app.py

import gradio as gr
import torch
import soundfile as sf
import spaces
import os
import numpy as np
import re
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, AutoTokenizer
from speechbrain.pretrained import EncoderClassifier
from datasets import load_dataset
from huggingface_hub import hf_hub_download
import uuid
import wave


from piper import PiperVoice


device = "cuda" if torch.cuda.is_available() else "cpu"


auth_token = os.environ.get("hf_token") or True

model_path = hf_hub_download(repo_id="fahadqazi/piper-sindhi", filename="model.onnx", use_auth_token=auth_token)
config_path = hf_hub_download(repo_id="fahadqazi/piper-sindhi", filename="model.onnx.json", use_auth_token=auth_token)


voice = PiperVoice.load(model_path=model_path, config_path=config_path, use_cuda=device=="cuda")

synthesize_args = {
    "speaker_id": 0,
    "sentence_silence": 0.5
}


def load_models_and_data():
    auth_token = os.environ.get("hf_token") or True
    
    model_name = "microsoft/speecht5_tts"
    processor = SpeechT5Processor.from_pretrained(model_name)
    
    tokenizer = AutoTokenizer.from_pretrained("fahadqazi/testts1234", use_auth_token=auth_token)
    processor.tokenizer = tokenizer

    model = SpeechT5ForTextToSpeech.from_pretrained("fahadqazi/testts1234", use_auth_token=auth_token).to(device)
    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

    
    return model, processor, vocoder

# model, processor, vocoder = load_models_and_data()

# embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
# speaker_embeddings = embeddings_dataset[7306]["xvector"]
# speaker_embeddings = torch.tensor(speaker_embeddings).to(device)

# default_embedding = speaker_embeddings 

replacements = [
    # ("۾", "مين"),  # 
    # ("۽", "ائين"),  # 
]

number_words = {
    0: "ٻڙي", 
    1: "هڪ", 
    2: "بہ", 
    3: "ٽي", 
    4: "چار", 
    5: "پنج", 
    6: "ڇه", 
    7: "ست", 
    8: "اٺ", 
    9: "نوه",
    10: "ڏهہ", 
    11: "يارنهن", 12: "ٻارنهن", 13: "تيرنهن", 14: "چوڏنهن", 15: "پنڌرنهن", 16: "سورنهن", 17: "سترنهن",
    18: "ارڙنهن", 19: "اوڻينهن", 20: "ويهہ", 30: "ٽيهہ", 40: "چاليهہ", 50: "پنجها", 60: "سٺ", 70: "ستر",
    80: "اسي", 90: "نوي", 100: "سوه", 1000: "هزار"
}

def number_to_words(number):
    if number < 20:
        return number_words[number]
    elif number < 100:
        tens, unit = divmod(number, 10)
        return (number_words[unit] if unit else "") + (" " + number_words[tens * 10])
    elif number < 1000:
        hundreds, remainder = divmod(number, 100)
        return (number_words[hundreds] + " سوه" if hundreds > 1 else "سوه") + (" " + number_to_words(remainder) if remainder else "")
    elif number < 1000000:
        thousands, remainder = divmod(number, 1000)
        return (number_to_words(thousands) + " هزار" if thousands > 1 else "هزار") + (" " + number_to_words(remainder) if remainder else "")
    elif number < 1000000000:
        millions, remainder = divmod(number, 1000000)
        return number_to_words(millions) + " ملين" + (" " + number_to_words(remainder) if remainder else "")
    elif number < 1000000000000:
        billions, remainder = divmod(number, 1000000000)
        return number_to_words(billions) + " بلين" + (" " + 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 normalize_text(text):
    # Convert to lowercase
    text = text.lower()

    # Replace numbers followed by "ع" with "عيسوي"
    text = re.sub(r'(\d+)\s*ع', r'\1 عيسوي', text)
    
    # Replace numbers with words
    text = replace_numbers_with_words(text)
    
    # Apply character replacements
    for old, new in replacements:
        text = text.replace(old, new)
    
    # # Remove punctuation
    # text = re.sub(r'[^\w\s]', '', text)
    
    return text

@spaces.GPU(duration=60)
def text_to_speech(text, audio_file=None):
    # Normalize the input text
    normalized_text = normalize_text(text)

    print("normalized text: ", normalized_text)
    
    # # Prepare the input for the model
    # inputs = processor(text=normalized_text, return_tensors="pt").to(device)
    
    # # Use the default speaker embedding
    # speaker_embeddings = default_embedding
    
    # # Generate speech
    # with torch.no_grad():
    #     speech = model.generate_speech(inputs["input_ids"], speaker_embeddings.unsqueeze(0), vocoder=vocoder)
    
    # speech_np = speech.cpu().numpy()

    # return (16000, speech_np)

    # Generate speech: Write to file
    output_file = f"{uuid.uuid4()}.wav"
    with wave.open(output_file, "wb") as wav_file:
        voice.synthesize(normalized_text, wav_file, **synthesize_args)
    
    # Save the audio to a file
    # with open("output.wav", "wb") as f:
    #     f.write(audio)

    # return audio_file

    return output_file

    
iface = gr.Interface(
    fn=text_to_speech,
    inputs=[
        gr.Textbox(label="Enter Sindhi text to convert to speech", value="هيلو ڪهڙا حال آهن")
    ],
    outputs=[
        gr.Audio(label="Generated Speech", type="numpy")
    ],
    title="Sindhi SpeechT5 Text-to-Speech Demo",
    description="Enter Sindhi text, and listen to the generated speech."
)

iface.launch(share=True)