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import torch
import re
import gradio as gr
import soundfile as sf
import numpy as np
from transformers import SpeechT5HifiGan
from IPython.display import Audio
from transformers import SpeechT5ForTextToSpeech
from transformers import SpeechT5Processor
# helper function
number_words = {
0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight", 9: "nine",
10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 14: "fourteen", 15: "fifteen", 16: "sixteen", 17: "seventeen",
18: "eighteen", 19: "nineteen", 20: "twenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy",
80: "eighty", 90: "ninety", 100: "hundred", 1000: "thousand"
}
replacements = [
("“", '"'),
("”", '"'),
("’", ","),
("_", " "),
("\xa0", " "),
("\n", " "),
("$","dollar"),
("%","percent"),
("&","and"),
("*","star"),
("+","plus"),
("—","-")
]
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] + " hundred" if hundreds > 1 else "hundred") + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000:
thousands, remainder = divmod(number, 1000)
return (number_to_words(thousands) + " thousand" if thousands > 1 else "thousand") + (" " + 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) + " billion" + (" " + 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)
# Find the numbers and change with words.
result = re.sub(r'\b\d+\b', replace, text)
return result
def cleanup_text(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
model = SpeechT5ForTextToSpeech.from_pretrained(
"Yassmen/speecht5_finetuned_english_tehnical"
)
checkpoint = "microsoft/speecht5_tts"
processor = SpeechT5Processor.from_pretrained(checkpoint)
def generate_wav_file(text):
try:
converted_text = replace_numbers_with_words(text)
cleaned_text = cleanup_text(converted_text)
final_text = normalize_text(cleaned_text)
inputs = processor(text=final_text, return_tensors="pt")
speaker_embeddings = torch.tensor(np.load('speaker_embedding.npy'))
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
return Audio(speech.numpy(), rate=16000)
except Exception as e:
print(f"Error: {e}")
return None
iface = gr.Interface(
fn=generate_wav_file,
inputs=gr.Textbox(lines=3, label="Enter text to convert to speech"),
outputs="audio",
title="Text-to-Speech Technical EN"
)
if __name__ == "__main__":
iface.launch()
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