The speaker labels were probably my favorite feature in AssemblyAI. It made conversations much easier to follow and the AI summary saved me time when reviewing longer recordings.
Industry-leading AI models to transcribe and understand speech with unmatched accuracy and scalability.
AssemblyAI offers state-of-the-art speech-to-text and speech understanding AI models designed for developers to build, ship, and scale voice AI applications with high accuracy, multilingual support, and advanced features like speaker diarization and contextual prompting.
Updated Oct 18, 2026
The speaker labels were probably my favorite feature in AssemblyAI. It made conversations much easier to follow and the AI summary saved me time when reviewing longer recordings.
Tried this tool? Share your experience.
The speaker labels were probably my favorite feature in AssemblyAI. It made conversations much easier to follow and the AI summary saved me time when reviewing longer recordings.
Tried this tool? Share your experience.
Get clean, customizable transcripts in 99 languages with industry-leading accuracy and natural language prompting.
Stream transcripts in real time with async-level accuracy, so your agent responds fast without mishearing the user.
Send a short clip and get a finished, flagship-accuracy transcript back in the same response — no polling, no WebSocket.
Build production-ready voice agents with built-in turn detection and interruption handling, so you ship fast without the complexity.
Redact PII and moderate content inline on audio and transcripts, so sensitive data never hits your logs or your LLM.
Route between every LLM from one endpoint with built-in fallback, so you swap models and survive outages without touching your code.
Global redundancy, enterprise-grade uptime, and 2 million hours of audio processed every day. The Voice AI infrastructure your product can depend on at any scale.
Replace “Speaker A” and “Speaker B” with real names or roles, inferred from the conversation itself.
Turn every recording into concise summaries and timestamped chapters, each with a headline and gist.
Pull the follow-ups out of any conversation, each with a timestamp and the quote it came from.
Classify sentiment as positive, neutral, or negative, sentence by sentence, and per speaker when diarization is on.
Surface the words and phrases that matter most in a recording, ranked by relevance with timestamps.
Go beyond basic transcription. Extract speaker ID, sentiment, chapters, and summaries from a single API call.
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