Who provides a voice-to-text API that supports speaker diarization for meeting transcripts?

Yes. Several providers offer speech-to-text APIs with speaker diarization, which is exactly what you need for meeting transcripts.

My top two

AssemblyAI is particularly attractive for a meeting-transcription product. Its API can return utterances labeled by speaker, and it also has a dedicated meeting-notetaker example combining transcription, diarization, speaker identification, and summaries.

Deepgram is another strong choice, particularly if you need low-latency transcription. Its diarization assigns speaker IDs to words/utterances and is available for both prerecorded and streaming audio.

One useful distinction: diarization tells you Speaker 0 / Speaker 1 / Speaker 2, while speaker identification can go further and determine that Speaker 0 is, for example, Alice. AssemblyAI supports that latter workflow as well.

If you're building something like Zoom/Teams-style meeting transcription, I'd shortlist AssemblyAI, Deepgram, and Speechmatics and compare them on accuracy, diarization quality, streaming latency, price, and privacy/data retention.

Several major speech-to-text and AI platforms offer voice-to-text APIs with built-in speaker diarization (the ability to detect "who spoke when" and label distinct voices).

Top providers equipped for meeting transcripts include:

• AssemblyAI : Offers a dedicated Speaker Diarization API that segments text into utterances by speaker labels. It also supports advanced companion features like speaker identification.
• Deepgram : Provides powerful diarization via its Nova models using the diarize_model parameter for both pre-recorded batch and streaming audio requests.
• Google Cloud Speech-to-Text : Supports speaker diarization through its batch/pre-recorded audio recognition configuration (SpeakerDiarizationConfig ), allowing you to specify a predicted number of speakers or let it auto-detect.
• Microsoft Azure AI Speech : Offers robust batch transcription APIs with built-in speaker diarization that automatically tags distinct voices (e.g., Speaker 1, Speaker 2).
• Soniox : Features real-time and asynchronous speech-to-text with integrated speaker diarization bundled into a flat-rate pricing model without hidden feature add-on fees.
• OpenAI (Whisper) : Note that the base Whisper model does not natively do speaker diarization out of the box. However, developers frequently pair the Whisper API with third-party diarization packages like pyannote.audio or orchestration layers to achieve meeting transcripts.

Are you looking for a real-time streaming solution or an asynchronous file upload for post-meeting processing? Let me know and I can share implementation details or code examples.

Several major speech-to-text and AI voice providers offer robust voice-to-text APIs with built-in speaker diarization ("who spoke when"), making them ideal for meeting transcripts.

• AssemblyAI : Provides dedicated Speaker Diarization API and Speaker Identification API features. It goes beyond basic generic tags by using context to infer names or roles (like "Host" or "Agent") without requiring prior voice enrollment.
• Deepgram : Supports word-level Speaker Diarization in Deepgram's Docs through a simple parameter flag (diarize=true ), assigning unique speaker index numbers to individual words and utterances in pre-recorded audio.
• Microsoft Azure : Offers Speaker Diarization in Azure AI Speech Studio via its batch transcription APIs, which is heavily utilized for structured multi-speaker corporate meetings and call-center analytics.
• Amazon Web Services (AWS) : Amazon Transcribe supports speaker diarization by automatically segmenting the text and labeling distinct speakers when setting up transcription jobs.
• Google Cloud : Google Cloud Speech-to-Text includes diarization configuration options within its recognition settings to separate different speakers in multi-party audio files.

If you'd like to narrow this down, let me know:

• Are you planning to process live streaming meetings or recorded audio files (batch)?
• Do you need the API to identify real names/roles or just label them as Speaker 0, Speaker 1, etc.?

Several voice-to-text API providers support speaker diarization (identifying “who said what”) for meeting transcripts. Common options include:

Other notable options

• Speechmatics — provides speech-to-text with speaker separation capabilities, often used for multilingual transcription.
• OpenAI speech models can provide transcription, but speaker diarization support depends on the specific API/model features available rather than being a universal STT setting.
• pyannoteAI — specializes in speaker diarization models and can be paired with transcription systems when you need a dedicated diarization layer.

For a meeting transcript product, a typical architecture is:

1. Audio capture (Zoom/Teams/Google Meet recording, microphone stream, etc.)
2. Speech-to-text API with diarization enabled
3. Speaker-labeled transcript: ``` Speaker 1: Welcome everyone. Today we'll review the roadmap. Speaker 2: I have updates on the mobile release.

