app.py

import gradio as gr
import torch
import numpy as np
import scipy.io.wavfile
from transformers import VitsModel, AutoTokenizer
import re

# Load model and tokenizer
model = VitsModel.from_pretrained("Somali-tts/somali_tts_model")
tokenizer = AutoTokenizer.from_pretrained("saleolow/somali-mms-tts")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Numbers in Somali
number_words = {
    0: "eber", 1: "koow", 2: "labo", 3: "seddex", 4: "afar", 5: "shan",
    6: "lix", 7: "todobo", 8: "sideed", 9: "sagaal", 10: "toban",
    11: "toban iyo koow", 12: "toban iyo labo", 13: "toban iyo seddex",
    14: "toban iyo afar", 15: "toban iyo shan", 16: "toban iyo lix",
    17: "toban iyo todobo", 18: "toban iyo sideed", 19: "toban iyo sagaal",
    20: "labaatan", 30: "sodon", 40: "afartan", 50: "konton",
    60: "lixdan", 70: "todobaatan", 80: "sideetan", 90: "sagaashan",
    100: "boqol", 1000: "kun"
}

def number_to_words(number):
    number = int(number)
    if number < 20:
        return number_words[number]
    elif number < 100:
        tens, unit = divmod(number, 10)
        return number_words[tens * 10] + (" iyo " + number_words[unit] if unit else "")
    elif number < 1000:
        hundreds, remainder = divmod(number, 100)
        part = (number_words[hundreds] + " boqol") if hundreds > 1 else "boqol"
        if remainder:
            part += " iyo " + number_to_words(remainder)
        return part
    elif number < 1000000:
        thousands, remainder = divmod(number, 1000)
        words = []
        if thousands == 1:
            words.append("kun")
        else:
            words.append(number_to_words(thousands) + " kun")
        if remainder >= 100:
            hundreds, rem2 = divmod(remainder, 100)
            if hundreds:
                boqol_text = (number_words[hundreds] + " boqol") if hundreds > 1 else "boqol"
                words.append(boqol_text)
            if rem2:
                words.append("iyo " + number_to_words(rem2))
        elif remainder:
            words.append("iyo " + number_to_words(remainder))
        return " ".join(words)
    elif number < 1000000000:
        millions, remainder = divmod(number, 1000000)
        words = []
        if millions == 1:
            words.append("milyan")
        else:
            words.append(number_to_words(millions) + " milyan")
        if remainder:
            words.append(number_to_words(remainder))
        return " ".join(words)
    else:
        return str(number)

def normalize_text(text):
    text = re.sub(r'(\d{1,3})(,\d{3})+', lambda m: m.group(0).replace(",", ""), text)
    text = re.sub(r'\.\d+', '', text)
    def replace_num(match):
        return number_to_words(match.group())
    text = re.sub(r'\d+', replace_num, text)
    symbol_map = {
        '$': 'doolar',
        '=': 'egwal',
        '+': 'balaas',
        '#': 'haash'
    }
    for sym, word in symbol_map.items():
        text = text.replace(sym, ' ' + word + ' ')
    text = text.replace("KH", "qa").replace("Z", "S")
    text = text.replace("SH", "SHa'a").replace("DH", "Dha'a")
    text = text.replace("ZamZam", "SamSam")
    return text

def tts(text):
    paragraphs = text.strip().split("\n")
    audio_list = []
    max_chars = 500  # Qiyaasta ugu badan 2 daqiiqo
    warn_msg = ""

    for i, para in enumerate(paragraphs):
        para = para.strip()
        if not para:
            continue

        if len(para) > max_chars:
            warn_msg += f"❗ Qaybta {i+1} aad ayaa ka badan 2 daqiiqo. Waan kala jaray.\n"
            sub_parts = [para[j:j+max_chars] for j in range(0, len(para), max_chars)]
        else:
            sub_parts = [para]

        for part in sub_parts:
            norm_para = normalize_text(part)
            inputs = tokenizer(norm_para, return_tensors="pt").to(device)
            with torch.no_grad():
                waveform = model(**inputs).waveform.squeeze().cpu().numpy()

            pause = np.zeros(int(model.config.sampling_rate * 0.8))  # 0.8s pause
            audio_list.append(np.concatenate((waveform, pause)))

    final_audio = np.concatenate(audio_list)
    filename = "output.wav"
    scipy.io.wavfile.write(filename, rate=model.config.sampling_rate, data=(final_audio * 32767).astype(np.int16))

    if warn_msg:
        print(warn_msg)
    return filename

# Gradio interface
gr.Interface(
    fn=tts,
    inputs=gr.Textbox(label="Geli qoraal Soomaali ah", lines=10, placeholder="Ku qor 1 ama in ka badan paragraph..."),
    outputs=gr.Audio(label="Codka TTS"),
    title="Somali TTS",
    description="Ku qor qoraal Soomaaliyeed si aad u maqasho cod dabiici ah. Qoraalka ha ka badnaan 2 daqiiqo per jumlad."
).launch()