Add files using upload-large-folder tool

config/eval_for_dpo_config.yaml

@@ -0,0 +1,41 @@
+defaults:
+  - eval_for_dpo_base_config
+  - override hydra/job_logging: custom-simplest
+  - _self_
+
+hydra:
+  run:
+    dir: ./output/${exp_id}
+  output_subdir: eval-${now:%Y-%m-%d_%H-%M-%S}-hydra
+
+exp_id: ${model}
+dataset: vggsound_dpo #vggsound # vggsound_dpo
+duration_s: 8.0 # todo can be changed?
+
+num_samples_per_video: 5 # todo Feb 25
+random_sample: False # todo Mar 12: True is for sampling from training data, False is for sampling from validation data
+sample_size: 10000 #20000 # todo Mar 12
+
+# todo May 10
+small_44k_pretrained_ckpt_path: ./output/vgg_only_small_44k_lumina_v2a_two_stream_May17_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k/vgg_only_small_44k_lumina_v2a_two_stream_May17_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May12_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k/vgg_only_small_44k_lumina_v2a_two_stream_May12_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta20000_full_reward_ib_desync/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta20000_full_reward_ib_desync_ema_final.pth
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta2000_iter1_desync/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta2000_iter1_desync_ema_final.pth 
+
+# for inference, this is the per-GPU batch size
+batch_size: 16
+output_name: gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_inference_ema
+
+# for iter2 dpo: gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_inference_ema
+
+#new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema
+
+#new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_desync_to_ib_iter1_for_dpo_inference_ema
+
+#new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_clap_iter1_for_dpo_inference_ema
+
+#new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema 
+#new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema 
+#new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema # todo Jan 14

dpo_training/create_dpo_file.py

@@ -0,0 +1,77 @@
+import os
+import json
+import argparse
+import multiprocessing
+from tqdm import tqdm
+from reward_models.av_align import calculate_av_align_samples
+import pandas as pd
+
+
+ROOT = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos'
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos'
+
+def process_av_align(data):
+    num_samples = len(data)
+    audio_files_list = []
+    for i in range(num_samples):
+        video_id = data[i]['video_id']
+        audio_path = os.path.join(ROOT, video_id, data[i]['audio_path'])
+        audio_files_list.append(audio_path)
+
+    video_file_path = os.path.join(ROOT, data[0]['video_id'], data[0]['video_path'])
+    caption = data[0]['caption']
+
+    scores = calculate_av_align_samples(video_file_path, audio_files_list)
+    outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+    }
+    for i in range(num_samples):
+        outputs[f'{i+1}'] = scores[i]
+
+    dpo_outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+        'chosen': scores.index(max(scores)) + 1,
+        'reject': scores.index(min(scores)) + 1
+    }
+
+    return outputs, dpo_outputs
+
+
+
+if __name__ == "__main__":
+
+    json_path = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/generated_videos.json'
+    
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos.json'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos.json'
+    
+    output_dir = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema' 
+    
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema'
+
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+
+    #cores = multiprocessing.cpu_count()
+    pool = multiprocessing.Pool(processes=50)
+
+    results = list(tqdm(pool.imap(process_av_align, data), total=len(data)))
+
+    saved_output_full = []
+    saved_output_dpo = []
+    for output, dpo_output in results:
+        saved_output_full.append(output)
+        saved_output_dpo.append(dpo_output)
+
+    output_full_df = pd.DataFrame(saved_output_full)
+    output_full_df.to_csv(os.path.join(output_dir, 'av_align_score.tsv'), sep='\t', index=False)
+
+    output_dpo_df = pd.DataFrame(saved_output_dpo)
+    output_dpo_df.to_csv(os.path.join(output_dir, 'dpo_av_align.tsv'), sep='\t', index=False)
+
+    print("Finished !!!")

dpo_training/create_dpo_file_new.py

@@ -0,0 +1,90 @@
+import os
+import json
+import argparse
+import multiprocessing
+from tqdm import tqdm
+from reward_models.av_align import calculate_av_align_samples
+import pandas as pd
+
+
+ROOT = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos'
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos'
+
+def process_av_align(data):
+    num_samples = len(data)
+    audio_files_list = []
+    for i in range(num_samples):
+        video_id = data[i]['video_id']
+        audio_path = os.path.join(ROOT, video_id, data[i]['audio_path'])
+        audio_files_list.append(audio_path)
+
+    video_file_path = os.path.join(ROOT, data[0]['video_id'], data[0]['video_path'])
+    caption = data[0]['caption']
+
+    scores = calculate_av_align_samples(video_file_path, audio_files_list)
+    outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+    }
+    for i in range(num_samples):
+        outputs[f'{i+1}'] = scores[i]
+
+    dpo_outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+        'chosen': scores.index(max(scores)) + 1,
+        'reject': scores.index(min(scores)) + 1
+    }
+
+    return outputs, dpo_outputs
+
+
+
+if __name__ == "__main__":
+
+    json_path = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos.json'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos.json'
+    
+    output_dir = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema'
+
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+        
+    # Manager for inter-process communication
+    manager = multiprocessing.Manager()
+    return_dict = manager.dict()
+    
+    
+    processes = []
+    
+    for each_data in data:
+        p = multiprocessing.Process(
+            target=process_av_align,
+            args=(each_data))
+        
+        processes.append(p)
+        p.start()
+        
+    
+    for p in processes:
+        p.join()
+
+    cores = multiprocessing.cpu_count()
+    pool = multiprocessing.Pool(processes=cores)
+
+    results = list(tqdm(pool.imap(process_av_align, data), total=len(data)))
+
+    saved_output_full = []
+    saved_output_dpo = []
+    for output, dpo_output in results:
+        saved_output_full.append(output)
+        saved_output_dpo.append(dpo_output)
+
+    output_full_df = pd.DataFrame(saved_output_full)
+    output_full_df.to_csv(os.path.join(output_dir, 'av_align_score.tsv'), sep='\t', index=False)
+
+    output_dpo_df = pd.DataFrame(saved_output_dpo)
+    output_dpo_df.to_csv(os.path.join(output_dir, 'dpo_av_align.tsv'), sep='\t', index=False)
+
+    print("Finished !!!")