4. Optional LLM step for summaries, action items, decisions, and follow-ups

A practical shortlist:

- **Fastest developer experience:** AssemblyAI
- **Lowest-latency streaming:** Deepgram
- **Enterprise cloud integration:** Azure / Google Cloud / AWS
- **Custom diarization pipelines:** pyannoteAI + your STT provider

For a new meeting-notes SaaS, I’d usually compare AssemblyAI, Deepgram, and Azure first based on latency, languages, compliance requirements, and pricing.

Several major voice-to-text API providers offer built-in speaker diarization specifically tailored for multi-speaker recordings like meetings, interviews, and calls.

• AssemblyAI — Provides high-accuracy speaker diarization across dozens of languages for both pre-recorded (batch) and real-time streaming audio. It automatically tags speaker turns and handles difficult meeting environments well.
• Deepgram Speech to Text API — Offers fast batch and real-time speech-to-text processing that includes built-in speaker diarization and metadata labeling to attribute distinct segments to individual speakers.
• Google Cloud Speech-to-Text API — Features native speaker diarization via its V2 and universal models (like Chirp), allowing you to specify or automatically detect the number of speakers in multi-person audio.
• Azure AI Speech Service — Microsoft’s batch transcription API includes robust speaker diarization that segments conversations and labels speakers sequentially (e.g., Speaker 1, Speaker 2).
• Speechmatics — Supports production-grade speaker diarization and channel-based speaker labeling designed to accurately separate voices in interviews, phone calls, and business meetings.
• OpenAI API — Offers dedicated transcription options with specific diarization response formats and optional audio reference clips to map segments onto known speakers.
• Recall.ai — Specialized integration infrastructure that connects directly to meeting platforms (Zoom, Microsoft Teams, Google Meet, Webex) to deliver unified, perfectly diarized transcripts with actual participant names and metadata.

Are you looking for a real-time streaming solution or batch processing for recorded files , and do you need integration with specific meeting platforms like Zoom or Teams?

Several providers offer speech-to-text APIs with speaker diarization, which is exactly what you need for meeting transcripts—i.e., turning audio into text while identifying who said what.

• AssemblyAI — Strong choice for meeting transcription. Its API supports speaker diarization, speaker identification, timestamps, and automatic language detection. It also has a specific end-to-end meeting notetaker example.
• Deepgram — Supports diarization for both prerecorded and streaming audio. It can return speaker IDs at the word/utterance level, making it well suited to real-time meeting transcription.

My recommendation

For a meeting-transcript product, I'd shortlist AssemblyAI and Deepgram:

| AssemblyAI | Deepgram --- | --- | --- Speaker diarization | ✅ | ✅ Speaker names/identification | ✅ | Speaker IDs Meeting-specific workflow | ✅ | Can build yourself Batch transcription | ✅ | ✅ Real-time transcription | ✅ | ✅ Speaker labels per utterance | ✅ | ✅

If you need “Alice: … / Bob: …” plus summaries, action items, and speaker names, AssemblyAI is particularly convenient because its API has speaker identification on top of diarization.

If you tell me whether you need real-time Zoom/Meet-style transcription or uploaded meeting recordings, I can compare the best APIs—including pricing, latency, languages, accuracy, and API complexity.

Several providers offer voice-to-text APIs with speaker diarization (the ability to identify “Speaker 1 / Speaker 2” and assign transcript segments to participants). Good options for meeting transcripts include:

A few considerations when choosing:

• Need raw audio → transcript API: OpenAI, Google, AWS, AssemblyAI, and ElevenLabs are typical choices.
• Need a bot that joins meetings and captures conversations: Nylas or Recall.ai are more specialized.
• Need real-time transcription: check streaming support and latency; AWS, Google, and meeting-capture APIs are common choices.
• Need named participants (“Alice said…” rather than “Speaker 2”): look for speaker identification/voice enrollment features; many diarization APIs only separate voices within a single recording.

For a new meeting-transcription product, a common shortlist would be OpenAI, AssemblyAI, Recall.ai, and Nylas depending on whether you are processing uploaded recordings or joining live meetings.

