Unsupervised clustering using Agglomerative

This tutorial is available as an IPython notebook at malaya-speech/example/diarization-clustering-agglomerative.

This module is language independent, so it save to use on different languages. Pretrained models trained on multilanguages.

This is an application of malaya-speech Pipeline, read more about malaya-speech Pipeline at malaya-speech/example/pipeline.

from malaya_speech import Pipeline
import malaya_speech
import numpy as np
import matplotlib.pyplot as plt

load Speaker Vector

So to know speakers similarity, we can use speaker vector, just load using malaya_speech.speaker_vector.deep_model. Read more about malaya-speech Speaker Vector at https://malaya-speech.readthedocs.io/en/latest/load-speaker-vector.html

We are going to compare conformer-base and vggvox-v2.

model_conformer = malaya_speech.speaker_vector.deep_model('conformer-base')
model_vggvox2 = malaya_speech.speaker_vector.deep_model('vggvox-v2')

Load audio sample

y, sr = malaya_speech.load('speech/video/The-Singaporean-White-Boy.wav')
len(y), sr

(1634237, 16000)

# just going to take 60 seconds
y = y[:sr * 60]

This audio extracted from https://www.youtube.com/watch?v=HylaY5e1awo&t=2s

Load VAD

We need to use VAD module to know which parts of the audio sample are speaker activities, read more about VAD at https://malaya-speech.readthedocs.io/en/latest/load-vad.html

vad = malaya_speech.vad.deep_model(model = 'vggvox-v2')

frames = list(malaya_speech.utils.generator.frames(y, 30, sr))

p = Pipeline()
pipeline = (
    p.batching(5)
    .foreach_map(vad.predict)
    .flatten()
)
p.visualize()

%%time

result = p(frames)
result.keys()

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/librosa/core/spectrum.py:224: UserWarning: n_fft=512 is too small for input signal of length=480
  n_fft, y.shape[-1]

CPU times: user 1min 1s, sys: 37.2 s, total: 1min 38s
Wall time: 21.7 s

dict_keys(['batching', 'predict', 'flatten'])

frames_vad = [(frame, result['flatten'][no]) for no, frame in enumerate(frames)]
grouped_vad = malaya_speech.utils.group.group_frames(frames_vad)
grouped_vad = malaya_speech.utils.group.group_frames_threshold(grouped_vad, threshold_to_stop = 0.3)

malaya_speech.extra.visualization.visualize_vad(y, grouped_vad, sr, figsize = (15, 3))

Load Agglomerative clustering model

class AgglomerativeClustering:
    def __init__(
        self,
        min_clusters: int,
        max_clusters: int,
        metric: str = 'cosine',
        threshold: float = 0.25,
        method: str = 'centroid',
    ):
        """
        Load malaya-speech AgglomerativeClustering, originallly from pyannote, https://github.com/pyannote/pyannote-audio/blob/develop/pyannote/audio/pipelines/clustering.py

        Parameters
        ----------
        min_clusters: int
            minimum cluster size, must bigger than 0
        max_clusters: int
            maximum cluster size, must equal or bigger than `min_clusters`.
            if equal to `min_clusters`, will directly fit into HMM without calculating the best cluster size.
        metric: str, optional (default='cosine')
            Only support `cosine` and `euclidean`.
        threshold: float, optional (default=0.35)
            minimum threshold to assume current iteration of cluster is the best fit.
        method: str, optional (default='centroid')
            All available methods at https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
        """

To get better results using HMM, set norm_function=None and log_distance_metric=None.

from malaya_speech.model.clustering import AgglomerativeClustering

agg_model = AgglomerativeClustering(min_clusters = 1, max_clusters = 10)

result_diarization_agg_conformer = malaya_speech.diarization.clustering(
    vad_results = grouped_vad,
    speaker_vector = model_conformer,
    model = agg_model,
    norm_function = None,
)
result_diarization_agg_conformer[:5]

[(<malaya_speech.model.frame.Frame at 0x186493510>, 'not a speaker'),
 (<malaya_speech.model.frame.Frame at 0x17e2096d0>, 'not a speaker'),
 (<malaya_speech.model.frame.Frame at 0x17e209750>, 'speaker 6'),
 (<malaya_speech.model.frame.Frame at 0x17e209410>, 'speaker 0'),
 (<malaya_speech.model.frame.Frame at 0x17ef728d0>, 'speaker 0')]

result_diarization_agg_vggvox2 = malaya_speech.diarization.clustering(
    vad_results = grouped_vad,
    speaker_vector = model_vggvox2,
    model = agg_model,
    norm_function = None,
)
result_diarization_agg_vggvox2[:5]

[(<malaya_speech.model.frame.Frame at 0x186493510>, 'not a speaker'),
 (<malaya_speech.model.frame.Frame at 0x17e2096d0>, 'not a speaker'),
 (<malaya_speech.model.frame.Frame at 0x17e209750>, 'speaker 4'),
 (<malaya_speech.model.frame.Frame at 0x17e209410>, 'speaker 0'),
 (<malaya_speech.model.frame.Frame at 0x17ef728d0>, 'speaker 0')]

nrows = 3
fig, ax = plt.subplots(nrows = nrows, ncols = 1)
fig.set_figwidth(20)
fig.set_figheight(nrows * 3)
malaya_speech.extra.visualization.visualize_vad(y, grouped_vad, sr, ax = ax[0])
malaya_speech.extra.visualization.plot_classification(result_diarization_agg_conformer,
                                                      'conformer + agglomerative', ax = ax[1],
                                                     x_text = 0.01)
malaya_speech.extra.visualization.plot_classification(result_diarization_agg_vggvox2,
                                                      'vggvox2 + agglomerative', ax = ax[2],
                                                     x_text = 0.01)
fig.tight_layout()
plt.show()