MeloTTS / app.py
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import gradio as gr
import os, torch, io
import sys
#os.system('python -m unidic download')
from melo.api import TTS
speed = 1.0
import tempfile
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
languages = ["EN", "ES", "FR", "ZH", "JP", "KR"]
en = ["EN-Default", "EN-US", "EN-BR", "EN_INDIA", "EN-AU"]
LANG = sys.argv[1]
def synthesize(speaker, text, speed=1.0, progress=gr.Progress()):
model = TTS(language=LANG, device=device)
speaker_ids = model.hps.data.spk2id
bio = io.BytesIO()
model.tts_to_file(text, speaker_ids[speaker], bio, speed=speed, pbar=progress.tqdm, format='wav')
return bio.getvalue()
with gr.Blocks() as demo:
with gr.Group():
if LANG == "EN":
speaker = gr.Dropdown(en, interactive=True, value='EN-Default', label='Speaker')
else:
speaker = gr.Dropdown([LANG], interactive=True, value=LANG, label='Speaker')
speed = gr.Slider(label='Speed', minimum=0.1, maximum=10.0, value=1.0, interactive=True, step=0.1)
text = gr.Textbox(label="Text to speak", value='The field of text to speech has seen rapid development recently')
btn = gr.Button('Synthesize', variant='primary')
aud = gr.Audio(interactive=False)
btn.click(synthesize, inputs=[speaker, text, speed], outputs=[aud])
demo.queue(api_open=False, default_concurrency_limit=10).launch(show_api=False)