dpo_training/create_dpo_file_single.py

@@ -0,0 +1,93 @@
+import os
+import json
+import argparse
+import multiprocessing
+from tqdm import tqdm
+from reward_models.av_align import calculate_av_align_samples
+import pandas as pd
+
+
+ROOT = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos'
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos'
+
+def process_av_align(data):
+    num_samples = len(data)
+    audio_files_list = []
+    for i in range(num_samples):
+        video_id = data[i]['video_id']
+        audio_path = os.path.join(ROOT, video_id, data[i]['audio_path'])
+        audio_files_list.append(audio_path)
+
+    video_file_path = os.path.join(ROOT, data[0]['video_id'], data[0]['video_path'])
+    caption = data[0]['caption']
+
+    scores = calculate_av_align_samples(video_file_path, audio_files_list)
+    outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+    }
+    for i in range(num_samples):
+        outputs[f'{i+1}'] = scores[i]
+
+    dpo_outputs = {
+        'id': data[0]['video_id'],
+        'label': caption,
+        'chosen': scores.index(max(scores)) + 1,
+        'reject': scores.index(min(scores)) + 1
+    }
+
+    return outputs, dpo_outputs
+
+
+
+if __name__ == "__main__":
+
+    json_path = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos.json'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos.json'
+    
+    output_dir = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema'
+    #'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema'
+
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+    
+    saved_output_full = []
+    saved_output_dpo = []
+    
+    for each_data in tqdm(data):
+        num_samples = len(each_data)
+        audio_files_list = []
+        for i in range(num_samples):
+            video_id = each_data[i]['video_id']
+            audio_path = os.path.join(ROOT, video_id, each_data[i]['audio_path'])
+            audio_files_list.append(audio_path)
+
+        video_file_path = os.path.join(ROOT, each_data[0]['video_id'], each_data[0]['video_path'])
+        caption = each_data[0]['caption']
+
+        scores = calculate_av_align_samples(video_file_path, audio_files_list)
+        outputs = {
+            'id': each_data[0]['video_id'],
+            'label': caption,
+        }
+        for i in range(num_samples):
+            outputs[f'{i+1}'] = scores[i]
+
+        dpo_outputs = {
+            'id': each_data[0]['video_id'],
+            'label': caption,
+            'chosen': scores.index(max(scores)) + 1,
+            'reject': scores.index(min(scores)) + 1
+        }
+
+        saved_output_full.append(outputs)
+        saved_output_dpo.append(dpo_outputs)
+    
+
+    output_full_df = pd.DataFrame(saved_output_full)
+    output_full_df.to_csv(os.path.join(output_dir, 'av_align_score.tsv'), sep='\t', index=False)
+
+    output_dpo_df = pd.DataFrame(saved_output_dpo)
+    output_dpo_df.to_csv(os.path.join(output_dir, 'dpo_av_align.tsv'), sep='\t', index=False)
+
+    print("Finished !!!")

