Rev AI

Developer-focused speech-to-text API delivering accurate transcriptions at scale.

4.5 (6)

Recensito da Daniel Nikulshyn·Aggiornato maggio 2026

Speech-to-Text Real-Time Voice AI Transcription API Developer Tools Pay-As-You-Go

Panoramica

Rev AI is an automated speech recognition (ASR) platform built on top of Rev's large dataset of human-transcribed audio. It provides REST and streaming APIs that convert recorded or live audio into text, with support for dozens of languages and a range of audio formats. The service is aimed at developers and businesses that need to embed transcription, captioning, or voice analytics into their own products. Features like speaker diarization, custom vocabulary, timestamps, and confidence scores help teams build search, accessibility, compliance, and analytics workflows on top of spoken content. Pricing is usage-based per minute of audio, with a free tier for testing and volume discounts for higher tiers, making it a practical option for both prototypes and production deployments.

Funzionalità chiave

Asynchronous speech-to-text API
Real-time streaming transcription
Speaker diarization and timestamps
Custom vocabulary support
Language identification across multiple languages
Word-level confidence scores

Casi d’uso

Add captions to video platforms

Use the async API to generate accurate transcripts and timestamps for uploaded videos, enabling closed captions and improved accessibility for end users.

Live transcription for meetings and events

Integrate the real-time streaming API to display live captions during webinars, conferences, or virtual meetings with low-latency speech-to-text.

Call analytics for contact centers

Transcribe customer calls with speaker diarization and confidence scores to power search, QA, and compliance analytics on conversational audio.

Searchable podcast and media archives

Convert podcast episodes and media libraries into text with custom vocabulary support, making spoken content discoverable through keyword search.

Pro & contro

Pro

High transcription accuracy backed by human-labeled data
Supports async and real-time streaming APIs
Speaker diarization and custom vocabulary
Clear pay-as-you-go pricing

Contro

Per-minute costs can add up at high volume
Fewer languages than some larger cloud providers
Requires technical integration work

Recensioni

4.5

Media su 6 valutazioni.

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Olga Ivanova

Solid for our team

We rolled this out across the team last quarter and speaker diarization and custom vocabulary. Language identification across multiple languages fits neatly into how we already work, and asynchronous speech-to-text API removed a step we used to do by hand. Per-minute costs can add up at high volume, which is the main caveat, but it has held up under daily use.

Joanna Kowalski

Use it every day

Honestly didn't expect to like it this much. Speaker diarization and timestamps is exactly what I needed, and supports async and real-time streaming APIs. I do wish requires technical integration work, but I reach for it almost every day now and it just clicks.

Camille Laurent

Does the job

Pretty happy overall. Asynchronous speech-to-text API just works and supports async and real-time streaming APIs. Per-minute costs can add up at high volume can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Devin Walker

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on asynchronous speech-to-text API, and high transcription accuracy backed by human-labeled data caught me off guard. still, I'd recommend giving it a real trial.

Ethan Brooks

Years in this space

I've evaluated a lot of these over the years. What stands out here is asynchronous speech-to-text API — handled better than most — and supports async and real-time streaming APIs. Worth the time if this is your use case.

Grace Okafor

Solid for our team

We rolled this out across the team last quarter and clear pay-as-you-go pricing. Language identification across multiple languages fits neatly into how we already work, and asynchronous speech-to-text API removed a step we used to do by hand. Per-minute costs can add up at high volume, which is the main caveat, but it has held up under daily use.

Q&A

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