Hire Whisper Developers for Speech-to-Text and Voice AI Applications

You share the speech work you need on a short call, review the vetted Whisper developers we shortlist, and choose who joins your team. Vetted candidates are presented within a week, and onboarding completes in 7 to 14 business days. To speed up the match, tell us whether the product is already in production, the problems you face and what has been tried, the work needed and any regulation or process it must follow, and the features or bugs to fix, so we can match the experience the work needs.

AI and ML roles run $30 to $60 an hour depending on seniority, and fixed scope projects have a $10,000 minimum. If your app calls the OpenAI API for transcription, that usage is billed separately by OpenAI per minute of audio. If you self host the open source Whisper model instead, you pay no usage fee to OpenAI, and the main running cost becomes the GPU servers you run it on.

Our hiring process for Whisper developers includes a 60 to 90 minute live coding round on Whisper, followed by scenario based questions on practical Whisper applications such as batch transcription, live captioning and self hosted deployment. Candidates who pass walk us through a recent production project and clear a third party background verification. New engineers then serve a probation period, and those who do not meet our bar do not continue with us. You interview shortlisted candidates yourself before anyone joins your team.

Whisper integration developers connect speech recognition to your product, turning raw audio into transcripts, subtitles or data your application can use. The work usually covers audio preprocessing with FFmpeg, splitting long recordings to fit the 25 MB upload limit of the OpenAI transcription API, and calling either that API or a self hosted Whisper endpoint. Where you need them, they add speaker labels from a separate diarization model such as pyannote, format the output as SRT or WebVTT subtitles and pass the transcript to an LLM for summaries and action items.

Use the OpenAI API when you want a managed service with no servers to run, and self host the open source Whisper model when audio must stay on your own infrastructure or your volume makes your own GPU servers cheaper than paying per minute. On the API, OpenAI notified developers on 26 August 2026 that the whisper-1 model shuts down on 26 February 2027, and it recommends gpt-transcribe for recorded audio and gpt-live-transcribe for live audio. The shutdown does not affect the open source model, whose code and model weights are released under the MIT License, and running it yourself keeps the word timestamps, SRT and WebVTT subtitles and, on its multilingual models, the translation into English that OpenAI serves through whisper-1 today, though your team then owns the GPUs, scaling and updates. If your app calls whisper-1 today, our developers can move it to a supported model or a self hosted deployment and compare transcripts on your own audio before the switch.

Whisper approaches human accuracy and robustness on general English speech, according to the OpenAI research paper. OpenAI says Whisper supports 98 languages but accuracy varies by language, and its model card reports strong results in about 10 of them. For domain specific content (medical, legal, technical), we add custom post processing and vocabulary correction that fixes the terms a general model gets wrong out of the box.

Not on its own, but near real time transcription is possible with the right architecture. OpenAI says Whisper models cannot be used for real time transcription out of the box, so we build streaming pipelines that process audio in chunks. For live captioning, we use optimized Whisper variants and GPU acceleration to keep the delay low. For batch processing, we handle large audio libraries efficiently with queued processing and parallel workers.