dpo_training/extract_video_training_latents.py

@@ -0,0 +1,286 @@
+import logging
+import os
+from argparse import ArgumentParser
+from datetime import timedelta
+from pathlib import Path
+
+import pandas as pd
+import tensordict as td
+import torch
+import torch.distributed as distributed
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+
+from mmaudio.data.data_setup import error_avoidance_collate
+from mmaudio.data.extraction.vgg_sound import VGGSound, VGGSound_DPO
+from mmaudio.model.utils.features_utils import FeaturesUtils
+from mmaudio.utils.dist_utils import local_rank, world_size
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+# for the 16kHz model
+# SAMPLING_RATE = 16000
+# DURATION_SEC = 8.0 # todo #4.0
+# NUM_SAMPLES = 128000 #64000 #128000 # todo
+# vae_path = './ext_weights/v1-16.pth' # VAE for visual input
+# bigvgan_path = './ext_weights/best_netG.pt' # vocoder
+# mode = '16k' # {16k, 44.1k}
+
+# for the 44.1kHz model
+SAMPLING_RATE = 44100
+DURATION_SEC = 8.0
+NUM_SAMPLES = 353280  
+vae_path = './ext_weights/v1-44.pth'
+bigvgan_path = None
+mode = '44k'
+
+synchformer_ckpt = './ext_weights/synchformer_state_dict.pth'
+
+# per-GPU
+BATCH_SIZE = 8  # 16
+NUM_WORKERS = 8  # 16
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+# ./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema/dpo_clap.tsv
+
+# kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_imagebind.tsv
+#
+
+data_cfg = {
+    'train': {
+       'root': './output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_inference_ema/generated_videos',
+        
+        
+        #'./output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_inference_ema/generated_videos',
+        
+        
+        #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema/generated_videos',
+        
+        #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_desync_to_ib_iter1_for_dpo_inference_ema/generated_videos', 
+        
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/generated_videos',
+        
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos'        
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos',
+
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema/generated_videos',
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/generated_videos',
+# ./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos',
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos', #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos', #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos', # todo
+        
+       'subset_name': './output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_inference_ema/dpo_full_reward_ib_desync.tsv',
+        
+        #'./output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_inference_ema/dpo_full_reward_ib_desync.tsv',
+        
+        #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema/dpo_full_reward_ib_desync.tsv',
+        
+        #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_desync_to_ib_iter1_for_dpo_inference_ema/dpo_imagebind.tsv',
+        
+        #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/dpo_full_reward_ib_2av_at_2desync.tsv',
+        
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_full_reward_ib_av_at_2desync.tsv',
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_full_reward_ib_2desync.tsv'
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_full_reward_ib_desync.tsv'
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_desync.tsv',
+        
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_imagebind.tsv',
+
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema/dpo_clap.tsv',
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/dpo_clap.tsv',
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_full_reward_2avalign_1cavp_2clap_thre03.tsv',
+# './output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_full_reward.tsv'
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/dpo_clap_new.tsv',
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_clap.tsv', 
+#'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/dpo_cavp.tsv', #'./output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/dpo_cavp.tsv', #todo dpo.tsv # todo change path
+       'normalize_audio': True,
+    },
+    # 'test': {
+    #    'root': '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/datasets/vggsound/videos',
+    #    'subset_name': './sets/vgg-test-filtered-caption.tsv', #'./filter_dataset/filtered_vggsound/test_filter_av_align.tsv', #'./sets/vgg-test-filtered.tsv',
+    #    'normalize_audio': False,
+    # },
+    # 'val': {
+    #     'root': '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/datasets/vggsound/videos',
+    #     'subset_name': './sets/vgg-val-filtered-caption.tsv',
+    #     # './filter_dataset/filtered_vggsound/val_filter_av_align.tsv', # './sets/vgg-val-filtered.tsv',
+    #     'normalize_audio': False,
+    # },
+}
+
+
+def distributed_setup():
+    distributed.init_process_group(backend="nccl", timeout=timedelta(hours=1))
+    log.info(f'Initialized: local_rank={local_rank}, world_size={world_size}')
+    return local_rank, world_size
+
+
+def setup_dataset(split: str):
+    dataset = VGGSound_DPO(
+        data_cfg[split]['root'],
+        tsv_path=data_cfg[split]['subset_name'],
+        sample_rate=SAMPLING_RATE,
+        duration_sec=DURATION_SEC,
+        audio_samples=NUM_SAMPLES,
+        normalize_audio=data_cfg[split]['normalize_audio'],
+    )
+    sampler = DistributedSampler(dataset, rank=local_rank, shuffle=False)
+    loader = DataLoader(dataset,
+                        batch_size=BATCH_SIZE,
+                        num_workers=NUM_WORKERS,
+                        sampler=sampler,
+                        drop_last=False,
+                        collate_fn=error_avoidance_collate)
+
+    return dataset, loader
+
+
+@torch.inference_mode()
+def extract():
+    # initial setup
+    distributed_setup()
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1 # 2000 samples
+# './dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_cavp/video-latents' 
+# './dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/iteration1_cavp/video-latents'
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_clap/video-latents
+#'./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_full_reward/video-latents'
+# './dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_full_reward_2avalign_1cavp_2clap_thre03/video-latents'
