What This Is
We build speech-to-text systems using Deepgram’s API — live transcription that converts audio to text as it happens, and batch processing that transcribes recorded audio files at scale. The output is structured, timestamped text that feeds into your application for search, analysis, summarisation, or compliance archiving. Audio goes in; usable, queryable text comes out.
Live transcription connects via WebSocket to Deepgram’s streaming API, delivering words to your application within milliseconds of them being spoken. This powers real-time captioning, live meeting notes, and call centre dashboards where supervisors see the conversation as it unfolds. Batch transcription processes recorded files — call recordings, meeting recordings, interview audio, podcast episodes — and returns full transcripts with speaker diarisation, timestamps, and confidence scores.
We have deployed Deepgram transcription for processing call recordings where searchability and compliance matter. One integration processes an average of 80 call recordings per day, transcribes them within minutes of the call ending, and stores the structured transcript linked to the customer record in the CRM. Support managers search across all calls by keyword instead of listening to recordings, cutting the time to investigate a complaint from 30 minutes of scrubbing audio to a 10-second text search.
When You Need This
Transcription automation fits when your business generates audio that has value locked inside it — value that is inaccessible until someone listens and writes it down. Call recordings that nobody reviews because there is no time. Meeting recordings that sit in cloud storage because nobody will watch a 45-minute video to find the one decision that was made. Interview audio that requires hours of manual transcription before the insights can be used.
It also applies when you need real-time text output from live audio — accessibility captions for events or webinars, live subtitling for video streams, or real-time conversation analysis in contact centres.
How We Work
We start by identifying the audio sources and the output requirements. Where does the audio come from — a phone system, a video conferencing tool, a recording device, a browser microphone? What format is it in? Where does the transcript need to go — a database, a document, a search index, an AI summarisation pipeline? These answers determine whether we use streaming or batch processing, and what post-processing the transcript needs.
For batch processing, we build a pipeline: audio files land in a processing queue (uploaded, pulled from a recording system, or triggered by a webhook), are sent to Deepgram with the appropriate model and language settings, and the returned transcript is parsed, formatted, and stored. Speaker diarisation identifies who said what. Timestamps enable jumping to specific moments in the original audio. The transcript is indexed for full-text search.
For live transcription, we establish a WebSocket connection to Deepgram’s streaming endpoint and pipe the audio in real time. Interim results arrive within 200-300 milliseconds; final results follow as Deepgram refines its output. Your application receives a continuous stream of text that it can display, store, or process as needed. We handle connection management, reconnection on network interruption, and buffering to ensure no audio is lost during transient failures.
What You Get
- Batch transcription pipeline for recorded audio files with automatic queue processing
- Live streaming transcription via WebSocket with sub-second latency
- Speaker diarisation — identifying and labelling different speakers in the transcript
- Timestamped output enabling navigation from transcript text to audio position
- Full-text search indexing across all transcribed content
- Language and model selection per audio source for accuracy optimisation
- Integration with your existing systems — CRM, file storage, search index, or AI pipeline
- Structured JSON output with confidence scores, word-level timestamps, and speaker labels
Technologies We Use
- Deepgram API — Nova-2 model for high-accuracy transcription, streaming WebSocket and REST batch endpoints, speaker diarisation, and language detection
- Laravel — queue-based batch processing, WebSocket proxy for streaming, webhook handlers for recording system integration
- PostgreSQL — transcript storage with full-text search indexing, speaker metadata, and audio source linking
- Redis — job queue management and streaming session state
Related Systems
Transcription is a data extraction layer that feeds into larger systems. Transcripts from call recordings feed a query management system or compliance archive. Meeting transcripts feed a reporting dashboard or knowledge base. The transcription handles the audio-to-text conversion; the system handles what happens with the text.
Make Your Audio Searchable
If your business records audio that nobody has time to listen to, get in touch and we will build a transcription pipeline that makes it instantly searchable.
