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import streamlit as st
from transformers import SeamlessM4Tv2Model, AutoProcessor
import torch
import numpy as np
from scipy.io.wavfile import write
import re
from io import BytesIO
# Load the processor and model
processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Number to words function for Uzbek
number_words = {
0: "nol", 1: "bir", 2: "ikki", 3: "uch", 4: "to'rt", 5: "besh", 6: "olti", 7: "yetti", 8: "sakkiz", 9: "to'qqiz",
10: "o'n", 11: "o'n bir", 12: "o'n ikki", 13: "o'n uch", 14: "o'n to'rt", 15: "o'n besh", 16: "o'n oltı", 17: "o'n yetti",
18: "o'n sakkiz", 19: "o'n toqqiz", 20: "yigirma", 30: "o'ttiz", 40: "qirq", 50: "ellik", 60: "oltmish", 70: "yetmish",
80: "sakson", 90: "to'qson", 100: "yuz", 1000: "ming", 1000000: "million"
}
def number_to_words(number):
if number < 20:
return number_words[number]
elif number < 100:
tens, unit = divmod(number, 10)
return number_words[tens * 10] + (" " + number_words[unit] if unit else "")
elif number < 1000:
hundreds, remainder = divmod(number, 100)
return (number_words[hundreds] + " yuz" if hundreds > 1 else "yuz") + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000:
thousands, remainder = divmod(number, 1000)
return (number_to_words(thousands) + " ming" if thousands > 1 else "ming") + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000000:
millions, remainder = divmod(number, 1000000)
return number_to_words(millions) + " million" + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000000000:
billions, remainder = divmod(number, 1000000000)
return number_to_words(billions) + " milliard" + (" " + number_to_words(remainder) if remainder else "")
else:
return str(number)
def replace_numbers_with_words(text):
def replace(match):
number = int(match.group())
return number_to_words(number)
result = re.sub(r'\b\d+\b', replace, text)
return result
# Replacements
replacements = [
("bo‘ladi", "bo'ladi"),
("yog‘ingarchilik", "yog'ingarchilik"),
]
def cleanup_text(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
# Streamlit App
st.title("Text-to-Speech using Seamless M4T Model")
# User Input
user_input = st.text_area("Enter the text for speech generation", height=200)
# Process the text and generate speech
if st.button("Generate Speech"):
if user_input.strip():
# Apply text transformations
converted_text = replace_numbers_with_words(user_input)
cleaned_text = cleanup_text(converted_text)
# Process input for model
inputs = processor(text=cleaned_text, src_lang="uzn", return_tensors="pt").to(device)
# Generate audio from text
audio_array_from_text = model.generate(**inputs, tgt_lang="uzn")[0].cpu().numpy().squeeze()
# Save to BytesIO
audio_io = BytesIO()
write(audio_io, 16000, audio_array_from_text.astype(np.float32))
audio_io.seek(0)
# Provide audio for playback
st.audio(audio_io, format='audio/wav')
else:
st.warning("Please enter some text to generate speech.")
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