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import streamlit as st
import numpy as np
import torch
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from io import StringIO
import soundfile as sf
# Load models outside of function calls for efficiency
@st.cache_data
def load_models():
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
return model, processor, vocoder
model, processor, vocoder = load_models()
# Load speaker embeddings
@st.cache_data
def get_speaker_embeddings():
speaker_embeddings = np.load("cmu_us_clb_arctic-wav-arctic_a0144.npy")
return torch.tensor(speaker_embeddings).unsqueeze(0)
speaker_embeddings = get_speaker_embeddings()
# Improved Styling
def local_css(file_name):
with open(file_name) as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
local_css("style.css")
# Streamlined Layout
st.title("Text-to-Voice Conversion")
st.markdown("Convert your text to speech using advanced AI models.")
# Function to convert text to speech
def text_to_speech(text):
try:
# Segment the text if it's too long
max_length = 100 # Set a max length as per model's capability
segments = [text[i:i+max_length] for i in range(0, len(text), max_length)]
audio_paths = []
for segment in segments:
inputs = processor(text=segment, return_tensors="pt")
spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
with torch.no_grad():
speech = vocoder(spectrogram)
audio_path = f"speech_segment_{len(audio_paths)}.wav"
sf.write(audio_path, speech.numpy(), samplerate=16000)
audio_paths.append(audio_path)
return audio_paths
except Exception as e:
st.error(f"Error in text-to-speech conversion: {e}")
return []
# Function to combine audio segments
def combine_audio_segments(paths):
combined_speech = []
for path in paths:
data, samplerate = sf.read(path)
combined_speech.extend(data)
sf.write("combined_speech.wav", np.array(combined_speech), samplerate)
return "combined_speech.wav"
# Text Input
text = st.text_area("Type your text or upload a text file below.")
# Convert Button
if st.button("Convert"):
if text:
audio_paths = text_to_speech(text)
combined_audio_path = combine_audio_segments(audio_paths)
audio_file = open(combined_audio_path, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
else:
st.error("Please enter some text to convert.")
# File Uploader
uploaded_file = st.file_uploader("Upload your text file here", type=['txt'])
if uploaded_file is not None:
stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
text = stringio.read()
st.write(text)
if st.button("Convert Uploaded File", key=1):
audio_paths = text_to_speech(text)
combined_audio_path = combine_audio_segments(audio_paths)
audio_file = open(combined_audio_path, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav') |