Eagle Speaker Recognition
Python Quick Start
- Linux (x86_64)
- macOS (x86_64, arm64)
- Windows (x86_64)
- NVIDIA Jetson Nano
- Raspberry Pi (3, 4)
- Picovoice Account & AccessKey
- Python 3.5 or higher
Picovoice Account & AccessKey
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Eagle has two distinct steps: Enrollment and Recognition. In the enrollment step, Eagle analyzes a series of utterances from a particular speaker to learn their unique voiceprint. This step produces an Eagle Profile object, which can be stored and utilized during inference. During the Recognition step, Eagle compares the incoming frames of audio to the voiceprints of all enrolled speakers in real-time to determine the similarity between them.
- Create an instance of the profiler:
- Pass the enrollment audio data to the profiler until the enrollment percentage reaches 100%:
- Export the speaker profile:
- Release the resources acquired by the profiler:
- Create an instance of Eagle:
- Pass the incoming audio stream to Eagle:
- Release the resources acquired by Eagle:
For the Eagle SDK, we offer demo applications that demonstrate how to use the speaker recognition engine on real-time audio streams (i.e. microphone input) and audio files.
Install the pveagledemo Python package using PIP:
This package installs command-line utilities for the Eagle Python demos.
--help flag to see the usage options for the demos:
Ensure you have a working microphone connected to your system and run the command corresponding to your platform to either enroll a speaker or perform speaker recognition:
For more information on our Eagle demos for Python, head over to our GitHub repository .