/
demo_utils.py
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/
demo_utils.py
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from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.animation import FuncAnimation
from resemblyzer import sampling_rate
from matplotlib import cm
from time import sleep, perf_counter as timer
from umap import UMAP
from sys import stderr
import matplotlib.pyplot as plt
import numpy as np
_default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
_my_colors = np.array([
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[97, 142, 151],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
[76, 255, 0],
], dtype=float) / 255
def play_wav(wav, blocking=True):
try:
import sounddevice as sd
# Small bug with sounddevice.play: the audio is cut 0.5 second too early. We pad it to
# make up for that
wav = np.concatenate((wav, np.zeros(sampling_rate // 2)))
sd.play(wav, sampling_rate, blocking=blocking)
except Exception as e:
print("Failed to play audio: %s" % repr(e))
def plot_similarity_matrix(matrix, labels_a=None, labels_b=None, ax: plt.Axes=None, title=""):
if ax is None:
_, ax = plt.subplots()
fig = plt.gcf()
img = ax.matshow(matrix, extent=(-0.5, matrix.shape[0] - 0.5,
-0.5, matrix.shape[1] - 0.5))
ax.xaxis.set_ticks_position("bottom")
if labels_a is not None:
ax.set_xticks(range(len(labels_a)))
ax.set_xticklabels(labels_a, rotation=90)
if labels_b is not None:
ax.set_yticks(range(len(labels_b)))
ax.set_yticklabels(labels_b[::-1]) # Upper origin -> reverse y axis
ax.set_title(title)
cax = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.15)
fig.colorbar(img, cax=cax, ticks=np.linspace(0.4, 1, 7))
img.set_clim(0.4, 1)
img.set_cmap("inferno")
return ax
def plot_histograms(all_samples, ax=None, names=None, title=""):
"""
Plots (possibly) overlapping histograms and their median
"""
if ax is None:
_, ax = plt.subplots()
for samples, color, name in zip(all_samples, _default_colors, names):
ax.hist(samples, density=True, color=color + "80", label=name)
ax.legend()
ax.set_xlim(0.35, 1)
ax.set_yticks([])
ax.set_title(title)
ylim = ax.get_ylim()
ax.set_ylim(*ylim) # Yeah, I know
for samples, color in zip(all_samples, _default_colors):
median = np.median(samples)
ax.vlines(median, *ylim, color, "dashed")
ax.text(median, ylim[1] * 0.15, "median", rotation=270, color=color)
return ax
def plot_projections(embeds, speakers, ax=None, colors=None, markers=None, legend=True,
title="", **kwargs):
if ax is None:
_, ax = plt.subplots(figsize=(6, 6))
# Compute the 2D projections. You could also project to another number of dimensions (e.g.
# for a 3D plot) or use a different different dimensionality reduction like PCA or TSNE.
reducer = UMAP(**kwargs)
projs = reducer.fit_transform(embeds)
# Draw the projections
speakers = np.array(speakers)
colors = colors or _my_colors
for i, speaker in enumerate(np.unique(speakers)):
speaker_projs = projs[speakers == speaker]
marker = "o" if markers is None else markers[i]
label = speaker if legend else None
ax.scatter(*speaker_projs.T, c=[colors[i]], marker=marker, label=label)
if legend:
ax.legend(title="Speakers", ncol=2)
ax.set_title(title)
ax.set_xticks([])
ax.set_yticks([])
ax.set_aspect("equal")
return projs
def interactive_diarization(similarity_dict, wav, wav_splits, x_crop=5, show_time=False):
fig, ax = plt.subplots()
lines = [ax.plot([], [], label=name)[0] for name in similarity_dict.keys()]
text = ax.text(0, 0, "", fontsize=10)
def init():
ax.set_ylim(0.4, 1)
ax.set_ylabel("Similarity")
if show_time:
ax.set_xlabel("Time (seconds)")
else:
ax.set_xticks([])
ax.set_title("Diarization")
ax.legend(loc="lower right")
return lines + [text]
times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits]
rate = 1 / (times[1] - times[0])
crop_range = int(np.round(x_crop * rate))
ticks = np.arange(0, len(wav_splits), rate)
ref_time = timer()
def update(i):
# Crop plot
crop = (max(i - crop_range // 2, 0), i + crop_range // 2)
ax.set_xlim(i - crop_range // 2, crop[1])
if show_time:
crop_ticks = ticks[(crop[0] <= ticks) * (ticks <= crop[1])]
ax.set_xticks(crop_ticks)
ax.set_xticklabels(np.round(crop_ticks / rate).astype(np.int))
# Plot the prediction
similarities = [s[i] for s in similarity_dict.values()]
best = np.argmax(similarities)
name, similarity = list(similarity_dict.keys())[best], similarities[best]
if similarity > 0.75:
message = "Speaker: %s (confident)" % name
color = _default_colors[best]
elif similarity > 0.65:
message = "Speaker: %s (uncertain)" % name
color = _default_colors[best]
else:
message = "Unknown/No speaker"
color = "black"
text.set_text(message)
text.set_c(color)
text.set_position((i, 0.96))
# Plot data
for line, (name, similarities) in zip(lines, similarity_dict.items()):
line.set_data(range(crop[0], i + 1), similarities[crop[0]:i + 1])
# Block to synchronize with the audio (interval is not reliable)
current_time = timer() - ref_time
if current_time < times[i]:
sleep(times[i] - current_time)
elif current_time - 0.2 > times[i]:
print("Animation is delayed further than 200ms!", file=stderr)
return lines + [text]
ani = FuncAnimation(fig, update, frames=len(wav_splits), init_func=init, blit=not show_time,
repeat=False, interval=1)
play_wav(wav, blocking=False)
plt.show()
def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
if ax is None:
_, ax = plt.subplots()
if shape is None:
height = int(np.sqrt(len(embed)))
shape = (height, -1)
embed = embed.reshape(shape)
cmap = cm.get_cmap()
mappable = ax.imshow(embed, cmap=cmap)
cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
cbar.set_clim(*color_range)
ax.set_xticks([]), ax.set_yticks([])
ax.set_title(title)