add app

app.py

@@ -0,0 +1,155 @@
+import os
+import gradio as gr
+import json
+import requests
+import random
+
+labels = ["Real Audio 🗣️", "Cloned Audio 🤖"]
+DURATION = 2
+
+
+def get_accuracy(score_matrix) -> str:
+
+    correct = score_matrix[0][0] + score_matrix[1][1]
+    total = sum(score_matrix[0]) + sum(score_matrix[1])
+    if total == 0:
+        return ""
+
+    accuracy = correct / total * 100
+    return f"{accuracy:.2f}%"
+
+
+def audio_link(path: str, model: str):
+    """Get the link to the audio file for a given path and model."""
+    return f"https://huggingface.co/datasets/jerpint/vox-cloned-data/resolve/main/{model}/{path}?download=true"
+
+
+def confusion_matrix_to_markdown(matrix, labels=None):
+    num_labels = len(matrix)
+    labels = labels or [f"Class {i}" for i in range(num_labels)]
+    accuracy = get_accuracy(matrix)
+
+    # Header row
+    markdown = f"| {' | '.join([''] + labels)} |\n"
+    markdown += f"| {' | '.join(['---'] * (num_labels + 1))} |\n"
+
+    # Data rows
+    for i, row in enumerate(matrix):
+        markdown += f"| {labels[i]} | " + " | ".join(map(str, row)) + " |\n"
+
+    markdown += f"\nAccuracy %: {accuracy}\n"
+
+    return markdown
+
+
+def load_and_cache_data():
+    json_link = "https://huggingface.co/datasets/jerpint/vox-cloned-data/resolve/main/files.json?download=true"
+    local_file = "files.json"
+
+    if not os.path.exists(local_file):
+        json_file = requests.get(json_link)
+        if json_file.status_code != 200:
+            raise Exception(f"Failed to load data from {json_link}")
+
+        # Cache the file
+        with open(local_file, "w") as f:
+            f.write(json_file.text)
+
+    with open(local_file, "r") as f:
+        return json.load(f)
+
+
+def load_data():
+    json_link = "https://huggingface.co/datasets/jerpint/vox-cloned-data/resolve/main/files.json?download=true"
+    json_file = requests.get(json_link)
+    if json_file.status_code != 200:
+        raise Exception(f"Failed to load data from {json_link}")
+    print("Loaded data")
+    return json.loads(json_file.text)
+
+
+def select_random_model(path):
+    """Select a random model from the list of models for a given path.
+    Will select commonvoice 50% of the time, and a random other model 50% of the time.
+    """
+    if random.random() < 0.5:
+        return "commonvoice"
+    else:
+        other_models = [m for m in data[path] if m != "commonvoice"]
+        return random.choice(other_models)
+
+
+def get_random_audio():
+    path = random.choice(paths)
+    model = select_random_model(path)
+    return path, model
+
+
+def next_audio():
+    new_audio = get_random_audio()
+    audio_cmp = gr.Audio(audio_link(new_audio[0], new_audio[1]))
+    return audio_cmp, new_audio
+
+
+data = load_data()
+
+# Keep only samples with minimum 2 sources
+data = {path: data[path] for path in data if len(data[path]) >= 2}
+
+# List all available paths
+paths = list(data.keys())
+
+
+with gr.Blocks() as demo:
+    current_audio = gr.State(get_random_audio)
+    score_matrix = gr.State([[0, 0], [0, 0]])
+
+    with gr.Column():
+        with gr.Row():
+            audio_cmp = gr.Audio(
+                audio_link(current_audio.value[0], current_audio.value[1])
+            )
+    with gr.Column():
+        with gr.Row():
+            button1 = gr.Button("Real Audio 🗣️")
+            button2 = gr.Button("Cloned Audio 🤖")
+
+    score_md = gr.Markdown(confusion_matrix_to_markdown(score_matrix.value, labels))
+
+    @gr.on(
+        triggers=[button1.click],
+        inputs=[current_audio, score_matrix],
+        outputs=[audio_cmp, current_audio, score_matrix, score_md],
+    )
+    def check_result(x, score_matrix):
+        is_correct = x[1] == "commonvoice"
+        audio_cmp, current_audio = next_audio()
+        if is_correct:
+            gr.Info("Correct! Real Audio", duration=DURATION)
+            score_matrix[0][0] += 1
+        else:
+            gr.Warning("Incorrect! Cloned Audio", duration=DURATION)
+            score_matrix[0][1] += 1
+
+        score_md = confusion_matrix_to_markdown(score_matrix, labels)
+        return audio_cmp, current_audio, score_matrix, score_md
+
+    @gr.on(
+        triggers=[button2.click],
+        inputs=[current_audio, score_matrix],
+        outputs=[audio_cmp, current_audio, score_matrix, score_md],
+    )
+    def check_result(x, score_matrix):
+        is_correct = x[1] != "commonvoice"
+        audio_cmp, current_audio = next_audio()
+        if is_correct:
+            gr.Info("Correct! Cloned Audio", duration=DURATION)
+            score_matrix[1][1] += 1
+        else:
+            gr.Warning("Incorrect! Real Audio", duration=DURATION)
+            score_matrix[1][0] += 1
+        score_md = confusion_matrix_to_markdown(score_matrix, labels)
+        return audio_cmp, current_audio, score_matrix, score_md
+
+
+demo.launch()