Sold by: pyannoteAI
Deployed on AWS
Accurately identify who spoke when in any audio using our Speaker Diarization. This scalable, language-agnostic service segments multi-speaker audio into speaker-labeled time intervals, supporting formats like WAV, MP3, and FLAC. Ideal for transcription, call analytics, media processing, and compliance workflows.
Overview
The Speaker Diarization API enables accurate segmentation of audio recordings by detecting and labeling individual speakers across time. Designed for seamless integration into transcription pipelines, media workflows, and audio analytics systems, it supports a wide range of formats including WAV, MP3, FLAC, and OGG. The service is language-agnostic and works across diverse audio sourcecalls, meetings, interviews, podcasts, and more. With built-in support for mono and stereo channels, varying sample rates, and flexible input options it can be deployed in batch or near-real-time use cases. Key features include automatic speaker count estimation, precise time-stamped speaker labeling, and detection of overlapping speech. Outputs are returned in structured JSON for easy integration with transcription engines, search indexes, or business intelligence tools. Whether you are enriching speech-to-text transcripts, analyzing call center performance, or processing long-form media, this API improves clarity, organization, and data usability.
Highlights
- Accurate speaker diarization for multi-speaker audio, with support for automatic speaker count estimation and overlapping speech detection.
- Language-agnostic and format-flexible: Works with WAV, MP3, FLAC, and more; supports mono and stereo channels for diverse use cases like transcription and media analysis.
Details
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Deployed on AWS
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.xlarge instance type, batch mode | $3.404 |
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $2.714 |
Dimensions summary
You pay by the hour for model inference on the ml.g4dn.xlarge instance type. Both dimensions run on the same instance size, so pricing scales with how many host hours you consume. The difference is the processing mode. Batch mode handles pre-recorded audio files submitted as jobs. Real-time mode processes streaming audio as a conversation unfolds. Pick the mode that matches your workload, or use both. There are no separate tiers or seat-based charges here; your cost tracks the running time of the instance in each mode.
Top-of-mind questions for buyers
One host hour equals one hour the ml.g4dn.xlarge instance runs in a given mode. Charges track the instance's running time, not the audio volume processed. If the instance is stopped, software host hours stop accruing. Underlying AWS infrastructure fees may still apply while the instance runs.
Batch mode processes pre-recorded audio files you submit as jobs, so it fits recordings you handle after the fact. Real-time mode processes streaming audio as a conversation happens, so it fits live captioning, meetings, or agent assist. Both bill per host hour on the same instance size.
Yes. Each mode bills separately by its own host hours. Running both means two independent host-hour meters, added together on your invoice. Neither mode charges by user seats or fixed tiers. Your total tracks the combined running time of each mode's instance.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Precision-2 model with improved diarization capabilities (37% accuracy improvement). New optional min_speakers and max_speakers input arguments.
Additional details
Inputs
- Summary
Diarization input: { "audio": "", "num_speakers": 2 }
- Input MIME type
- application/json
https://github.com/pyannoteai/aws-marketplace-docs/blob/main/example_files/marklex1min.mp3
https://github.com/pyannoteai/aws-marketplace-docs/blob/main/example_files/marklex1min.mp3
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
audio | base64 audio | - | No |
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