Create myanmar_tts.py

myanmar_tts.py

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+"""
+This is a simplified wrapper for myanmar-tts to handle import issues.
+It's intended to make the HuggingFace Space deployment easier.
+"""
+
+import os
+import sys
+import importlib.util
+
+# Add the repository directory to Python path if needed
+REPO_DIR = "myanmar-tts"
+if os.path.exists(REPO_DIR) and REPO_DIR not in sys.path:
+    sys.path.append(os.path.abspath(REPO_DIR))
+
+# Try to import directly, or from the repository
+try:
+    # First attempt: direct imports
+    from text import text_to_sequence
+    from utils.hparams import create_hparams
+    from train import load_model
+    from synthesis import generate_speech
+except ImportError:
+    try:
+        # Second attempt: repository imports
+        from myanmar_tts.text import text_to_sequence
+        from myanmar_tts.utils.hparams import create_hparams
+        from myanmar_tts.train import load_model
+        from myanmar_tts.synthesis import generate_speech
+    except ImportError:
+        # If still failing, try to load modules dynamically
+        def load_module(module_name, file_path):
+            if not os.path.exists(file_path):
+                raise ImportError(f"Module file not found: {file_path}")
+            
+            spec = importlib.util.spec_from_file_location(module_name, file_path)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+            return module
+        
+        # Try to load critical modules
+        try:
+            text_module = load_module("text", os.path.join(REPO_DIR, "text", "__init__.py"))
+            text_to_sequence = text_module.text_to_sequence
+            
+            hparams_module = load_module("hparams", os.path.join(REPO_DIR, "utils", "hparams.py"))
+            create_hparams = hparams_module.create_hparams
+            
+            train_module = load_module("train", os.path.join(REPO_DIR, "train.py"))
+            load_model = train_module.load_model
+            
+            synthesis_module = load_module("synthesis", os.path.join(REPO_DIR, "synthesis.py"))
+            generate_speech = synthesis_module.generate_speech
+        except Exception as e:
+            print(f"Failed to import myanmar-tts modules: {str(e)}")
+            raise
+
+# Define a simple synthesis function
+def synthesize(text, model_dir="trained_model"):
+    """
+    Synthesize speech from the given text using the Myanmar TTS model.
+    
+    Args:
+        text (str): The Burmese text to synthesize
+        model_dir (str): Directory containing the model files
+        
+    Returns:
+        tuple: (waveform, sample_rate)
+    """
+    import torch
+    import numpy as np
+    
+    checkpoint_path = os.path.join(model_dir, "checkpoint_latest.pth.tar")
+    config_path = os.path.join(model_dir, "hparams.yml")
+    
+    if not os.path.exists(checkpoint_path) or not os.path.exists(config_path):
+        raise FileNotFoundError(f"Model files not found in {model_dir}")
+    
+    # Load the model
+    hparams = create_hparams(config_path)
+    model = load_model(hparams)
+    model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu'))['state_dict'])
+    model.eval()
+    
+    # Process text
+    sequence = np.array(text_to_sequence(text, ['burmese_cleaners']))[None, :]
+    sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cpu().long()
+    
+    # Generate mel spectrograms
+    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
+    
+    # Generate waveform
+    with torch.no_grad():
+        waveform = generate_speech(mel_outputs_postnet, hparams)
+    
+    return waveform, hparams.sampling_rate