pipeline

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Copyright (c) 2020- CNRS

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AUTHORS Hervé Bredin - http://herve.niderb.fr

Applying pretrained pipelines on your own data

This tutorial assumes that you have already followed the data preparation tutorial.

For the purpose of this tutorial, we use a speaker diarization pipeline available on torch.hub that was pretrained on AMI training subset:

import torch
pipeline = torch.hub.load('pyannote/pyannote-audio', 'dia_ami')

We will apply this pretrained pipeline on the first file of the AMI test subset.

# ... or use a file provided by a pyannote.database protocol
# in this example, we are using AMI first test file.
from pyannote.database import get_protocol
from pyannote.database import FileFinder
preprocessors = {'audio': FileFinder()}
protocol = get_protocol('AMI.SpeakerDiarization.MixHeadset',
                        preprocessors=preprocessors)
test_file = next(protocol.test())

⚠️ If you would like to test this pipeline on your own data, you could do something like this (or define your own protocol).

# one can use their own file like this...
test_file = {'uri': 'filename', 'audio': '/path/to/your/filename.wav'}

Note that, in case of domain mismatch between your data and the AMI corpus, you might be better off training your own models or fine-tuning a pretrained one, and tuning your own pipeline.

Diarization

diarization = pipeline(test_file)

Visualization

# let's visualize the diarization output using pyannote.core visualization API
from matplotlib import pyplot as plt
from pyannote.core import Segment, notebook

# only plot one minute (between t=120s and t=180s)
notebook.crop = Segment(120, 180)

# create a figure with 6 rows with matplotlib
nrows = 2
fig, ax = plt.subplots(nrows=nrows, ncols=1)
fig.set_figwidth(20)
fig.set_figheight(nrows * 2)

# 1st row: reference annotation
notebook.plot_annotation(test_file['annotation'], ax=ax[0], time=False)
ax[0].text(notebook.crop.start + 0.5, 0.1, 'reference', fontsize=14)

# 2nd row: pipeline output
notebook.plot_annotation(diarization, ax=ax[1], time=False)
ax[1].text(notebook.crop.start + 0.5, 0.1, 'hypothesis', fontsize=14)

That's all folks!