+# './dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/iteration1_clap/video-latents'
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema/iteration1_clap/video-latents
+
+# May 
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_imagebind/video-latents
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_desync/video-latents
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_full_reward_ib_desync/video-latents
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_full_reward_ib_2desync/video-latents
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/iteration1_dpo_full_reward_ib_2av_at_2desync/video-latents
+
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/iteration1_dpo_full_reward_ib_av_at_2desync/video-latents
+
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_desync_to_ib_iter1_for_dpo_inference_ema/iteration1_desync_to_ib/video-latents
+
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema/iteration2_ib_desync/video-latents
+
+# ./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_May17_depth16_caption_2000samples_full_reward_ib_desync_steps5k_iter2_for_dpo_inference_ema/iteration2_ib_desync/video-latents
+
+
+    parser = ArgumentParser()
+    parser.add_argument('--latent_dir',
+                        type=Path,
+                        default='./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_May17_depth16_caption_2000samples_full_reward_ib_desync_steps5k_iter3_for_dpo_inference_ema/iteration3_ib_desync/video-latents') # todo needs to change 
+    parser.add_argument('--output_dir', type=Path, default='./dpo_training/dpo_vgg_val/vggsound_dpo-new_model_lumina_v2a_two_stream_May17_depth16_caption_2000samples_full_reward_ib_desync_steps5k_iter3_for_dpo_inference_ema/iteration3_ib_desync/memmap') # todo needs to change
+    args = parser.parse_args()
+
+    latent_dir = args.latent_dir
+    output_dir = args.output_dir
+
+    # cuda setup
+    torch.cuda.set_device(local_rank)
+    feature_extractor = FeaturesUtils(tod_vae_ckpt=vae_path,
+                                      enable_conditions=True,
+                                      bigvgan_vocoder_ckpt=bigvgan_path,
+                                      synchformer_ckpt=synchformer_ckpt,
+                                      mode=mode).eval().cuda()
+
+    for split in data_cfg.keys():
+        print(f'Extracting latents for the {split} split')
+        this_latent_dir = latent_dir / split
+        this_latent_dir.mkdir(parents=True, exist_ok=True)
+
+        # setup datasets
+        dataset, loader = setup_dataset(split)
+        log.info(f'Number of samples: {len(dataset)}')
+        log.info(f'Number of batches: {len(loader)}')
+
+        for curr_iter, data in enumerate(tqdm(loader)):
+            output = {
+                'id': data['id'],
+                'caption': data['caption'],
+            }
+
+            audio_chosen = data['audio_chosen'].cuda()
+            dist_chosen = feature_extractor.encode_audio(audio_chosen)
+            output['mean_chosen'] = dist_chosen.mean.detach().cpu().transpose(1, 2)
+            output['std_chosen'] = dist_chosen.std.detach().cpu().transpose(1, 2)
+
+            audio_reject = data['audio_reject'].cuda()
+            dist_reject = feature_extractor.encode_audio(audio_reject)
+            output['mean_reject'] = dist_reject.mean.detach().cpu().transpose(1, 2)
+            output['std_reject'] = dist_reject.std.detach().cpu().transpose(1, 2)
+
+
+            clip_video = data['clip_video'].cuda()
+            clip_features = feature_extractor.encode_video_with_clip(clip_video)
+            output['clip_features'] = clip_features.detach().cpu()
+
+            sync_video = data['sync_video'].cuda()
+            sync_features = feature_extractor.encode_video_with_sync(sync_video)
+            output['sync_features'] = sync_features.detach().cpu()
+
+            caption = data['caption']
+            text_features = feature_extractor.encode_text(caption)
+            output['text_features'] = text_features.detach().cpu()
+
+            torch.save(output, this_latent_dir / f'r{local_rank}_{curr_iter}.pth')
+
+        distributed.barrier()
+
+        # combine the results
+        if local_rank == 0:
+            print('Extraction done. Combining the results.')
+
+            used_id = set()
+            list_of_ids_and_labels = []
+            output_data = {
+                'mean_chosen': [],
+                'std_chosen': [],
+                'mean_reject': [],
+                'std_reject': [],
+                'clip_features': [],
+                'sync_features': [],
+                'text_features': [],
+            }
+
+            for t in tqdm(sorted(os.listdir(this_latent_dir))):
+
+                if t.split('.')[-1] != 'pth':
+                    continue
+
+                data = torch.load(this_latent_dir / t, weights_only=True)
+                bs = len(data['id'])
+
+                for bi in range(bs):
+                    this_id = data['id'][bi]
+                    this_caption = data['caption'][bi]
+                    if this_id in used_id:
+                        print('Duplicate id:', this_id)
+                        continue
+
+                    list_of_ids_and_labels.append({'id': this_id, 'label': this_caption})
+                    used_id.add(this_id)
+                    output_data['mean_chosen'].append(data['mean_chosen'][bi])
+                    output_data['std_chosen'].append(data['std_chosen'][bi])
+                    output_data['mean_reject'].append(data['mean_reject'][bi])
+                    output_data['std_reject'].append(data['std_reject'][bi])
+                    output_data['clip_features'].append(data['clip_features'][bi])
+                    output_data['sync_features'].append(data['sync_features'][bi])
+                    output_data['text_features'].append(data['text_features'][bi])
+
+            output_dir.mkdir(parents=True, exist_ok=True)
+            output_df = pd.DataFrame(list_of_ids_and_labels)
+            output_df.to_csv(output_dir / f'vgg-{split}.tsv', sep='\t', index=False)
+
+            print(f'Output: {len(output_df)}')
+
+            output_data = {k: torch.stack(v) for k, v in output_data.items()}
+            td.TensorDict(output_data).memmap_(output_dir / f'vgg-{split}')
+
+            print(f'Finished combining {split} results')
+
+
+if __name__ == '__main__':
+    extract()
+    distributed.destroy_process_group()

