app.py

import re
import pandas as pd
from phonikud_onnx import Phonikud
from phonikud import phonemize
from piper_onnx import Piper
import soundfile as sf
import gradio as gr


phonikud_onnx = Phonikud("phonikud-1.0.int8.onnx")
piper = Piper('tts-model.onnx', 'tts-model.config.json')

# Letter-to-phoneme mapping for acronyms
LETTER_TO_PHONEME = {
    'א': 'ʔa', 'ב': 'ba', 'ג': 'ɡa', 'ד': 'da', 'ה': 'ha', 'ו': 'va',
    'ז': 'za', 'ח': 'χa', 'ט': 'ta', 'י': 'ja', 'כ': 'ka', 'ל': 'la',
    'מ': 'ma', 'נ': 'na', 'ס': 'sa', 'ע': 'ʔa', 'פ': 'pa', 'צ': 'tsa',
    'ק': 'ka', 'ר': 'ʁa', 'ש': 'ʃa', 'ת': 'ta', 'ם': 'm', 'ן': 'n',
    'ף': 'f', 'ך': 'χ', 'ץ': 'ts'
}

# Split text into acronyms and regular chunks
def split_text(text):
    words = []
    tokens = text.split()
    for token in tokens:
        if re.sub(r'[״":,.!?]', '', token) in acronym_dict:
            words.append(("in_dict", re.sub(r'[״":,.!?]', '', token))) # Remove quotes here
        elif re.search(r'\w+["״]\w+', token):
            words.append(("acronym", token))
        elif token.strip():
            words.append(("text", token))
    return words


def handle_acronym(acronym):
    acronym = re.sub(r'[״":,.!?]', '', acronym) # Remove quotes here
    # Convert all letters except the last one
    phonemes = ''.join(LETTER_TO_PHONEME.get(letter, letter) for letter in acronym[:-1])

    # Convert the last letter and remove trailing 'a' if present
    last_phoneme = LETTER_TO_PHONEME.get(acronym[-1], acronym[-1]).replace('a', '')  # Removes ALL 'a's
    phonemes += last_phoneme

    print(f"Acronym: {acronym} → Phonemes: {phonemes}")  # Optional debug
    return phonemes

def convert_txt_to_phonemes(text):
    result = []
    for kind, chunk in split_text(text):
        if kind== "in_dict":
            print(f"Found! {chunk} → {acronym_dict[chunk]}")
            result.append(acronym_dict[chunk])
        elif kind == "acronym":
            result.append(handle_acronym(chunk))
        else:
            diacritized = phonikud_onnx.add_diacritics(chunk)
            result.append(phonemize(diacritized))
    phonemes = ' '.join(result)
    print(phonemes)
    return phonemes

def heb_to_speech(text, temp_word=" רות", temp_duration=0.36):
    # Step 1: Add temporary word
    text_with_temp = text + temp_word
    phonemes = convert_txt_to_phonemes(text_with_temp)

    # Step 2: Generate audio
    samples, sample_rate = piper.create(phonemes, is_phonemes=True)
    sf.write("raw_audio.wav", samples, sample_rate)

    # Step 3: Trim temporary word
    trim_samples = int(temp_duration * sample_rate)
    trimmed_samples = samples[:-trim_samples]
    sf.write("final_audio.wav", trimmed_samples, sample_rate)
    return "final_audio.wav"


acronym_df = pd.read_excel("acronym-phonemes-dict.xlsx")
acronym_dict = {row['acronym']: row['phonemes'] for _, row in acronym_df.iterrows()}

with gr.Blocks() as demo:
    text_input = gr.Textbox(label="Insert Hebrew text", lines=2)
    generate_btn = gr.Button("Generate")
    audio_output = gr.Audio(label="🔊", type="filepath", interactive=False)

    generate_btn.click(fn=heb_to_speech, inputs=text_input, outputs=audio_output)

demo.launch(share=True)