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app_utils.py

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+import os
+import json
+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',"🌠 راد",'https://huggingface.co/facebook/mms-tts-fas'],
+    ['coqui-vits-female1-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/persian-tts-female1-vits-coqui',"🌺 نگار",'https://huggingface.co/Kamtera/persian-tts-female1-vits'],
+    ['coqui-vits-male1-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/persian-tts-male1-vits-coqui',"🌟 آرش",'https://huggingface.co/Kamtera/persian-tts-male1-vits'],
+    ['coqui-vits-male-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/male-male-coqui-vits',"🦁 کیان",'https://huggingface.co/Kamtera/persian-tts-male-vits'],
+    ['coqui-vits-female-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/female-female-coqui-vits',"🌷 مهتاب",'https://huggingface.co/Kamtera/persian-tts-female-vits'],
+    ['coqui-vits-female-GPTInformal-karim23657','https://huggingface.co/karim23657/persian-tts-vits/tree/main/female-GPTInformal-coqui-vits',"🌼 شیوا",'https://huggingface.co/karim23657/persian-tts-female-GPTInformal-Persian-vits'],
+    ['coqui-vits-male-SmartGitiCorp','https://huggingface.co/karim23657/persian-tts-vits/tree/main/male-SmartGitiCorp-coqui-vits',"🚀 بهمن",'https://huggingface.co/SmartGitiCorp/persian_tts_vits'],
+    ['vits-piper-fa-ganji','https://huggingface.co/karim23657/persian-tts-vits/tree/main/vits-piper-fa-ganji',"🚀 برنا",'https://huggingface.co/SadeghK/persian-text-to-speech'],
+    ['vits-piper-fa-ganji-adabi','https://huggingface.co/karim23657/persian-tts-vits/tree/main/vits-piper-fa-ganji-adabi',"🚀 برنا-1",'https://huggingface.co/SadeghK/persian-text-to-speech'],
+    ['vits-piper-fa-gyro-medium','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-fa_IR-gyro-medium.tar.bz2',"💧 نیما",'https://huggingface.co/gyroing/Persian-Piper-Model-gyro'],
+    ['piper-fa-amir-medium','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-piper-fa_IR-amir-medium.tar.bz2',"⚡️ آریا",'https://huggingface.co/SadeghK/persian-text-to-speech'],
+    ['vits-mimic3-fa-haaniye_low','https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/vits-mimic3-fa-haaniye_low.tar.bz2',"🌹 ریما",'https://github.com/MycroftAI/mimic3'],
+    ['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',"🌠 پیام",'https://huggingface.co/mah92/persian-english-piper-tts-model'],
+]
+
+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:
+                if model_info == i[2]:
+                    model_url=i[1]
+            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}"
+

vits-piper-fa-ganji.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71e35c08741b63b570a40c08d411c49c6fea754e263e86ce8343fb9f19119a03
+size 63516173