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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, css="footer {visibility: hidden}") 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) |