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import streamlit as st
import torch
from TTS.api import TTS
import os
import tempfile
os.environ["COQUI_TOS_AGREED"] = "1"
device = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize TTS model
@st.cache_resource
def load_tts_model():
return TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
tts = load_tts_model()
def clone(text, audio_file, language, speaking_rate, pitch, volume,
emotion, sample_rate, temperature, seed):
if seed is not None:
torch.manual_seed(seed)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
temp_audio_path = temp_audio.name
tts.tts_to_file(
text=text,
speaker_wav=audio_file,
language=language,
file_path=temp_audio_path
)
return temp_audio_path
st.title('Advanced Voice Clone')
st.write('Customize your voice cloning experience with various parameters.')
text = st.text_area('Text')
audio_file = st.file_uploader('Voice reference audio file', type=['wav', 'mp3'])
language = st.selectbox('Language', ["en", "es", "fr", "de", "it"], index=0)
speaking_rate = st.slider('Speaking Rate', 0.5, 2.0, 1.0)
pitch = st.slider('Pitch Adjustment', -10, 10, 0)
volume = st.slider('Volume', 0.1, 2.0, 1.0)
emotion = st.selectbox('Emotion', ["neutral", "happy", "sad", "angry"], index=0)
sample_rate = st.selectbox('Sample Rate', [22050, 24000, 44100, 48000], index=1)
temperature = st.slider('Temperature', 0.1, 1.0, 0.8)
seed = st.number_input('Seed (optional)', value=None)
if st.button('Generate'):
if text and audio_file:
with st.spinner('Generating audio...'):
output_path = clone(text, audio_file, language, speaking_rate, pitch, volume,
emotion, sample_rate, temperature, seed)
st.audio(output_path)
else:
st.warning('Please provide both text and a voice reference audio file.')
# Clean up temporary files
for file in os.listdir():
if file.endswith('.wav') and file.startswith('tmp'):
os.remove(file) |