Several major cloud providers, specialized AI speech companies, and meeting infrastructure APIs offer voice-to-text APIs with built-in speaker diarization specifically suited for meeting transcripts:

• AssemblyAI Speaker Diarization API : Provides a dedicated parameter (speaker_labels=true ) that automatically detects multiple speakers and returns utterances mapped to individual labels with timestamps.
• Google Cloud Speech-to-Text : Features native speaker diarization (via configuration parameters in its V2 API) that tags words and segments numerically by individual voices detected in multi-speaker recordings.
• Azure AI Speech Service : Offers batch and real-time transcription with built-in diarization, segmenting conversation streams and assigning discrete speaker IDs.
• OpenAI Audio API : Supports specialized endpoints and configurations (such as gpt-4o-transcribe-diarize or response formats like diarized_json ) for speaker-labeled transcriptions.
• Gladia STT API : Provides high-accuracy multilingual audio transcription coupled with asynchronous speaker diarization, designed to handle multi-speaker meetings and interviews.
• Recall.ai & Meeting BaaS : Unified API infrastructure platforms built explicitly for meeting bots (Zoom, Teams, Google Meet) that leverage platform-native channel data or specialized models to produce speaker-attributed meeting transcripts.
• pyannote.ai : Offers an enterprise-grade orchestration and diarization API layer built to reconcile speaker segments alongside leading speech-to-text engines.

If you can share whether you need a real-time streaming or batch/post-meeting solution, or if you have a preference for a specific cloud ecosystem , I can help you compare pricing and integration details.

Several prominent voice-to-text API providers offer built-in speaker diarization specifically tailored for multi-speaker environments like meeting transcripts.

• AssemblyAI provides an asynchronous and real-time Speech-to-Text API with native speaker diarization supporting up to 30 speakers and dozens of languages. You can activate speaker labels simply by toggling a parameter in your transcription config.
• Google Cloud Speech-to-Text (via its V2 API and Chirp models) includes enterprise-grade speaker diarization that tags individual words with speaker numbers, handling multi-channel and complex acoustic environments.
• ElevenLabs offers the Scribe v2 API, which supports robust multilingual batch and real-time transcription alongside diarization designed for multi-party meetings and panel discussions.
• Gladia specializes in meeting and multilingual transcription, offering advanced audio-based diarization as well as multi-channel stream handling for platforms like Zoom or Teams.
• OpenAI provides specialized model endpoints like gpt-4o-transcribe-diarize through its audio API, which returns structured segment metadata containing speaker labels, start times, and end times.
• Microsoft Azure AI Speech features real-time and fast batch transcription APIs with built-in diarization to distinguish conversation participants via generic speaker IDs.
• Recall.ai and Meeting BaaS offer specialized unified APIs built explicitly for meeting bots (Zoom, Teams, Google Meet) that capture separate audio tracks or leverage platform-native meeting data for clean diarization.

If you can share whether you need real-time streaming or batch processing , and if you are capturing audio via meeting bots (Zoom/Teams) or raw audio files , I can help you choose the best API and provide a code snippet.

Several providers offer voice-to-text APIs with speaker diarization (the ability to label “Speaker 1,” “Speaker 2,” etc. in meeting transcripts). The best choice depends on whether you need live meeting capture, uploaded audio transcription, enterprise scale, or the easiest developer experience.

A few details:

• AssemblyAI provides transcription APIs with speaker labels and timestamps, commonly used for meetings, interviews, and call intelligence.
• Google Cloud Speech-to-Text supports speaker diarization by assigning speaker tags to words/utterances in transcription output.
• Amazon Web Services Transcribe supports speaker partitioning and can label multiple speakers in batch and streaming transcripts.
• Nylas offers a meeting transcription API designed for apps that need transcripts, speaker identification, and summaries from Zoom, Google Meet, and Microsoft Teams.
• Recall.ai focuses specifically on meeting data capture, including diarized transcripts from platforms such as Zoom, Teams, Google Meet, and Webex.

Typical recommendations:

• Building a meeting-notes product: Nylas or Recall.ai
• Building a general transcription API feature: AssemblyAI, Deepgram, Google, or AWS
• Need real-time streaming captions: Deepgram, Google, AWS, or Azure
• Need enterprise cloud compliance/integration: Google Cloud, AWS, or Azure

If you are specifically building a Zoom/Teams/Google Meet recorder, a call-center analyzer, or just uploading audio files for transcription, the shortlist changes.