app.py

import os
import json
import gradio as gr
import shutil
import subprocess
import requests
import tarfile
from pathlib import Path
import soundfile as sf
import sherpa_onnx
import numpy as np


models = [
    ['mms fa','https://huggingface.co/willwade/mms-tts-multilingual-models-onnx/resolve/main/fas'],
    ['coqui-vits-female1-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/persian-tts-female1-vits-coqui'],
    ['coqui-vits-male1-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/persian-tts-male1-vits-coqui'],
    ['coqui-vits-male-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/male-male-coqui-vits'],
    ['coqui-vits-female-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/female-female-coqui-vits'],
    ['coqui-vits-female-GPTInformal-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/female-GPTInformal-coqui-vits'],
    ['coqui-vits-male-SmartGitiCorp','https://huggingface.co/karim23657/persian-tts-vits/tree/main/male-SmartGitiCorp-coqui-vits'],
    ['vits-piper-fa-gyro-medium','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-fa_IR-gyro-medium.tar.bz2'],
    ['piper-fa-amir-medium','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-fa_IR-amir-medium.tar.bz2'],
    ['vits-mimic3-fa-haaniye_low','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-mimic3-fa-haaniye_low.tar.bz2'],
    ['vits-piper-fa_en-rezahedayatfar-ibrahimwalk-medium','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-fa_en-rezahedayatfar-ibrahimwalk-medium.tar.bz2'],
]
models_n =[
  [
    "mms fa", 
    "🌠 راد"
  ],
  [
    "coqui-vits-female1-karim23657", 
    "🌺 نگار"
  ],
  [
    "coqui-vits-male1-karim23657", 
    "🌟 آرش"
  ],
  [
    "coqui-vits-male-karim23657", 
    "🦁 کیان"
  ],
  [
    "coqui-vits-female-karim23657", 
    "🌷 مهتاب"
  ],
  [
    "coqui-vits-female-GPTInformal-karim23657", 
    "🌼 شیوا"
  ],
  [
    "coqui-vits-male-SmartGitiCorp", 
    "🚀 بهمن"
  ],
  [
    "vits-piper-fa-gyro-medium", 
    "💧 نیما"
  ],
  [
    "piper-fa-amir-medium", 
    "⚡️ آریا"
  ],
  [
    "vits-mimic3-fa-haaniye_low", 
    "🌹 ریما"
  ],
  [
    "vits-piper-fa_en-rezahedayatfar-ibrahimwalk-medium", 
    "🌠 پیام"
  ]
]
for i,m in enumerate(models):
    models_n[i][0]=m[1]
dropdown_choices = list([i[1] for i in models_n])
def download_and_extract_model(url, destination):
    """Download and extract the model files."""
    print(f"Downloading from URL: {url}")
    print(f"Destination: {destination}")
    
    # Convert Hugging Face URL format if needed
    if "huggingface.co" in url:
        # Replace /tree/main/ with /resolve/main/ for direct file download
        base_url = url.replace("/tree/main/", "/resolve/main/")
        model_id = base_url.split("/")[-1]
        
        # Check if this is an MMS model
        is_mms_model = True 
        
        if is_mms_model:
            # MMS models have both model.onnx and tokens.txt
            model_url = f"{base_url}/model.onnx"
            tokens_url = f"{base_url}/tokens.txt"
            
            # Download model.onnx
            print("Downloading model.onnx...")
            model_path = os.path.join(destination, "model.onnx")
            response = requests.get(model_url, stream=True)
            if response.status_code != 200:
                raise Exception(f"Failed to download model from {model_url}. Status code: {response.status_code}")
            
            total_size = int(response.headers.get('content-length', 0))
            block_size = 8192
            downloaded = 0
            
            print(f"Total size: {total_size / (1024*1024):.1f} MB")
            with open(model_path, "wb") as f:
                for chunk in response.iter_content(chunk_size=block_size):
                    if chunk:
                        f.write(chunk)
                        downloaded += len(chunk)
                        if total_size > 0:
                            percent = int((downloaded / total_size) * 100)
                            if percent % 10 == 0:
                                print(f" {percent}%", end="", flush=True)
            print("\nModel download complete")
            
            # Download tokens.txt
            print("Downloading tokens.txt...")
            tokens_path = os.path.join(destination, "tokens.txt")
            response = requests.get(tokens_url, stream=True)
            if response.status_code != 200:
                raise Exception(f"Failed to download tokens from {tokens_url}. Status code: {response.status_code}")
            
            with open(tokens_path, "wb") as f:
                f.write(response.content)
            print("Tokens download complete")
            
            return
        else:
            # Other models are stored as tar.bz2 files
            url = f"{base_url}.tar.bz2"
        
        # Try the URL
        response = requests.get(url, stream=True)
        if response.status_code != 200:
            raise Exception(f"Failed to download model from {url}. Status code: {response.status_code}")
        
        # Check if this is a Git LFS file pointer
        content_start = response.content[:100].decode('utf-8', errors='ignore')
        if content_start.startswith('version https://git-lfs.github.com/spec/v1'):
            raise Exception(f"Received Git LFS pointer instead of file content from {url}")
    
