warpq

import warpq import os # NOTE: Replace 'ref_audio.wav' and 'deg_audio.wav' with actual paths to your audio files. # Ensure these files exist in the current directory or provide full paths. # For example, you might create dummy files for testing: # import soundfile as sf # import numpy as np # sf.write('ref_audio.wav', np.random.rand(16000), 16000) # sf.write('deg_audio.wav', np.random.rand(16000), 16000) ref_audio_path = os.path.join(os.getcwd(), 'ref_audio.wav') # Example path deg_audio_path = os.path.join(os.getcwd(), 'deg_audio.wav') # Example path # Create dummy audio files for demonstration if they don't exist if not os.path.exists(ref_audio_path) or not os.path.exists(deg_audio_path): import soundfile as sf import numpy as np sample_rate = 16000 duration = 1 # second data = np.random.rand(int(sample_rate * duration)).astype(np.float32) * 0.5 sf.write(ref_audio_path, data, sample_rate) sf.write(deg_audio_path, data * 0.9, sample_rate) print(f"Created dummy audio files: {ref_audio_path}, {deg_audio_path}") try: # Initialize WARP-Q model. model_path=None uses a default pretrained model. model = warpq.WARPQ(model_path=None) # Predict the WARP-Q score score = model.predict(ref_audio_path, deg_audio_path) print(f"WARP-Q score for {os.path.basename(deg_audio_path)} (vs {os.path.basename(ref_audio_path)}): {score:.4f}") except Exception as e: print(f"An error occurred: {e}") print("Please ensure your audio files exist and are valid (e.g., .wav format, same sample rate).") print("Also verify PyTorch and torchaudio are correctly installed.") finally: # Clean up dummy files if os.path.exists(ref_audio_path): os.remove(ref_audio_path) if os.path.exists(deg_audio_path): os.remove(deg_audio_path)