Speech-to-TextTranscription
Searchable, speaker-labelled transcripts from calls, interviews and recordings, including Urdu and English spoken in the same sentence.
Speech-to-Text Transcription: what the work involves
Hours of audio pile up in most organisations: sales calls, customer support lines, interviews, lectures, voice notes sent over WhatsApp. Nobody has time to relisten, so knowledge in those recordings is effectively lost. Manual transcription is slow, and outsourced typing of Urdu or mixed-language audio is inconsistent and awkward to search.
We build a processing pipeline around the audio you actually have. Files arrive from a telephony system, a folder or an app upload, are converted and cleaned, and split into chunks. A speech model such as Whisper, or a cloud service where suitable, produces timestamped text, and diarisation labels who is speaking. Domain words, brand names and Urdu terms are supplied as hints, then the text is corrected by a language model against that vocabulary. Transcripts are indexed for search, and low-confidence passages are highlighted for a person to check against the audio. We benchmark on a sample of your recordings and report error rates per language and audio quality.
Core features
Multi-source audio intake
Recordings are collected from telephony systems, WhatsApp exports, uploads or shared folders and normalised.
Timestamped transcripts
Text is linked to time positions, so clicking a line jumps to that moment in the recording.
Speaker labelling
Diarisation separates speakers, such as agent and customer, which makes calls far easier to read.
Custom vocabulary
Product names, people, place names and Urdu terms are supplied as hints and corrected in post-processing.
Searchable archive
Transcripts are indexed so you can search across thousands of recordings by phrase or topic.
Review of uncertain passages
Low-confidence segments are highlighted for a person to verify against the original audio.
What we get right before launch
Audio quality and accents
Phone-line noise, overlapping speakers and regional accents raise error rates sharply. We benchmark on your real recordings and advise on capture changes where they help.
Recording consent
Recording and transcribing calls requires notice or consent, and rules vary between Pakistan, Australia and US states. We build consent prompts and retention limits into the design.
Code-switching
Speakers who move between Urdu and English mid-sentence trip many models. We test mixed samples specifically, choose accordingly, and report the weak spots honestly.
Tools and technology
- Whisper
- Python
- FastAPI
- OpenAI
- Google Speech-to-Text
- Twilio
- PostgreSQL
- pgvector
Common questions, answered
How well does it handle Urdu audio?
Modern models transcribe clear Urdu reasonably, but noisy lines, dialects and mixed English reduce quality. We test a sample of your recordings first and tell you where manual checking is still needed.
Can it run without sending audio to the cloud?
Yes. Open models like Whisper can run on your own servers or on a GPU machine we configure, keeping recordings in-house. Speed and cost depend on volume and hardware.
Can it transcribe live calls?
Streaming transcription is possible with suitable models and telephony integration, with slightly lower accuracy than batch processing. Many teams process recordings shortly after the call ends instead.
Can it separate the agent from the customer?
Yes, either with speaker diarisation or, where your phone system records two channels, by treating each channel as one speaker, which is usually more reliable.
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Ready to start your Speech-to-Text Transcription project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.