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import json
import pickle
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
def plot_scalar_on_scale(scalar_value, distance_type):
# Ensure the scalar is within bounds
scalar_value = np.clip(scalar_value, 0.0, 1.0)
# Create a figure and axis
fig, ax = plt.subplots(figsize=(8, 2))
# Create a horizontal gradient (from close to distant)
gradient = np.linspace(0, 1, 256).reshape(1, -1)
ax.imshow(gradient, extent=[0, 1, 0, 1], aspect='auto', cmap='viridis_r')
# Plot the scalar value as a vertical line
ax.axvline(x=scalar_value, color='white', lw=5)
# Add a dot at the scalar position
ax.plot(scalar_value, 0.5, 'o', color='white', markersize=42)
ax.text(scalar_value, 0.5, f'{scalar_value:.2f}', color='black', ha='center', va='center', fontsize=14)
# Add labels rotated 90 degrees on the sides
ax.text(-0.03, 0.5, 'Close', ha='center', va='center', fontsize=14, rotation=90)
ax.text(1.03, 0.5, 'Distant', ha='center', va='center', fontsize=14, rotation=270)
# Customize the axis
ax.set_xticks([]) # Remove x-axis ticks
ax.set_yticks([]) # Remove y-axis ticks
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_title(distance_type)
# Remove spines for a cleaner look
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
return fig
# Show the plot
# plt.tight_layout()
# plt.show()
def load_json_from_path(path):
with open(path, "r", encoding="utf8") as f:
obj = json.loads(f.read())
return obj
class Measurer:
def __init__(self):
# learned dist
tree_lookup_path = "lang_1_to_lang_2_to_learned_dist.json"
self.learned_dist_func = load_json_from_path(tree_lookup_path)
# tree dist
tree_lookup_path = "lang_1_to_lang_2_to_tree_dist.json"
self.tree_dist_func = load_json_from_path(tree_lookup_path)
# map dist
map_lookup_path = "lang_1_to_lang_2_to_map_dist.json"
self.map_dist_func = load_json_from_path(map_lookup_path)
largest_value_map_dist = 0.0
for _, values in self.map_dist_func.items():
for _, value in values.items():
largest_value_map_dist = max(largest_value_map_dist, value)
for key1 in self.map_dist_func:
for key2 in self.map_dist_func[key1]:
self.map_dist_func[key1][key2] = self.map_dist_func[key1][key2] / largest_value_map_dist
# ASP
asp_dict_path = "asp_dict.pkl"
with open(asp_dict_path, 'rb') as dictfile:
asp_sim = pickle.load(dictfile)
lang_list = list(asp_sim.keys())
self.asp_dist_func = dict()
seen_langs = set()
for lang_1 in lang_list:
if lang_1 not in seen_langs:
seen_langs.add(lang_1)
self.asp_dist_func[lang_1] = dict()
for index, lang_2 in enumerate(lang_list):
if lang_2 not in seen_langs: # it's symmetric
self.asp_dist_func[lang_1][lang_2] = 1 - asp_sim[lang_1][index]
def get_dists(self, l1, l2):
if l1 in self.tree_dist_func:
if l2 in self.tree_dist_func[l1]:
tree_dist = self.tree_dist_func[l1][l2]
else:
tree_dist = self.tree_dist_func[l2][l1]
else:
tree_dist = self.tree_dist_func[l2][l1]
if l1 in self.map_dist_func:
if l2 in self.map_dist_func[l1]:
map_dist = self.map_dist_func[l1][l2]
else:
map_dist = self.map_dist_func[l2][l1]
else:
map_dist = self.map_dist_func[l2][l1]
if l1 in self.asp_dist_func:
if l2 in self.asp_dist_func[l1]:
asp_dist = self.asp_dist_func[l1][l2]
else:
asp_dist = self.asp_dist_func[l2][l1]
else:
asp_dist = self.asp_dist_func[l2][l1]
if l1 in self.learned_dist_func:
if l2 in self.learned_dist_func[l1]:
learned_dist = self.learned_dist_func[l1][l2]
else:
learned_dist = self.learned_dist_func[l2][l1]
else:
learned_dist = self.learned_dist_func[l2][l1]
return tree_dist, map_dist, asp_dist, learned_dist
def measure(self, l1, l2):
if l1 == l2:
f1 = plot_scalar_on_scale(0.0, f"Language Family Tree Distance between {l1} and {l2}")
f2 = plot_scalar_on_scale(0.0, f"Distance on the Globe between {l1} and {l2}")
f3 = plot_scalar_on_scale(0.0, f"Distance between Phoneme-Sets between {l1} and {l2}")
f4 = plot_scalar_on_scale(0.0, f"Machine-Learned Distance between {l1} and {l2}")
else:
tree_dist, map_dist, asp_dist, learned_dist = self.get_dists(l1.split(" ")[-1].split("(")[1].split(")")[0],
l2.split(" ")[-1].split("(")[1].split(")")[0])
f1 = plot_scalar_on_scale(tree_dist, f"Language Family Tree Distance between {l1} and {l2}")
f2 = plot_scalar_on_scale(map_dist, f"Distance on the Globe between {l1} and {l2}")
f3 = plot_scalar_on_scale(asp_dist, f"Distance between Phoneme-Sets between {l1} and {l2}")
f4 = plot_scalar_on_scale(learned_dist, f"Machine-Learned Distance between {l1} and {l2}")
return f1, f2, f3, f4
m = Measurer()
iso_to_name = load_json_from_path("iso_to_fullname.json")
text_selection = [f"{iso_to_name[iso_code]} ({iso_code})" for iso_code in iso_to_name]
iface = gr.Interface(fn=m.measure,
inputs=[gr.Dropdown(text_selection,
type="value",
value='English (eng)',
label="Select the fist Language (type on your keyboard to find it quickly)"),
gr.Dropdown(text_selection,
type="value",
value='German (deu)',
label="Select the second Language (type on your keyboard to find it quickly)")],
outputs=[gr.Plot(label="", show_label=False, format="png", container=True),
gr.Plot(label="", show_label=False, format="png", container=True),
gr.Plot(label="", show_label=False, format="png", container=True),
gr.Plot(label="", show_label=False, format="png", container=True)],
description="<br><br> This demo allows you to view the distance between two languages from the ISO 639-3 list according to several distance measurement functions. "
"For more information, check out our paper: https://arxiv.org/abs/2406.06403 and our text-to-speech tool, in which we make use of "
"this technique: https://github.com/DigitalPhonetics/IMS-Toucan <br><br>",
fill_width=True)
iface.launch()
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