Create app.py

app.py

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+import json
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from ArticulatoryTextFrontend import ArticulatoryTextFrontend
+
+
+def visualize_one_hot_encoded_sequence(tensor, sentence, col_labels, cmap='BuGn'):
+    """
+    Visualize a 2D one-hot encoded tensor as a heatmap.
+    """
+    tensor = torch.clamp(tensor, min=0, max=1).transpose(0, 1).cpu().numpy()
+    if tensor.ndim != 2:
+        raise ValueError("Input tensor must be a 2D array")
+
+    # Check the size of labels matches the tensor dimensions
+    row_labels = ["stressed", "very-high-tone", "high-tone", "mid-tone", "low-tone", "very-low-tone", "rising-tone", "falling-tone", "peaking-tone", "dipping-tone", "lengthened", "half-length", "shortened", "consonant", "vowel", "phoneme", "silence", "end of sentence", "questionmark", "exclamationmark", "fullstop", "word-boundary", "dental", "postalveolar",
+                  "velar", "palatal", "glottal", "uvular", "labiodental", "labial-velar", "alveolar", "bilabial", "alveolopalatal", "retroflex", "pharyngal", "epiglottal", "central", "back", "front_central", "front", "central_back", "mid", "close-mid", "close", "open-mid", "close_close-mid", "open-mid_open", "open", "rounded", "unrounded", "plosive",
+                  "nasal", "approximant", "trill", "flap", "fricative", "lateral-approximant", "implosive", "vibrant", "click", "ejective", "aspirated", "unvoiced", "voiced"]
+
+    if row_labels and len(row_labels) != tensor.shape[0]:
+        raise ValueError("Number of row labels must match the number of rows in the tensor")
+    if col_labels and len(col_labels) != tensor.shape[1]:
+        raise ValueError("Number of column labels must match the number of columns in the tensor")
+
+    fig, ax = plt.subplots(figsize=(16, 16))
+
+    # Create the heatmap
+    ax.imshow(tensor, cmap=cmap, aspect='auto')
+
+    # Add labels
+    if row_labels:
+        ax.set_yticks(np.arange(tensor.shape[0]), row_labels)
+    if col_labels:
+        ax.set_xticks(np.arange(tensor.shape[1]), col_labels, rotation=0)
+
+    ax.grid(False)
+    ax.set_xlabel('Phones')
+    ax.set_ylabel('Features')
+
+    # Display the heatmap
+    ax.set_title(f"»{sentence}«")
+    return fig
+
+
+def vis_wrapper(sentence, language):
+    tf = ArticulatoryTextFrontend(language=language.split(" ")[-1].split("(")[1].split(")")[0])
+    features = tf.string_to_tensor(sentence)
+    phones = tf.get_phone_string(sentence)
+
+    return visualize_one_hot_encoded_sequence(tensor=features, sentence=sentence, col_labels=phones)
+
+
+def load_json_from_path(path):
+    with open(path, "r", encoding="utf8") as f:
+        obj = json.loads(f.read())
+
+    return obj
+
+
+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=vis_wrapper,
+                     inputs=[gr.Textbox(lines=2,
+                                        placeholder="write the sentence you want to visualize here...",
+                                        value="What I cannot create, I do not understand.",
+                                        label="Text input"),
+                             gr.Dropdown(text_selection,
+                                         type="value",
+                                         value='English (eng)',
+                                         label="Select the Language of the Text (type on your keyboard to find it quickly)")],
+                     outputs=[gr.Plot()],
+                     allow_flagging="never",
+                     live=False,
+                     fill_width=True)
+iface.launch()