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"""Simple demo app.
Copyright PolyAI Limited.
"""
import time
from pathlib import Path
import gradio as gr
from transformer_infer import PhemeClient, parse_arguments
VOICE_OPTIONS = [
"male_voice",
"POD1000000004_S0000246",
"POD1000000018_S0000253",
"POD1000000048_S0000035",
"YOU1000000006_S0000051",
"YOU1000000044_S0000798",
]
args = parse_arguments()
model = PhemeClient(args)
def inference(
text,
voice,
top_k,
temperature
):
with open("PhemeVoice.log", "a") as f:
f.write(f"{voice}: {text} \n")
start_time = time.time()
data = model.infer(
text, voice, top_k=top_k, temperature=temperature)
samplerate = 16_000
print("Time taken: ", time.time() - start_time)
yield (samplerate, data)
def main():
title = "Pheme"
description = """Pheme Model can generate a variety of conversational voices in 16 kHz for phone-call applications.
Paper: https://arxiv.org/pdf/2401.02839.pdf
Github: https://github.com/PolyAI-LDN/pheme
"""
text = gr.Textbox(
lines=3,
value="I gotta say, I never expect that to happened. Um I had some expectations but you know.",
label="Text",
)
voice = gr.Dropdown(
VOICE_OPTIONS, value="POD1000000048_S0000035", label="Select voice:", type="value"
)
temperature = gr.Slider(minimum=.3, maximum=1.5, value=0.7, step=0.05)
top_k = gr.Slider(minimum=10, maximum=250, value=210)
output_audio = gr.Audio(label="audio:", autoplay=True)
interface = gr.Interface(
fn=inference,
inputs=[
text,
voice,
top_k,
temperature,
],
title=title,
description=description,
outputs=[output_audio],
)
interface.queue().launch(share=True)
if __name__ == "__main__":
main()
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