Open-source speaker recognition is the only option for developers, unless they work for large enterprises. Recently, at Picovoice we made our internal tool for Speaker Recognition public. So, those who prefer production-ready solutions now have an option!

Let's get started!

1. Install [Picovoice Eagle Speaker Recognition](https://picovoice.ai/platform/eagle/) using pip. We will be using pvrecorder to get cross-platform audio, so install that as well:

pip3 install pveagle pvrecorder

2. Grab your AccessKey
If you haven't create an account on Picovoice Console for free and grab your AccessKey

3. Enroll Speakers
Import pveagle and create an instance of the EagleProfiler class:

import pveagle

access_key = "{YOUR_ACCESS_KEY}";
try:
    eagle_profiler = pveagle.create_profiler(access_key=access_key)
except pveagle.EagleError as e:
    # Handle error
    pass

Don't forget to replace the placeholder with your AccessKey!

Now, import pvrecorder and create an instance of the recorder as well. Use the EagleProfiler's .min_enroll_samples as the frame_length:

from pvrecorder import PvRecorder

DEFAULT_DEVICE_INDEX = -1
recorder = PvRecorder(
    device_index=DEFAULT_DEVICE_INDEX,
    frame_length=eagle_profiler.min_enroll_samples)

The .enroll() function generates a percentage value to know when Enrollment is done and another speaker can be enrolled:

recorder.start()

enroll_percentage = 0.0
while enroll_percentage < 100.0:
    audio_frame = recorder.read()
    enroll_percentage, feedback = eagle_profiler.enroll(audio_frame)

recorder.stop()

4. Export the Speaker Profile

We'll need use in the next step to identify / verify the speaker!

speaker_profile = eagle_profiler.export()

You can reuse the speaker_profile object. Check out the docs for details.

Add more speakers by creating additional profiles by calling the .reset() function on the EagleProfiler, and repeating the .enroll() step.

5. Clean up used resources:
Once you create profiles for all speakers, let's clean up used resources!

recorder.delete()
eagle_profiler.delete()

6. Recognize Speakers:

import pveagle

access_key = "{YOUR_ACCESS_KEY}"
profiles = [speaker_profile_1, speaker_profile_2]
try:
eagle = pveagle.create_recognizer(
access_key=access_key,
speaker_pofiles=profiles)
except pveagle.EagleError as e:
# Handle error
pass

Set up `pvrecorder` to use with Eagle Speaker Recognition:

recorder = PvRecorder(
device_index=DEFAULT_DEVICE_INDEX,
frame_length=eagle.frame_length)

Pass audio frames into the `eagle.process()` function get back speaker scores:

while True:
audio_frame = recorder.read()
scores = eagle.process(audio_frame)

When finished, again clean up used resources:

eagle.delete()

## Connect them All Together

import pveagle
from pvrecorder import PvRecorder

DEFAULT_DEVICE_INDEX = -1
access_key = "{YOUR_ACCESS_KEY}";

Step 1: Enrollment

try:
eagle_profiler = pveagle.create_profiler(access_key=access_key)
except pveagle.EagleError as e:
pass

enroll_recorder = PvRecorder(
device_index=DEFAULT_DEVICE_INDEX,
frame_length=eagle_profiler.min_enroll_samples)

enroll_recorder.start()

enroll_percentage = 0.0
while enroll_percentage < 100.0:
audio_frame = enroll_recorder.read()
enroll_percentage, feedback = eagle_profiler.enroll(audio_frame)

enroll_recorder.stop()

speaker_profile = eagle_profiler.export()

enroll_recorder.delete()
eagle_profiler.delete()

Step 2: Recognition

try:
eagle = pveagle.create_recognizer(
access_key=access_key,
speaker_pofiles=[speaker_profile])
except pveagle.EagleError as e:
pass

recognizer_recorder = PvRecorder(
device_index=DEFAULT_DEVICE_INDEX,
frame_length=eagle.frame_length)

recognizer_recorder.start()

while True:
audio_frame = recorder.read()
scores = eagle.process(audio_frame)
print(scores)

recognizer_recorder.stop()

recognizer_recorder.delete()
eagle.delete()

---
For more information:

- [Check the original article],(https://picovoice.ai/blog/speaker-recognition-in-python/)
- Learn more about Speaker [Recognition](https://picovoice.ai/blog/speaker-recognition/), [Identification](https://picovoice.ai/blog/speaker-identification/) and [Verification](https://picovoice.ai/blog/voice-biometrics/),
- [Check out other Eagle Speaker Recognition SDKs](https://picovoice.ai/docs/eagle/)
- Visit [Eagle Speaker Recognition GitHub repository](https://github.com/Picovoice/eagle) for open-source demos and to create issues. While on GitHub, if you like building with Eagle Speaker Recognition, give it a star and help fellow devs find it easily.