Skip to main content

Eagle Speaker Recognition Engine demos

Project description

Eagle Speaker Recognition Demos

Made in Vancouver, Canada by Picovoice

Eagle

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)
    • Android and iOS
    • Chrome, Safari, Firefox, and Edge
    • Raspberry Pi (5, 4, 3) and NVIDIA Jetson Nano

Compatibility

  • Python 3.7+
  • Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64), Raspberry Pi (5, 4, 3), and NVIDIA Jetson Nano.

Installation

pip3 install pveagledemo

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.

Overview

Eagle consists of 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 results in an EagleProfile 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.

Microphone Demo

The microphone demo captures audio input from a microphone that is connected. To run the demo, use the following command in the terminal:

eagle_demo_mic {enroll, test} --access_key ${ACCESS_KEY} ...

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console.

The commands enroll and test are used to create a speaker profile and perform speaker recognition, respectively. Detailed explanations of these commands will be provided in their respective sections.

Furthermore, the demo offers optional arguments, which can be accessed by utilizing the --help argument. By doing so, you will receive a comprehensive listing of the available arguments along with their corresponding descriptions.

eagle_demo_mic --help

Speaker Enrollment

If the demo is executed in the enrollment mode by using the enroll command, it will initiate the enrollment process using the audio captured from the microphone. It will display the progress percentage in the terminal until it reaches 100%. Once completed, it will save the profile of the enrolled speaker to the disk.

eagle_demo_mic enroll --access_key ${ACCESS_KEY} --output_profile_path ${OUTPUT_PROFILE_PATH}

Replace ${OUTPUT_PROFILE_PATH} with the absolute path where the generated profile should be written.

Speaker Recognition

Once the speaker profile for all speakers are created, the demo can be run in the test mode by running the following command:

eagle_demo_mic test --access_key ${ACCESS_KEY} --input_profile_paths ${INPUT_PROFILE_PATH_1 ...}

In this mode, you can include multiple speaker profiles by specifying them with the --input_profile_paths option. Eagle will assess and provide a distinct score for each profile, which will be displayed in the terminal.

File Demo

Similar to the mic demo, the file demo can be run in two modes: enroll and test

eagle_demo_file {enroll,test} --access_key ${ACCESS_KEY} ...

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console.

The commands enroll and test are used to create a speaker profile and perform speaker recognition, respectively, and will be discussed in detail in their respective sections.

To view the optional arguments for the demo, use the --help argument. This will display a list of available arguments and their descriptions.

eagle_demo_file --help

Speaker Enrollment

To run the demo in enroll mode, you need two additional input arguments along with the AccessKey.

eagle_demo_file enroll --access_key ${ACCESS_KEY} \
  --output_profile_path ${OUTPUT_PROFILE_PATH} --enroll_audio_paths ${ENROLL_AUDIO_PATH_1 ...} 

In this command, {ENROLL_AUDIO_PATH_1 ...} represents the absolute paths to the enroll audio files. If multiple files are provided, Eagle will process all of them. Once the specified files are processed, the demo will generate a profile at ${OUTPUT_PROFILE_PATH}.

Speaker Recognition

The file demo requires a test audio and one or more speaker profiles that were created during the enrollment step.

To run the demo, use the following command in the console:

eagle_demo_file test --access_key ${ACCESS_KEY} \
  --input_profile_paths ${INPUT_PROFILE_PATH_1 ...} --test_audio_path ${TEST_AUDIO_PATH}

The demo will display the result for each enrolled speaker in the terminal.

Optionally, you can also generate a .csv file for further analysis by including the --csv_output_path parameter:

eagle_demo_file test --access_key ${ACCESS_KEY} \
  --input_profile_paths ${INPUT_PROFILE_PATH_1 ...} --test_audio_path ${TEST_AUDIO_PATH} \
  --csv_output_path ${CSV_OUTPUT_PATH}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pveagledemo-1.0.2.tar.gz (13.1 kB view details)

Uploaded Source

Built Distribution

pveagledemo-1.0.2-py3-none-any.whl (13.1 kB view details)

Uploaded Python 3

File details

Details for the file pveagledemo-1.0.2.tar.gz.

File metadata

  • Download URL: pveagledemo-1.0.2.tar.gz
  • Upload date:
  • Size: 13.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.12

File hashes

Hashes for pveagledemo-1.0.2.tar.gz
Algorithm Hash digest
SHA256 e67af6e933e935efff536ffd5315f5b93091b04abc348e51ada8315a650a05a0
MD5 d243c99738acfe6780b6737494a6f8bf
BLAKE2b-256 5edf00bfde2b6710d37ec921d7a033b1654f0ea46101e99afa172f964199e828

See more details on using hashes here.

File details

Details for the file pveagledemo-1.0.2-py3-none-any.whl.

File metadata

  • Download URL: pveagledemo-1.0.2-py3-none-any.whl
  • Upload date:
  • Size: 13.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.12

File hashes

Hashes for pveagledemo-1.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 4de54d77e61352a0c6986b6a91cea3a0ab7ddb2c251ead53acb11dbbdfd76c3f
MD5 b4ffd11475b1fff98f585413268935cc
BLAKE2b-256 56ad24921deee2a0a4e8c07cb6038c5f68630515b119211067916c06448cfbe6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page