dpo_training/generate_dpo_data.py

@@ -0,0 +1,135 @@
+import logging
+import os
+from pathlib import Path
+
+import hydra
+import torch
+import torch.distributed as distributed
+import torchaudio
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig
+from tqdm import tqdm
+
+from mmaudio.data.data_setup import setup_eval_dataset
+from mmaudio.eval_utils import ModelConfig, all_model_cfg, generate, make_video, make_video_new, load_video, load_full_video_frames # todo Feb 24
+from mmaudio.model.flow_matching import FlowMatching
+from mmaudio.model.networks_new import MMAudio, get_my_mmaudio
+from mmaudio.model.utils.features_utils import FeaturesUtils
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+local_rank = int(os.environ['LOCAL_RANK'])
+world_size = int(os.environ['WORLD_SIZE'])
+log = logging.getLogger()
+
+
+@torch.inference_mode()
+@hydra.main(version_base='1.3.2', config_path='config', config_name='eval_for_dpo_config.yaml')
+def main(cfg: DictConfig):
+    device = 'cuda'
+    torch.cuda.set_device(local_rank)
+
+    if cfg.model not in all_model_cfg:
+        raise ValueError(f'Unknown model variant: {cfg.model}')
+    model: ModelConfig = all_model_cfg[cfg.model]
+    #model.download_if_needed()
+    seq_cfg = model.seq_cfg
+
+    run_dir = Path(HydraConfig.get().run.dir)
+    if cfg.output_name is None:
+        output_dir = run_dir / cfg.dataset
+    else:
+        output_dir = run_dir / f'{cfg.dataset}-{cfg.output_name}'
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # todo
+    output_dir_audio = output_dir / 'audios'
+    output_dir_video = output_dir / 'videos'
+    output_dir_audio.mkdir(parents=True, exist_ok=True)
+    output_dir_video.mkdir(parents=True, exist_ok=True)
+    
+    # load a pretrained model
+    seq_cfg.duration = cfg.duration_s
+    net: MMAudio = get_my_mmaudio(cfg.model).to(device).eval()
+    net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True))
+    log.info(f'Loaded weights from {model.model_path}')
+    net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
+    log.info(f'Latent seq len: {seq_cfg.latent_seq_len}')
+    log.info(f'Clip seq len: {seq_cfg.clip_seq_len}')
+    log.info(f'Sync seq len: {seq_cfg.sync_seq_len}')
+
+    # misc setup
+    rng = torch.Generator(device=device)
+    rng.manual_seed(cfg.seed)
+    fm = FlowMatching(cfg.sampling.min_sigma,
+                      inference_mode=cfg.sampling.method,
+                      num_steps=cfg.sampling.num_steps)
+
+    feature_utils = FeaturesUtils(tod_vae_ckpt=model.vae_path,
+                                  synchformer_ckpt=model.synchformer_ckpt,
+                                  enable_conditions=True,
+                                  mode=model.mode,
+                                  bigvgan_vocoder_ckpt=model.bigvgan_16k_path,
+                                  need_vae_encoder=False)
+    feature_utils = feature_utils.to(device).eval()
+
+    if cfg.compile:
+        net.preprocess_conditions = torch.compile(net.preprocess_conditions)
+        net.predict_flow = torch.compile(net.predict_flow)
+        feature_utils.compile()
+
+    dataset, loader = setup_eval_dataset(cfg.dataset, cfg)
+
+    with torch.amp.autocast(enabled=cfg.amp, dtype=torch.bfloat16, device_type=device):
+        for batch in tqdm(loader):
+            audios = generate(batch.get('clip_video', None),
+                              batch.get('sync_video', None),
+                              batch.get('caption', None),
+                              feature_utils=feature_utils,
+                              net=net,
+                              fm=fm,
+                              rng=rng,
+                              cfg_strength=cfg.cfg_strength,
+                              clip_batch_size_multiplier=64,
+                              sync_batch_size_multiplier=64)
+            audios = audios.float().cpu()
+            names = batch['name']    
+            '''
+             for audio, name in zip(audios, names):
+                 torchaudio.save(output_dir / f'{name}.flac', audio, seq_cfg.sampling_rate) 
+            '''
+            
+            '''
+            video_infos = batch.get('video_info', None)
+            assert video_infos is not None
+            for audio, name, video_info in zip(audios, names, video_infos):
+                torchaudio.save(output_dir_audio / f'{name}.flac', audio, seq_cfg.sampling_rate)
+                video_save_path = output_dir_video / f'{name}.mp4'
+                make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)       
+            '''
+
+            video_paths = batch['video_path']
+
+            for audio, name, video_path in zip(audios, names, video_paths):
+                torchaudio.save(output_dir_audio / f'{name}.flac', audio, seq_cfg.sampling_rate)
+                video_info = load_full_video_frames(video_path, cfg.duration_s)
+                video_save_path = output_dir_video / f'{name}.mp4'
+                make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate) 
+
+
+def distributed_setup():
+    distributed.init_process_group(backend="nccl")
+    local_rank = distributed.get_rank()
+    world_size = distributed.get_world_size()
+    log.info(f'Initialized: local_rank={local_rank}, world_size={world_size}')
+    return local_rank, world_size
+
+
+if __name__ == '__main__':
+    distributed_setup()
+
+    main()
+
+    # clean-up
+    distributed.destroy_process_group()

dpo_training/generated_videos_file.py

@@ -0,0 +1,81 @@
+"""
+1. input: csv or json file: video_id --> sample_id --> audio.flac + video.mp4
+2. return: csv or json file: video_id -- caption -- chosen: sample_id/video.mp4 -- reject: sample_id/video.mp4
+3. return (optional): video_id -- caption -- sample_id_1 (score), sample_id_2, ......
+"""
+import os
+import pandas as pd
+import json
+from tqdm import tqdm
+
+def read_csv_file(csv_path):
+    captions = {}
+    df = pd.read_csv(csv_path, sep='\t', dtype={'id': str}).to_dict('records')
+    for row in df:
+        video_name = row['id']
+        captions[video_name] = row['label']
+    return captions
+
+
+data_original_csv_path = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/sets/vgg-val-filtered-caption.tsv'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/sets/vgg-train-filtered-caption.tsv' # for training subset for dpo training 
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/sets/vgg-val-filtered-caption.tsv' # for validation set for dpo training
+
+
+captions = read_csv_file(data_original_csv_path)
+
+
+root = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_desync_to_ib_iter1_for_dpo_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema/generated_videos' 
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema/generated_videos'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema/generated_videos'
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema/generated_videos' # for video dir path
+
+
+output_dir = '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter2_steps5k_inference_ema'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-gen_dpo_data_May17_lumina_v2a_two_stream_depth16_caption_beta20000_full_reward_ib_desync_iter1_steps5k_inference_ema'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_May10_depth16_caption_2000samples_full_reward_ib_desync_iter2_for_dpo_inference_ema'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_2000samples_av_align_iter1_for_dpo_inference_ema' 
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_inference_ema'
+
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_inference_ema'
+#'/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/zhoutao-240108120126/kwang/MMAudio/output/small_44k/vggsound_dpo-new_model_lumina_v2a_two_stream_Mar1_depth16_caption_inference_ema'
+
+video_ids_list = os.listdir(root)
+video_full_list = []
+for i, video_id in enumerate(tqdm(video_ids_list)):
+    video_folder_path = os.path.join(root, video_id)
+    #video_sample_list = sorted(os.listdir(video_folder_path))
+    video_sample_list = sorted(list(map(int, os.listdir(video_folder_path))))
+    video_sample_list = list(map(str, video_sample_list))
+    each_video_list = []
+    for j, sample_id in enumerate(video_sample_list):
+        video_path = f'{sample_id}/{video_id}.mp4'
+        audio_path = f'{sample_id}/{video_id}.flac'
+        each_video_list.append({
+            'video_id': video_id,
+            'sample_id': sample_id,
+            'video_path': video_path,
+            'audio_path': audio_path,
+            'caption': captions[video_id]
+        })
+    video_full_list.append(each_video_list)
+
+with open(f'{output_dir}/generated_videos.json', 'w') as f:
+    json.dump(video_full_list, f)
+
+print(f"All generated samples have been generated and saved to {output_dir}")

