🔭 Speaker diarization via transfer learning
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README.md

minutes

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Audio speaker diarization library.

Under Construction!

🏃 Development

Dependencies are managed using a Pipfile and Pipenv:

pipenv install
pipenv shell

Testing

pytest --cov=minutes -vvv test

Example Usage

from minutes import Speaker, Minutes, Conversation

minutes = Minutes(parent='cnn')

# Create some speakers, add some audio.
s1, s2 = Speaker('s1'), Speaker('s2')
s1.add_audio('path/to/audio1')
s2.add_audio('path/to/audio2')

# Add speakers to the model.
minutes.add_speakers([s1, s2])

# Fit the model.
minutes.fit()

# Collect a new conversation for prediction.
conversation = Conversation('/path/to/conversation.wav')

# Create phrases from the conversation.
phrases = minutes.phrases(conversation)