import os import sys sys.path.insert(1, os.path.join(sys.path[0], '../utils')) import numpy as np import argparse import librosa import matplotlib.pyplot as plt import torch from utilities import create_folder, get_filename from models import * from pytorch_utils import move_data_to_device import config def audio_tagging(args): """Inference audio tagging result of an audio clip. """ # Arugments & parameters sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type checkpoint_path = args.checkpoint_path audio_path = args.audio_path device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu') classes_num = config.classes_num labels = config.labels # Model Model = eval(model_type) model = Model(sample_rate=sample_rate, window_size=window_size, hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, classes_num=classes_num) checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model']) # Parallel if 'cuda' in str(device): model.to(device) print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) else: print('Using CPU.') # Load audio (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = move_data_to_device(waveform, device) # Forward with torch.no_grad(): model.eval() batch_output_dict = model(waveform, None) clipwise_output = batch_output_dict['clipwise_output'].data.cpu().numpy()[0] """(classes_num,)""" sorted_indexes = np.argsort(clipwise_output)[::-1] # Print audio tagging top probabilities for k in range(10): print('{}: {:.3f}'.format(np.array(labels)[sorted_indexes[k]], clipwise_output[sorted_indexes[k]])) # Print embedding if 'embedding' in batch_output_dict.keys(): embedding = batch_output_dict['embedding'].data.cpu().numpy()[0] print('embedding: {}'.format(embedding.shape)) return clipwise_output, labels def sound_event_detection(args): """Inference sound event detection result of an audio clip. """ # Arugments & parameters sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type checkpoint_path = args.checkpoint_path audio_path = args.audio_path device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu') classes_num = config.classes_num labels = config.labels frames_per_second = sample_rate // hop_size # Paths fig_path = os.path.join('results', '{}.png'.format(get_filename(audio_path))) create_folder(os.path.dirname(fig_path)) # Model Model = eval(model_type) model = Model(sample_rate=sample_rate, window_size=window_size, hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, classes_num=classes_num) checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model']) # Parallel print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) if 'cuda' in str(device): model.to(device) # Load audio (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = move_data_to_device(waveform, device) # Forward with torch.no_grad(): model.eval() batch_output_dict = model(waveform, None) framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0] """(time_steps, classes_num)""" print('Sound event detection result (time_steps x classes_num): {}'.format( framewise_output.shape)) sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1] top_k = 10 # Show top results top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]] """(time_steps, top_k)""" # Plot result stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=window_size, hop_length=hop_size, window='hann', center=True) frames_num = stft.shape[-1] fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4)) axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet') axs[0].set_ylabel('Frequency bins') axs[0].set_title('Log spectrogram') axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1) axs[1].xaxis.set_ticks(np.arange(0, frames_num, frames_per_second)) axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / frames_per_second)) axs[1].yaxis.set_ticks(np.arange(0, top_k)) axs[1].yaxis.set_ticklabels(np.array(labels)[sorted_indexes[0 : top_k]]) axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3) axs[1].set_xlabel('Seconds') axs[1].xaxis.set_ticks_position('bottom') plt.tight_layout() plt.savefig(fig_path) print('Save sound event detection visualization to {}'.format(fig_path)) return framewise_output, labels if __name__ == '__main__': parser = argparse.ArgumentParser(description='Example of parser. ') subparsers = parser.add_subparsers(dest='mode') parser_at = subparsers.add_parser('audio_tagging') parser_at.add_argument('--sample_rate', type=int, default=32000) parser_at.add_argument('--window_size', type=int, default=1024) parser_at.add_argument('--hop_size', type=int, default=320) parser_at.add_argument('--mel_bins', type=int, default=64) parser_at.add_argument('--fmin', type=int, default=50) parser_at.add_argument('--fmax', type=int, default=14000) parser_at.add_argument('--model_type', type=str, required=True) parser_at.add_argument('--checkpoint_path', type=str, required=True) parser_at.add_argument('--audio_path', type=str, required=True) parser_at.add_argument('--cuda', action='store_true', default=False) parser_sed = subparsers.add_parser('sound_event_detection') parser_sed.add_argument('--sample_rate', type=int, default=32000) parser_sed.add_argument('--window_size', type=int, default=1024) parser_sed.add_argument('--hop_size', type=int, default=320) parser_sed.add_argument('--mel_bins', type=int, default=64) parser_sed.add_argument('--fmin', type=int, default=50) parser_sed.add_argument('--fmax', type=int, default=14000) parser_sed.add_argument('--model_type', type=str, required=True) parser_sed.add_argument('--checkpoint_path', type=str, required=True) parser_sed.add_argument('--audio_path', type=str, required=True) parser_sed.add_argument('--cuda', action='store_true', default=False) args = parser.parse_args() if args.mode == 'audio_tagging': audio_tagging(args) elif args.mode == 'sound_event_detection': sound_event_detection(args) else: raise Exception('Error argument!')