mmaudio/eval_utils.py

@@ -0,0 +1,470 @@
+import dataclasses
+import logging
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import torch
+from colorlog import ColoredFormatter
+from PIL import Image
+from torchvision.transforms import v2
+
+from mmaudio.data.av_utils import ImageInfo, VideoInfo, read_frames, reencode_with_audio, reencode_with_audio_new, read_full_frames
+from mmaudio.model.flow_matching import FlowMatching
+#from mmaudio.model.networks import MMAudio
+from mmaudio.model.networks_new import MMAudio
+from mmaudio.model.sequence_config import CONFIG_16K, CONFIG_44K, SequenceConfig
+from mmaudio.model.utils.features_utils import FeaturesUtils
+from mmaudio.utils.download_utils import download_model_if_needed
+
+log = logging.getLogger()
+
+
+@dataclasses.dataclass
+class ModelConfig:
+    model_name: str
+    model_path: Path
+    vae_path: Path
+    bigvgan_16k_path: Optional[Path]
+    mode: str
+    synchformer_ckpt: Path = Path('./ext_weights/synchformer_state_dict.pth')
+
+    @property
+    def seq_cfg(self) -> SequenceConfig:
+        if self.mode == '16k':
+            return CONFIG_16K
+        elif self.mode == '44k':
+            return CONFIG_44K
+
+    def download_if_needed(self):
+        download_model_if_needed(self.model_path)
+        download_model_if_needed(self.vae_path)
+        if self.bigvgan_16k_path is not None:
+            download_model_if_needed(self.bigvgan_16k_path)
+        download_model_if_needed(self.synchformer_ckpt)
+
+# todo need to change accordingly 
+small_16k = ModelConfig(model_name='small_16k',
+                        model_path=Path('./output/vgg_only_small_16k/vgg_only_small_16k_ema_final.pth'), 
+                        vae_path=Path('./ext_weights/v1-16.pth'),
+                        bigvgan_16k_path=Path('./ext_weights/best_netG.pt'),
+                        mode='16k')
+
+# ./output/vgg_only_small_44k_text_drop_jan18/vgg_only_small_44k_text_drop_jan18_ema_final.pth # todo done
+# ./output/vgg_only_small_44k/vgg_only_small_44k_ema_final.pth # todo done
+# ./output/vgg_only_44k_filtered/vgg_only_44k_filtered_ema_final.pth # todo done
+
+# ./output/vgg_only_small_44k_caption_jan26/vgg_only_small_44k_caption_jan26_ema_final.pth # done
+# ./output/vgg_only_small_44k_with_caption_text_drop/vgg_only_small_44k_with_caption_text_drop_ema_final.pth # done
+
+
+#  ./output/vgg_only_small_44k_new_model_feb1/vgg_only_small_44k_new_model_feb1_ema_final.pth # Done
+#  ./output/vgg_only_small_44k_new_model_feb3/vgg_only_small_44k_new_model_feb3_ema_final.pth # Done
+
+#   ./output/vgg_only_small_44k_new_model_two_streams_with_rope_Feb5/vgg_only_small_44k_new_model_two_streams_with_rope_Feb5_ema_final.pth                  # in progress
+
+# ./output/vgg_only_small_44k_new_model_two_steam_feb4/vgg_only_small_44k_new_model_two_steam_feb4_ema_final.pth # in progress
+
+# for two stream with caption
+# ./output/vgg_only_small_44k_new_model_two_stream_with_rope_Feb7/vgg_only_small_44k_new_model_two_stream_with_rope_Feb7_ema_final.pth
+
+
+# for two stream with caption, text drop
+# ./output/vgg_only_small_44k_caption_new_model_two_stream_with_rope_text_drop_Feb9/vgg_only_small_44k_caption_new_model_two_stream_with_rope_text_drop_Feb9_ema_final.pth
+
+
+# for lumina-v2a, v1: 
+# ./output/vgg_only_small_44k_new_model_lumina_v2a_Feb12/vgg_only_small_44k_new_model_lumina_v2a_Feb12_ema_final.pth
+
+#./output/vgg_only_small_44k_new_model_lumina_2a_with_RMSNorm_Feb12/vgg_only_small_44k_new_model_lumina_2a_with_RMSNorm_Feb12_ema_final.pth
+
+
+# Feb 14: 
+#./output/vgg_only_small_44k_new_model_lumina_v2a_two_stream_Feb13_with_RMSNorm/vgg_only_small_44k_new_model_lumina_v2a_two_stream_Feb13_with_RMSNorm_ema_final.pth
+
+#./output/vgg_only_small_44k_caption_new_model_lumina_v2a_two_stream_Feb13_with_RMSNorm/vgg_only_small_44k_caption_new_model_lumina_v2a_two_stream_Feb13_with_RMSNorm_ema_final.pth
+
+
+# todo Feb 24: used for generate dpo data by using pre-trained baseline model
+# ./output/vgg_only_small_44k_new_model_lumina_v2a_two_stream_Feb18_caption_depth16/vgg_only_small_44k_new_model_lumina_v2a_two_stream_Feb18_caption_depth16_ema_final.pth
+
+
+# Mar 6 for DPO 
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_new/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_new_ema_final.pth
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_latest/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_latest_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_latestlatest/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar6_latestlatest_ema_final.pth
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar7/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar7_ema_final.pth
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar7_new/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_dpo_iter1_Mar7_new_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta5000_Mar12/vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta5000_Mar12_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta2000_Mar12/vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta2000_Mar12_ema_final.pth
+
+# ./output/vgg_only_small_44k_vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta10000_Mar12/vgg_only_small_44k_vgg_only_small_44k_lumina_v2a_two_stream_depth_caption_dpo_iter1_cavp_beta10000_Mar12_ema_final.pth
+
+# todo May 12 & 16 
