noisereduce

import noisereduce as nr import numpy as np # --- 1. Generate dummy noisy audio --- rate = 44100 # sampling rate duration = 5 # seconds t = np.linspace(0, duration, int(rate * duration), endpoint=False) # Clean signal (e.g., a sine wave) clean_audio = 0.5 * np.sin(2 * np.pi * 440 * t) # A4 note # Add some random noise noise = 0.2 * np.random.randn(len(t)) noisy_audio = clean_audio + noise # --- 2. Reduce noise --- # For stationary noise (default and generally faster) reduced_noise_stationary = nr.reduce_noise( y=noisy_audio, sr=rate, stationary=True ) # For non-stationary noise (e.g., speech with varying background noise) # This is often more effective but can be slower. reduced_noise_non_stationary = nr.reduce_noise( y=noisy_audio, sr=rate, stationary=False ) print(f"Original audio shape: {noisy_audio.shape}") print(f"Reduced audio (stationary) shape: {reduced_noise_stationary.shape}") print(f"Reduced audio (non-stationary) shape: {reduced_noise_non_stationary.shape}") # --- Optional: Using the PyTorch backend (requires `pip install noisereduce[torch]`) --- # try: # import torch # model = nr.nn.NoiseReduce(sr=rate, nonstationary=False) # audio_tensor = torch.from_numpy(noisy_audio).float().unsqueeze(0) # Add batch dim # reduced_audio_tensor = model(audio_tensor) # reduced_audio_pytorch = reduced_audio_tensor.squeeze(0).numpy() # print(f"Reduced audio (PyTorch) shape: {reduced_audio_pytorch.shape}") # except ImportError: # print("PyTorch not installed, skipping PyTorch example.")