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linter and test updates for speaker_encoder, gmm_Attention

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Eren Golge
2019-11-12 12:42:42 +01:00
parent 1401a0db6b
commit df1b8b3ec7
14 changed files with 171 additions and 207 deletions
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speaker_encoder/visual.py
+24 -18
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@@ -3,28 +3,34 @@ import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
matplotlib.use("Agg")
colormap = np.array([
[76, 255, 0],
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
], dtype=np.float) / 255
colormap = (
np.array(
[
[76, 255, 0],
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
],
dtype=np.float,
)
/ 255
)
def plot_embeddings(embeddings, num_utter_per_speaker):
embeddings = embeddings[:10*num_utter_per_speaker]
embeddings = embeddings[: 10 * num_utter_per_speaker]
model = umap.UMAP()
projection = model.fit_transform(embeddings)
num_speakers = embeddings.shape[0] // num_utter_per_speaker
@@ -32,7 +38,7 @@ def plot_embeddings(embeddings, num_utter_per_speaker):
colors = [colormap[i] for i in ground_truth]
fig, ax = plt.subplots(figsize=(16, 10))
im = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
_ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
plt.gca().set_aspect("equal", "datalim")
plt.title("UMAP projection")
plt.tight_layout()