Accurate speech-to-text transcription for voice & speech workflows
Rev.ai is a speech-to-text API platform delivering automatic and human-reviewed transcription services for developers and teams. It suits product engineers, media producers, and enterprise pipelines that need timestamped, speaker-labeled transcripts with customizable vocabulary; pricing is usage-based with a free trial quota and paid per-minute tiers, making it practical for pay-as-you-go transcription projects.
Rev.ai is an API-first speech-to-text service that converts audio and video into timestamped, speaker-labeled transcripts. Its primary capability is ASR (automatic speech recognition) tuned for media and enterprise use, offering both streaming and batch transcription with custom vocab and diarization. Rev.ai differentiates itself by pairing Rev’s long-established transcription expertise with a developer-focused REST/WebSocket API and options for human review via Rev’s separate services. It targets developers, podcasters, and media teams in the voice & speech category, and is available under a pay-as-you-go pricing model with a free trial quota for new accounts.
Rev.ai is the developer-facing automatic speech recognition (ASR) product from Rev, the company known for human transcription services. Launched by Rev (the parent company) to expose machine transcription via API, Rev.ai positions itself as a scalable speech-to-text engine for apps, media, and enterprise systems. The core value proposition is straightforward: provide accurate, timestamped transcripts with speaker diarization and vocabulary customization while integrating into developer workflows via REST and WebSocket endpoints. Rev.ai leverages acoustic and language models trained on large speech datasets and offers both synchronous (batch) and streaming transcription modes to meet different latency requirements.
Key features include streaming WebSocket real-time transcription for live audio and low-latency use cases, and long-file batch transcription for pre-recorded media up to many hours per file. The API supports speaker diarization (speaker_labels) so transcripts include speaker segments and timestamps, and custom vocabulary / post-processing rules to improve recognition of industry terms, proper names, or product SKUs. Rev.ai returns JSON with word-level timestamps, confidence scores, and alternative hypotheses; it also supports multiple audio codecs and sample rates and provides language and model selection where applicable. Developers can upload files, request captions in formats like VTT/RTT, and use asynchronous job polling or webhooks to integrate results into publishing pipelines.
Pricing for Rev.ai is usage-based and reported on their pricing page. New users get a free trial credit that covers a limited number of minutes for testing (the trial credit amount is provided when you sign up). The paid tier charges per audio minute for automatic transcription (per-minute ASR rate listed on the site) and Rev’s separate human transcription and caption services are priced higher per minute. There is no fixed monthly “Pro” subscription; instead you pay per minute used. Enterprise customers can negotiate committed volume discounts and SLA terms under custom contracts. The model makes Rev.ai attractive to teams that prefer predictable per-minute billing rather than monthly seats.
Rev.ai is used by developers building transcription into apps, media companies automating caption workflows, and enterprises needing searchable audio archives. For example, a product manager at a conference SaaS company uses Rev.ai to produce searchable, timestamped recordings for customer support, while a post-production editor at a video network integrates Rev.ai to auto-generate VTT captions at scale. Journalists and researchers also use it to transcribe interviews quickly. Compared with a competitor like Google Cloud Speech-to-Text, Rev.ai is often chosen for its media-focused features, Rev’s transcription heritage, and straightforward per-minute pricing, though cloud providers may offer broader language and ecosystem integration.
Three capabilities that set Rev.ai apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free Trial | Free | One-time trial credit covering limited minutes for testing | Developers evaluating ASR accuracy and API features |
| Pay-as-you-go (Automatic) | Exact per-minute price on site | Billed per audio minute for ASR, no monthly seats or limits | Teams needing flexible, low-volume transcription |
| Human Transcription | Exact per-minute human rate on site | Human-reviewed transcripts with higher accuracy, billed per minute | Content teams needing near-100% accuracy |
| Enterprise | Custom | Committed minutes, SLAs, dedicated support and integrations | Large organizations requiring volume discounts and SLAs |
Copy these into Rev.ai as-is. Each targets a different high-value workflow.
