Node Inputs
Required Fields
- Audio File: The audio recording to be transcribed
- Supported Formats: mp3, mp4, mpeg, mpga, m4a, wav, webm, mov
- Maximum File Size: 25MB
Optional Fields
- Use Link: When enabled, allows you to provide a URL to the audio file instead of uploading
- Link: The URL of the audio file to transcribe (required if “Use Link” is enabled)
- Model: The AI system used for transcription
- Current available model: OpenAI Whisper
Node Output
- Transcript: The transcribed text content from the audio file
Node Functionality
The Speech to Text node leverages OpenAI’s Whisper model to:- Convert spoken words in audio files into accurate text
- Maintain proper punctuation and capitalization
- Recognize and transcribe multiple languages
- Process various audio formats and quality levels
- Support batch processing via Loop Mode
OpenAI Whisper Limitations
The current implementation uses OpenAI’s Whisper model, which has the following limitations:-
File Size Restriction:
- Maximum file size: 25MB
-
Accuracy Factors:
- Audio quality affects transcription accuracy
- Background noise can reduce precision
- Heavy accents may result in lower accuracy
- Technical or specialized terminology may not be recognized correctly
When to Use
The Speech to Text node is ideal when you need to:Convert Recordings
- Meeting recordings for documentation
- Interview audio for research
- Voice notes for personal productivity
- Lecture content for educational materials
Create Documentation
- Generate meeting minutes automatically
- Create searchable interview transcripts
- Produce podcast transcripts for accessibility
- Develop text-based course materials
Process Audio Content
- Extract information from audio messages
- Make audio content searchable
- Prepare data for sentiment analysis
- Archive spoken information in text format
Common Use Cases
| Use Case | Input | Output | Business Value |
|---|---|---|---|
| Meeting Documentation | Weekly meeting recording (MP3/MP4) | Complete text transcript | Searchable records, easier follow-up, team alignment |
| Content Repurposing | Podcast or video content | Text for articles, blog posts | Content multiplier, improved SEO, wider audience reach |
| Customer Research | Interview recordings | Text for analysis | Easier pattern recognition, quote extraction, theme identification |
| Legal Documentation | Recorded statements | Written documentation | Compliance records, searchable archives, supporting evidence |
Example Flow: Podcast Content Repurposing
This flow automatically:- Reads podcast audio files
- Transcribes them to text
- Creates a summary and extracts key topics
- Outputs to Google Docs and Airtable for content planning
Credits Usage
- The Speech to Text node consumes 20 credits per run
- Loop Mode processing uses credits for each file processed