    # Create model directory if it doesn't exist
    os.makedirs(destination, exist_ok=True)
    
    # For non-MMS models, handle tar.bz2 files
    tar_path = os.path.join(destination, "model.tar.bz2")
    
    # Download the file
    print("Downloading model archive...")
    response = requests.get(url, stream=True)
    total_size = int(response.headers.get('content-length', 0))
    block_size = 8192
    downloaded = 0
    
    print(f"Total size: {total_size / (1024*1024):.1f} MB")
    with open(tar_path, "wb") as f:
        for chunk in response.iter_content(chunk_size=block_size):
            if chunk:
                f.write(chunk)
                downloaded += len(chunk)
                if total_size > 0:
                    percent = int((downloaded / total_size) * 100)
                    if percent % 10 == 0:
                        print(f" {percent}%", end="", flush=True)
    print("\nDownload complete")
    
    # Extract the tar.bz2 file
    print(f"Extracting {tar_path} to {destination}")
    try:
        with tarfile.open(tar_path, "r:bz2") as tar:
            tar.extractall(path=destination)
        os.remove(tar_path)
        print("Extraction complete")
    except Exception as e:
        print(f"Error during extraction: {str(e)}")
        raise
    
    print("Contents of destination directory:")
    for root, dirs, files in os.walk(destination):
        print(f"\nDirectory: {root}")
        if dirs:
            print("  Subdirectories:", dirs)
        if files:
            print("  Files:", files)

def dl_espeak_data():
    # Download the file
    tar_path='espeak-ng-data.tar.bz2'
    print("Downloading model archive...")
    response = requests.get('https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2', stream=True)
    total_size = int(response.headers.get('content-length', 0))
    block_size = 8192
    downloaded = 0
    
    print(f"Total size: {total_size / (1024*1024):.1f} MB")
    with open(tar_path, "wb") as f:
        for chunk in response.iter_content(chunk_size=block_size):
            if chunk:
                f.write(chunk)
                downloaded += len(chunk)
                if total_size > 0:
                    percent = int((downloaded / total_size) * 100)
                    if percent % 10 == 0:
                        print(f" {percent}%", end="", flush=True)
    print("\nDownload complete")
    
    # Extract the tar.bz2 file
    destination=os.path.dirname(os.path.abspath(__file__))
    print(f"Extracting {tar_path} to {destination}")
    try:
        with tarfile.open(tar_path, "r:bz2") as tar:
            tar.extractall(path=destination)
        os.remove(tar_path)
        print("Extraction complete")
    except Exception as e:
        print(f"Error during extraction: {str(e)}")
        raise
    
    print("Contents of destination directory:")
    for root, dirs, files in os.walk(destination):
        print(f"\nDirectory: {root}")
        if dirs:
            print("  Subdirectories:", dirs)
        if files:
            print("  Files:", files)

dl_espeak_data()

def find_model_files(model_dir):
    """Find model files in the given directory and its subdirectories."""
    model_files = {}
    
    # Check if this is an MMS model
    is_mms = True
    
    for root, _, files in os.walk(model_dir):
        for file in files:
            file_path = os.path.join(root, file)
            
            # Model file
            if file.endswith('.onnx'):
                model_files['model'] = file_path
            
            # Tokens file
            elif file == 'tokens.txt':
                model_files['tokens'] = file_path
            
            # Lexicon file (only for non-MMS models)
            elif file == 'lexicon.txt' and not is_mms:
                model_files['lexicon'] = file_path
    
    # Create empty lexicon file if needed (only for non-MMS models)
    if not is_mms and 'model' in model_files and 'lexicon' not in model_files:
        model_dir = os.path.dirname(model_files['model'])
        lexicon_path = os.path.join(model_dir, 'lexicon.txt')
        with open(lexicon_path, 'w', encoding='utf-8') as f:
            pass  # Create empty file
        model_files['lexicon'] = lexicon_path
    
    return model_files if 'model' in model_files else {}

def generate_audio(text, model_info):
    """Generate audio from text using the specified model."""
    try:
        model_dir = os.path.join("./models", model_info)
        
        print(f"\nLooking for model in: {model_dir}")
        
        # Download model if it doesn't exist
        if not os.path.exists(model_dir):
            print(f"Model directory doesn't exist, downloading {model_info}...")
            os.makedirs(model_dir, exist_ok=True)
            for i in models_n:
                if model_info == i[1]:
                    model_url=i[0]
            download_and_extract_model(model_url, model_dir)
        
        print(f"Contents of {model_dir}:")
        for item in os.listdir(model_dir):
            item_path = os.path.join(model_dir, item)
            if os.path.isdir(item_path):
                print(f"  Directory: {item}")
                print(f"    Contents: {os.listdir(item_path)}")
            else:
                print(f"  File: {item}")
        
        # Find and validate model files
        model_files = find_model_files(model_dir)
        if not model_files or 'model' not in model_files:
            raise ValueError(f"Could not find required model files in {model_dir}")
        
        print("\nFound model files:")
        print(f"Model: {model_files['model']}")
        print(f"Tokens: {model_files.get('tokens', 'Not found')}")
        print(f"Lexicon: {model_files.get('lexicon', 'Not required for MMS')}\n")
        
