pveagle 3.0.2

Eagle Speaker Recognition Engine

Eagle Binding for Python

Eagle Speaker Recognition Engine

Made in Vancouver, Canada by Picovoice

Eagle is an on-device speaker recognition engine. Eagle is:

• Private; All voice processing runs locally.
• Cross-Platform:
  • Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64)
  • Android and iOS
  • Chrome, Safari, Firefox, and Edge
  • Raspberry Pi (3, 4, 5)

Compatibility

• Python 3.9+
• Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

Installation

pip3 install pveagle

AccessKey

Eagle requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Eagle SDKs. You can get your AccessKey for free. Make sure to keep your AccessKey secret. Signup or Login to Picovoice Console to get your AccessKey.

Usage

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 EagleProfile object, which can be stored and utilized during inference. During the Recognition step, Eagle compares the incoming audio to the voiceprints of all enrolled speakers in real-time to determine the similarity between them.

Speaker Enrollment

Create an instance of the profiler:

import pveagle

# AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
access_key = "${ACCESS_KEY}"
eagle_profiler = pveagle.create_profiler(access_key)

EagleProfiler is responsible for processing and enrolling PCM audio data, with the valid audio sample rate determined by eagle_profiler.sample_rate. The audio data must be 16-bit linearly-encoded and single-channel.

When passing samples to eagle_profiler.enroll, the number of samples must be equal to eagle_profiler.frame_length. The percentage value obtained from this process indicates the progress of enrollment.

def get_next_enroll_audio_frame(frame_length):
    pass

def has_next_enroll_audio_frame(frame_length):
    pass


percentage = 0.0
while percentage < 100.0 and has_next_enroll_audio_frame(eagle_profiler.frame_length):
    percentage = eagle_profiler.enroll(get_next_enroll_audio_frame(eagle_profiler.frame_length))
percentage = eagle_profiler.flush()

After the percentage reaches 100%, the enrollment process is considered complete. While it is possible to continue providing additional audio data to the profiler to improve the accuracy of the voiceprint, it is not necessary to do so. Once all the audio from a single source has been submitted it is necessary to call flush before submitting any audio from another source.

speaker_profile = eagle_profiler.export()

The eagle_profiler.export() function produces an EagleProfile object, which can be converted into a binary form using the EagleProfile.to_bytes() method. This binary representation can be saved and subsequently retrieved using the EagleProfile.from_bytes() method.

To reset the profiler and enroll a new speaker, the eagle_profiler.reset() method can be used. This method clears all previously stored data, making it possible to start a new enrollment session with a different speaker.

Finally, when done be sure to explicitly release the resources:

eagle_profiler.delete()

Speaker Recognition

Create an instance of the engine:

eagle = pveagle.create_recognizer(access_key)

When initialized, eagle.sample_rate specifies the valid sample rate for Eagle. The minimum number of audio samples in an input array, is defined by eagle.min_process_samples.

Like the profiler, Eagle is designed to work with single-channel audio that is encoded using 16-bit linear PCM.

Process audio with one or more speaker profiles from the EagleProfiler.

def get_next_audio_chunk(num_samples):
    pass


while True:
    scores = eagle.process(get_next_audio_chunk(eagle.min_process_samples), speaker_profiles)

The scores will be None or be an array that contains floating-point numbers that indicate the similarity between the input audio frame and the enrolled speakers. Each value in the array corresponds to a specific enrolled speaker, maintaining the same order as the speaker profiles provided during initialization. The values in the array range from 0.0 to 1.0, where higher values indicate a stronger degree of similarity. A result of None indicates that there was not enough voice in the audio to recognize any speakers.

Finally, when done be sure to explicitly release the resources:

eagle.delete()

Demos

pveagledemo provides command-line utilities for processing real-time audio (i.e. microphone) and files using Eagle.