Create app.py

app.py

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+DESCR = """
+# TTS Arena
+
+Vote on different speech synthesis models!
+
+## Instructions
+
+* Listen to two anonymous models
+* Vote on which one is more natural and realistic
+* If there's a tie, click Skip
+
+*IMPORTANT: Do not only rank the outputs based on naturalness. Also rank based on intelligibility (can you actually tell what they're saying?) and other factors (does it sound like a human?).*
+
+**When you're ready to begin, click the Start button below!** The model names will be revealed once you vote.
+""".strip()
+import gradio as gr
+import random
+import os
+from datasets import load_dataset
+dataset = load_dataset("ttseval/tts-arena", token=os.getenv('HF_TOKEN'))
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+model_names = {
+    'styletts2': 'StyleTTS 2',
+    'tacotron': 'Tacotron',
+    'speedyspeech': 'Speedy Speech',
+    'overflow': 'Overflow TTS',
+    'vits': 'VITS',
+    'vitsneon': 'VITS Neon',
+    'neuralhmm': 'Neural HMM',
+    'glow': 'Glow TTS',
+    'fastpitch': 'FastPitch',
+}
+def get_random_split(existing_split=None):
+    choice = random.choice(list(dataset.keys()))
+    if existing_split and choice == existing_split:
+        return get_random_split(choice)
+    else:
+        return choice
+def get_random_splits():
+    choice1 = get_random_split()
+    choice2 = get_random_split(choice1)
+    return (choice1, choice2)
+def a_is_better(model1, model2):
+    chosen_model = model1
+    print(chosen_model)
+    return reload(model1, model2)
+def b_is_better(model1, model2):
+    chosen_model = model2
+    print(chosen_model)
+    return reload(model1, model2)
+def reload(chosenmodel1=None, chosenmodel2=None):
+    # Select random splits
+    split1, split2 = get_random_splits()
+    d1, d2 = (dataset[split1], dataset[split2])
+    choice1, choice2 = (d1.shuffle()[0]['audio'], d2.shuffle()[0]['audio'])
+    if split1 in model_names:
+        split1 = model_names[split1]
+    if split2 in model_names:
+        split2 = model_names[split2]
+    out = [
+        (choice1['sampling_rate'], choice1['array']),
+        (choice2['sampling_rate'], choice2['array']),
+        split1,
+        split2
+    ]
+    if chosenmodel1: out.append(f'This model was {chosenmodel1}')
+    if chosenmodel2: out.append(f'This model was {chosenmodel2}')
+    return out
+with gr.Blocks(theme=theme) as demo:
+# with gr.Blocks() as demo:
+    gr.Markdown(DESCR)
+    with gr.Row():
+        gr.HTML('<div align="left"><h3>Model A</h3></div>')
+        gr.HTML('<div align="right"><h3>Model B</h3></div>')
+    model1 = gr.Textbox(interactive=False, visible=False)
+    model2 = gr.Textbox(interactive=False, visible=False)
+    with gr.Group():
+        with gr.Row():
+            prevmodel1 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model A")
+            prevmodel2 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model B", text_align="right")
+        with gr.Row():
+            aud1 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})
+            aud2 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})
+        with gr.Row():
+            abetter = gr.Button("A is Better", scale=3)
+            skipbtn = gr.Button("Skip", scale=1)
+            bbetter = gr.Button("B is Better", scale=3)
+    outputs = [aud1, aud2, model1, model2, prevmodel1, prevmodel2]
+    abetter.click(a_is_better, outputs=outputs, inputs=[model1, model2])
+    bbetter.click(b_is_better, outputs=outputs, inputs=[model1, model2])
+    skipbtn.click(b_is_better, outputs=outputs, inputs=[model1, model2])
+    demo.load(reload, outputs=[aud1, aud2, model1, model2])
+demo.queue(api_open=False).launch(show_api=False)