        # Check if this is an MMS model
        is_mms = 'mms' in os.path.basename(model_dir).lower()
        
        # Create configuration based on model type
        if is_mms:
            if 'tokens' not in model_files or not os.path.exists(model_files['tokens']):
                raise ValueError("tokens.txt is required for MMS models")
                
            # MMS models use tokens.txt and no lexicon
            vits_config = sherpa_onnx.OfflineTtsVitsModelConfig(
                model_files['model'],  # model
                '',                    # lexicon
                model_files['tokens'], # tokens
                '',                    # data_dir
                '',                    # dict_dir
                0.667,                 # noise_scale
                0.8,                   # noise_scale_w
                1.0                    # length_scale
            )
        else:
            # Non-MMS models use lexicon.txt
            if 'tokens' not in model_files or not os.path.exists(model_files['tokens']):
                raise ValueError("tokens.txt is required for VITS models")
                
            # Set data dir if it exists
            espeak_data = os.path.join(os.path.dirname(model_files['model']), 'espeak-ng-data')
            data_dir = espeak_data if os.path.exists(espeak_data) else 'espeak-ng-data'
            
            # Get lexicon path if it exists
            lexicon = model_files.get('lexicon', '') if os.path.exists(model_files.get('lexicon', '')) else ''
            
            # Create VITS model config
            vits_config = sherpa_onnx.OfflineTtsVitsModelConfig(
                model_files['model'],  # model
                lexicon,               # lexicon
                model_files['tokens'], # tokens
                data_dir,             # data_dir
                '',                   # dict_dir
                0.667,                # noise_scale
                0.8,                  # noise_scale_w
                1.0                   # length_scale
            )
        
        # Create the model config with VITS
        model_config = sherpa_onnx.OfflineTtsModelConfig()
        model_config.vits = vits_config
        
        # Create TTS configuration
        config = sherpa_onnx.OfflineTtsConfig(
            model=model_config,
            max_num_sentences=2
        )
        
        # Initialize TTS engine
        tts = sherpa_onnx.OfflineTts(config)

        # Generate audio
        audio_data = tts.generate(text)

        # Ensure we have valid audio data
        if audio_data is None or len(audio_data.samples) == 0:
            raise ValueError("Failed to generate audio - no data generated")
            
        # Convert samples list to numpy array and normalize
        audio_array = np.array(audio_data.samples, dtype=np.float32)
        if np.any(audio_array):  # Check if array is not all zeros
            audio_array = audio_array / np.abs(audio_array).max()
        else:
            raise ValueError("Generated audio is empty")
            
        # Return in Gradio's expected format (numpy array, sample rate)
        return (audio_array, audio_data.sample_rate)
            
    except Exception as e:
        error_msg = str(e)
        # Check for OOV or token conversion errors
        if "out of vocabulary" in error_msg.lower() or "token" in error_msg.lower():
            error_msg = f"Text contains unsupported characters: {error_msg}"
        print(f"Error generating audio: {error_msg}")
        print(f"Error in TTS generation: {error_msg}")
        raise

def tts_interface(selected_model, text, status_output):
    try:
        if not text.strip():
            return None, "Please enter some text"
            

        model_id = selected_model
        # Store original text for status message
        original_text = text
        

        try:
            # Update status with language info
            voice_name = model_id
            status = f"Generating speech using {voice_name} ..."
            
            # Generate audio
            audio_data, sample_rate = generate_audio(text, model_id)
            
            # Include translation info in final status if text was actually translated
            final_status = f"Generated speech using {voice_name}"
            final_status += f"\nText: '{text}'"
            
            return (sample_rate, audio_data), final_status
        except ValueError as e:
            # Handle known errors with user-friendly messages
            error_msg = str(e)
            if "cannot process some words" in error_msg.lower():
                return None, error_msg
            return None, f"Error: {error_msg}"
            
    except Exception as e:
        print(f"Error in TTS generation: {str(e)}")
        error_msg = str(e)
        return None, f"Error: {error_msg}"

# Gradio Interface
with gr.Blocks() as app:
    gr.Markdown("# Sherpa-ONNX متن به گفتار")
    with gr.Row():
        with gr.Column():
            model_dropdown = gr.Radio(
                choices=dropdown_choices,
                label="مدل",
                value=dropdown_choices[0] if dropdown_choices else None
            )
            text_input = gr.Textbox(
                label="متن",
                placeholder="متن را وارد کنید ...",
                lines=3
            )
            
            with gr.Row():
                generate_btn = gr.Button("بگو")
                stop_btn = gr.Button("توقف")
            
        with gr.Column():
            audio_output = gr.Audio(
                label="گفتار",
                type="numpy"
            )
            status_text = gr.Textbox(
                label="وضعیت",
                interactive=False
            )
    

    # Set up event handlers
    gen_event = generate_btn.click(
        fn=tts_interface,
        inputs=[model_dropdown, text_input, status_text],
        outputs=[audio_output, status_text]
    )
    
    stop_btn.click(
        fn=None,
        cancels=gen_event,
        queue=False
    )

app.launch()