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import streamlit as st
import outetts
from scipy.io.wavfile import write
import tempfile
import os
from pydub import AudioSegment
import sounddevice as sd
import wave
import numpy as np
# Initialize model configuration
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en" # Supported languages: en, zh, ja, ko
)
# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)
# Streamlit UI
st.title("OuteTTS Speech Synthesis")
st.write("Enter text below to generate speech.")
# Sidebar for reference voice
st.sidebar.title("Voice Cloning")
reference_audio = st.sidebar.file_uploader("Upload a reference audio (any format)", type=["wav", "mp3", "ogg", "flac", "m4a"])
transcript = st.sidebar.text_area("Transcription of the reference audio")
# Function to convert audio to WAV format
def convert_to_wav(audio_file):
temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
audio = AudioSegment.from_file(audio_file)
audio.export(temp_audio.name, format="wav")
return temp_audio.name
if reference_audio and transcript:
ref_audio_path = convert_to_wav(reference_audio)
# Create speaker profile
speaker = interface.create_speaker(ref_audio_path, transcript)
# Save the speaker profile
interface.save_speaker(speaker, "speaker.json")
else:
speaker = None
# Recording functionality
def record_audio(duration=5, samplerate=44100):
st.sidebar.write("Recording...")
recording = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype=np.int16)
sd.wait()
temp_audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name
with wave.open(temp_audio_path, "wb") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(samplerate)
wf.writeframes(recording.tobytes())
return temp_audio_path
if not speaker:
st.sidebar.write("Or record your voice below:")
if st.sidebar.button("Record Voice"):
ref_audio_path = record_audio()
st.sidebar.success("Recording complete!")
transcript = st.sidebar.text_area("Transcription of the recorded audio")
if transcript:
# Create speaker profile from recorded audio
speaker = interface.create_speaker(ref_audio_path, transcript)
# Save the speaker profile
interface.save_speaker(speaker, "speaker.json")
text_input = st.text_area("Text to convert to speech:", "Hello, this is an AI-generated voice.")
if st.button("Generate Speech"):
with st.spinner("Generating audio..."):
# Generate speech with or without the speaker profile
output = interface.generate(
text=text_input,
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,
speaker=speaker
)
# Save the synthesized speech to a file
output_path = "output.wav"
output.save(output_path)
# Play the audio in the Streamlit app
st.audio(output_path, format="audio/wav")
st.success("Speech generated successfully!")
# Clean up temporary files
if reference_audio:
os.remove(ref_audio_path)
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