for Your Transcription Needs

Whisper offers several model sizes, each with different trade-offs between speed and accuracy:

Model	Accuracy	Processing Speed	Use Case
tiny	Lowest	Fastest	Quick drafts, meetings where perfect accuracy isn't critical
base	Low	Very fast	General use when speed matters more than accuracy
small	Medium	Fast	Good balance for most transcription needs
medium	High	Moderate	When accuracy is important but time is limited
large	Highest	Slowest	Critical transcriptions where accuracy is paramount
large-v2	Best	Slowest	Most accurate, best for complex audio or accents

For example, with the "tiny" model, a one-hour recording might take only 5 minutes to process but might miss some words. The "large" model could take 30 minutes for the same file but will produce significantly more accurate results.

To specify which model to use, simply replace "small" in the code with your preferred model size:

!whisper "yourfilename.mp3" --model large

TIP: For YouTube videos or podcast transcriptions where accuracy is crucial, use the "large" model. For quick meeting notes or drafts, the "tiny" or "small" model will save significant time.

Managing Multiple Files and Understanding Output Formats

Working with Multiple Files

If you have several audio files to transcribe, you can set up a queue:

Upload all your files to the Colab environment
Create a new code cell for each file
Run them in sequence, or let them execute automatically one after another

For example:

!whisper "interview1.mp3" --model small

!whisper "meeting_notes.mp3" --model tiny

!whisper "podcast_episode.mp3" --model large

This is perfect for batch processing—you can set up multiple transcription jobs, start them, and return later when they're complete.

Understanding Output Formats

When the transcription finishes, Whisper generates several output files:

TXT: Plain text transcription without timestamps
VTT: Web Video Text Tracks format with timestamps
SRT: SubRip subtitle format
TSV: Tab-separated values format
JSON: Structured data format

For most users, the TXT and VTT files will be most useful. The TXT file gives you just the transcribed text, perfect for summaries or further processing with AI tools. The VTT file includes timestamps showing when each phrase was spoken, which is invaluable for video subtitles or identifying specific moments in long recordings.

TIP: If you plan to use the transcription with AI tools like ChatGPT, download the plain TXT format to avoid wasting token limits on timestamp information unless you specifically need timing data.

To download any file, simply click on it in the file browser. The files will appear in the left sidebar after transcription is complete.

Next Steps

Transcribing audio files is just the beginning of what you can do with this data. Consider these additional processes:

Content analysis: Use Espo.ai's content analysis tools to extract key insights from your newly transcribed text.
SEO optimization: Transform transcriptions into SEO-friendly content with Espo.ai's SEO content generation capabilities.
Summarization: Create concise summaries of long interviews or meetings using AI-powered summarization tools.
Multi-language support: Whisper supports multiple languages, making this method valuable for international content creators.

Free audio transcription using Google Colab and Whisper opens up new possibilities for content creators, researchers, and professionals who need to convert spoken word to text without expensive subscriptions. For more advanced AI and content tools to further enhance your workflow, explore Espo.ai's full suite of solutions.