Role: You are a transcription assistant using Rev.ai to convert meeting audio into a clean, searchable transcript. Constraints: produce speaker-labeled lines, include ISO 8601 timestamps for every speaker turn, normalize filler words (remove 'um', 'uh' unless meaningful), and keep verbatim only for quoted text. Output format: JSON with keys: "transcript" (array of {speaker, start, end, text}), "keywords" (top 10 nouns/phrases). Example output item: {"speaker":"Speaker 1","start":"2026-04-22T10:01:05Z","end":"2026-04-22T10:01:23Z","text":"We should prioritize Q3 roadmap."}. Provide only valid JSON.
Role: You are a captions generator that uses Rev.ai output to create VTT captions for a video editor. Constraints: segments max 42 characters per line, max 2 lines per cue, each cue duration 1–7 seconds, include speaker label at start of cue when a new speaker speaks. Output format: a valid WebVTT string starting with 'WEBVTT' and with cues like '00:00:05.000 --> 00:00:09.000' and speaker prefix '[Host]:'. Example cue: '00:00:05.000 --> 00:00:09.000\n[Host]: Welcome to episode one.' Provide only the VTT text, no extra commentary.
Role: You are a QA analyst using Rev.ai transcripts to score call quality. Constraints: output JSON with: overall_score (0-100), metrics {silence_percent, agent_talk_percent, customer_talk_percent, interruptions_count, sentiment_agent, sentiment_customer}, and flagged_segments array (items with start,end,reason). Use thresholds: silence_percent>10% flagged, interruptions_count>3 flagged, negative sentiment for customer flagged. Output format example: {"overall_score":72,"metrics":{...},"flagged_segments":[{"start":"00:12:05","end":"00:12:20","reason":"customer negative sentiment"}]}. Base scores on talk-time balance, politeness, and issue resolution language. Return only JSON.
Role: You are a podcast editor using Rev.ai transcripts to create chapter markers and episode highlights. Constraints: produce 6–10 chapter markers, each with start timestamp, 12–20 word chapter title, 30–60 word summary, and 5 topic tags. Also generate 3 bullet-point highlights for social copy. Output format: JSON array of chapters and a separate "highlights" array. Example chapter item: {"start":"00:05:30","title":"Hiring for Product","summary":"Short summary of the hiring discussion...","tags":["hiring","recruiting","product"]}. Return only valid JSON.
Role: You are a compliance engineer processing Rev.ai transcripts to detect and redact PII. Multi-step constraints: (1) Identify and categorize PII types (names, SSN, credit card, emails, phone, addresses, DOB, account numbers). (2) Replace each PII instance in transcript with a standardized redaction token like "[REDACTED:SSN]" preserving original token length by masking characters for auditing (e.g., '***-**-6789'). (3) Produce a separate JSON "pii_log" listing original_value (masked), type, speaker, start, end, confidence (0-1). Output format: JSON {"redacted_transcript":string, "pii_log":[]} Example pii_log item: {"original":"***-**-6789","type":"SSN","speaker":"Agent","start":"00:12:10","end":"00:12:12","confidence":0.97}. Return only JSON.
Role: You are a data engineer converting Rev.ai transcripts into a labeled training dataset for ASR model fine-tuning. Constraints: produce newline-delimited JSON (NDJSON); each record must include "audio_url","speaker","start","end","transcript","normalized_transcript","phonetic_variants" (array). Normalize punctuation and casing in normalized_transcript; provide up to 3 phonetic variants for rare words or custom vocab. Few-shot examples: {"audio_url":"s3://bucket/file.mp3","speaker":"Speaker 1","start":"00:00:05","end":"00:00:12","transcript":"Um, I think we should...","normalized_transcript":"I think we should","phonetic_variants":["data-privacy","datuh-privacy"]}. Output only NDJSON, one record per line.
Choose Rev.ai over Google Cloud Speech-to-Text if you prioritize media-focused caption outputs and easy access to human-reviewed transcripts.