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import streamlit as st
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import soundfile as sf
import random
import time
st.title('Multiply TTS Generator')
text = st.text_input(
label="write your word or sentence",
value="Hi,duino"
)
num_random_voices = st.number_input(
label="Enter the number of random voices",
min_value=1,
value=1,
step=1
)
output_filename = ""
def generate_speech():
global output_filename
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
inputs = processor(text=text, return_tensors="pt")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
total_voices = len(embeddings_dataset)
random_voices = random.sample(range(total_voices), num_random_voices)
combined_speech = []
for index, voice_index in enumerate(random_voices):
speaker_embeddings = torch.tensor(embeddings_dataset[voice_index]["xvector"]).unsqueeze(0)
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
combined_speech.extend(speech.numpy())
if index != len(random_voices) - 1:
# Add a pause of 2 seconds between voices
pause_samples = int(16000 * 2) # 2 seconds at 16kHz sample rate
pause = torch.zeros(pause_samples)
combined_speech.extend(pause)
output_filename = "_".join(text.split()) + "_speech.wav"
sf.write(output_filename, combined_speech, samplerate=16000)
if st.button("Generate"):
generate_speech()
audio_file = open(output_filename, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format="audio/wav")
st.write("Speech generated and saved as: " + output_filename)
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