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import gradio as gr
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1")
tts.to("cuda")
def predict(prompt, language, audio_file_pth):
tts.tts_to_file(
text=prompt,
file_path="output.wav",
speaker_wav=audio_file_pth,
language=language,
)
return gr.make_waveform(
audio="output.wav",
)
title = "Coqui🐸 XTTS"
description = """
<p>For faster inference without waiting in the queue, you should duplicate this space and upgrade to GPU via the settings.
<br/>
<a href="https://huggingface.co/spaces/coqui/xtts?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
</p>
XTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 3-second audio clip.
Built on Tortoise, XTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy.
<br/>
This is the same model that powers Coqui Studio, and Coqui API, however we apply a few tricks to make it faster and support streaming inference.
"""
gr.Interface(
fn=predict,
inputs=[
gr.Textbox(
label="Text Prompt",
info="One or two sentences at a time is better",
placeholder="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
),
gr.Dropdown(
label="Language",
info="Select an output language for the synthesised speech",
choices=[
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cz",
"ar",
"zh",
],
max_choices=1,
value="en"
),
gr.Audio(
label="Reference Audio",
info="Upload a reference audio for target speaker voice",
type="filepath",
value="examples/en_speaker_6.wav"
),
],
outputs=[
gr.Video(label="Synthesised Speech"),
],
title=title,
).launch(debug=True)
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