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_Mar12_beta2000/vgg_only_small_44k_lumina_v2a_two_stream_Mar12_depth16_caption_10000samples_Mar12_beta2000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta10000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta10000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta5000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta5000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta2000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta2000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta2000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta2000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta7000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_clap_beta7000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_10000samples_clap_beta10000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_10000samples_clap_beta10000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_10000samples_clap_beta10000/vgg_only_small_44k_lumina_v2a_two_stream_Mar21_depth16_caption_10000samples_clap_beta10000_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar26_depth16_caption_beta10000_av_align/vgg_only_small_44k_lumina_v2a_two_stream_Mar26_depth16_caption_beta10000_av_align_ema_final.pth
+
+
+# todo full reward 
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_Mar26_depth16_caption_beta10000_full_reward/vgg_only_small_44k_lumina_v2a_two_stream_Mar26_depth16_caption_beta10000_full_reward_ema_final.pth
+
+
+# todo April 12
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_April12_beta10000_av_align/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_April12_beta10000_av_align_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_April12_beta2000_av_align/vgg_only_small_44k_lumina_v2a_two_stream_depth16_caption_April12_beta2000_av_align_ema_final.pth
+
+# todo April 13
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_capton_beta10000_reward_2avalign_1cavp_2clap_thre03/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_capton_beta10000_reward_2avalign_1cavp_2clap_thre03_ema_final.pth
+
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_beta10000_clap_iter1/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_caption_10audio_per_video_beta10000_clap_iter1_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_caption_beta10000_av_align_to_clap_iter1/vgg_only_small_44k_lumina_v2a_two_stream_April12_depth16_caption_beta10000_av_align_to_clap_iter1_ema_final.pth
+
+# after May 
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_imagebind/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_imagebind_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_desync/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_desync_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta2000_iter1_desync/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta2000_iter1_desync_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_desync_3ksteps/vgg_only_small_44k_lumina_v2a_two_stream_May6_depth16_caption_beta10000_iter1_desync_3ksteps_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_iter1_desync_3ksteps/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_iter1_desync_3ksteps_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_full_reward_ib_desync/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_full_reward_ib_desync_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_full_reward_ib_2desync/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta10000_full_reward_ib_2desync_ema_final.pth
+
+#./output/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta20000_full_reward_ib_desync/vgg_only_small_44k_lumina_v2a_two_stream_May7_depth16_caption_beta20000_full_reward_ib_desync_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_imagebind_at/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_imagebind_at_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync_ema_final.pth
+
+# ./output/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync_10audio_per_video/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync_10audio_per_video_ema_final.pth
+
+# './output/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync_10audio_per_video/vgg_only_small_44k_lumina_v2a_two_stream_May10_depth16_caption_beta10000_iter1_full_reward_ib_av_at_2desync_10audio_per_video_ema_final.pth'
+
+small_44k = ModelConfig(model_name='small_44k',
+                        model_path=None,
+                        vae_path=Path('./ext_weights/v1-44.pth'),
+                        bigvgan_16k_path=None,
+                        mode='44k')
+
+
+medium_44k = ModelConfig(model_name='medium_44k',
+                         model_path=Path('/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/MMAudio/output/train_vgg_only_medium_44k/train_vgg_only_medium_44k_ema_final.pth'),
+                         vae_path=Path('./ext_weights/v1-44.pth'),
+                         bigvgan_16k_path=None,
+                         mode='44k')
+large_44k = ModelConfig(model_name='large_44k',
+                        model_path=Path('./output/vgg_only_large_44k/vgg_only_large_44k_ema_final.pth'),
+                        vae_path=Path('./ext_weights/v1-44.pth'),
+                        bigvgan_16k_path=None,
+                        mode='44k')
+large_44k_v2 = ModelConfig(model_name='large_44k_v2',
+                           model_path=Path('./weights/mmaudio_large_44k_v2.pth'),
+                           vae_path=Path('./ext_weights/v1-44.pth'),
+                           bigvgan_16k_path=None,
+                           mode='44k')
+all_model_cfg: dict[str, ModelConfig] = {
+    'small_16k': small_16k,
+    'small_44k': small_44k,
+    'medium_44k': medium_44k,
+    'large_44k': large_44k,
+    'large_44k_v2': large_44k_v2,
+}
+
+
+def generate(
+    clip_video: Optional[torch.Tensor],
+    sync_video: Optional[torch.Tensor],
+    text: Optional[list[str]],
+    *,
+    negative_text: Optional[list[str]] = None,
+    feature_utils: FeaturesUtils,
+    net: MMAudio,
+    fm: FlowMatching,
+    rng: torch.Generator,
+    cfg_strength: float,
+    clip_batch_size_multiplier: int = 40,
+    sync_batch_size_multiplier: int = 40,
+    image_input: bool = False,
+) -> torch.Tensor:
+    device = feature_utils.device
+    dtype = feature_utils.dtype
+
+    bs = len(text)
+    if clip_video is not None:
+        clip_video = clip_video.to(device, dtype, non_blocking=True)
+        clip_features = feature_utils.encode_video_with_clip(clip_video,
+                                                             batch_size=bs *
+                                                             clip_batch_size_multiplier)
+        if image_input:
+            clip_features = clip_features.expand(-1, net.clip_seq_len, -1)
+    else:
+        clip_features = net.get_empty_clip_sequence(bs)
+
+    if sync_video is not None and not image_input:
+        sync_video = sync_video.to(device, dtype, non_blocking=True)
+        sync_features = feature_utils.encode_video_with_sync(sync_video,
+                                                             batch_size=bs *
+                                                             sync_batch_size_multiplier)
+    else:
+        sync_features = net.get_empty_sync_sequence(bs)
+
+    if text is not None:
+        text_features = feature_utils.encode_text(text)
+    else:
+        text_features = net.get_empty_string_sequence(bs)
+
+    if negative_text is not None:
+        assert len(negative_text) == bs
+        negative_text_features = feature_utils.encode_text(negative_text)
+    else:
+        negative_text_features = net.get_empty_string_sequence(bs)
+
+    x0 = torch.randn(bs,
+                     net.latent_seq_len,
+                     net.latent_dim,
+                     device=device,
+                     dtype=dtype,
+                     generator=rng)
+    preprocessed_conditions = net.preprocess_conditions(clip_features, sync_features, text_features)
+    empty_conditions = net.get_empty_conditions(
+        bs, negative_text_features=negative_text_features if negative_text is not None else None)
+
+    cfg_ode_wrapper = lambda t, x: net.ode_wrapper(t, x, preprocessed_conditions, empty_conditions,
+                                                   cfg_strength)
+    x1 = fm.to_data(cfg_ode_wrapper, x0)
+    x1 = net.unnormalize(x1)
+    spec = feature_utils.decode(x1)
+    audio = feature_utils.vocode(spec)
+    return audio
+
+
+# todo Feb 25
+def generate_dpo(
+    clip_video: Optional[torch.Tensor],
+    sync_video: Optional[torch.Tensor],
+    text: Optional[list[str]],
+    *,
+    negative_text: Optional[list[str]] = None,
+    feature_utils: FeaturesUtils,
+    net: MMAudio,
+    fm: FlowMatching,
+    rng: torch.Generator,
+    cfg_strength: float,
+    clip_batch_size_multiplier: int = 40,
+    sync_batch_size_multiplier: int = 40,
+    image_input: bool = False,
+    num_samples_per_video: int = 1,
+) -> list[torch.Tensor]:
+    device = feature_utils.device
+    dtype = feature_utils.dtype
+
+    bs = len(text)
+    if clip_video is not None:
+        clip_video = clip_video.to(device, dtype, non_blocking=True)
+        clip_features = feature_utils.encode_video_with_clip(clip_video,
+                                                             batch_size=bs *
+                                                             clip_batch_size_multiplier)
+        if image_input:
+            clip_features = clip_features.expand(-1, net.clip_seq_len, -1)
+    else:
+        clip_features = net.get_empty_clip_sequence(bs)
+
+    if sync_video is not None and not image_input:
+        sync_video = sync_video.to(device, dtype, non_blocking=True)
+        sync_features = feature_utils.encode_video_with_sync(sync_video,
+                                                             batch_size=bs *
+                                                             sync_batch_size_multiplier)
+    else:
+        sync_features = net.get_empty_sync_sequence(bs)
+
+    if text is not None:
+        text_features = feature_utils.encode_text(text)
+    else:
+        text_features = net.get_empty_string_sequence(bs)
+
+    if negative_text is not None:
+        assert len(negative_text) == bs
+        negative_text_features = feature_utils.encode_text(negative_text)
+    else:
+        negative_text_features = net.get_empty_string_sequence(bs)
+
+    preprocessed_conditions = net.preprocess_conditions(clip_features, sync_features, text_features)
+    empty_conditions = net.get_empty_conditions(bs, negative_text_features=negative_text_features if negative_text is not None else None)
+
+    cfg_ode_wrapper = lambda t, x: net.ode_wrapper(t, x, preprocessed_conditions, empty_conditions, cfg_strength)
+
+    x0 = torch.randn(bs*num_samples_per_video,
+                     net.latent_seq_len,
+                     net.latent_dim,
+                     device=device,
+                     dtype=dtype,
+                     generator=rng)
+
+    audios = []
+    for i, each_x0 in enumerate(x0.chunk(num_samples_per_video, dim=0)):
+        x1 = fm.to_data(cfg_ode_wrapper, each_x0)  # todo Feb 25
+        x1 = net.unnormalize(x1)
+        spec = feature_utils.decode(x1)
+        audio = feature_utils.vocode(spec)
+        audios.append(audio)
+    return audios
+
+
+LOGFORMAT = "[%(log_color)s%(levelname)-8s%(reset)s]: %(log_color)s%(message)s%(reset)s"
+
+
+def setup_eval_logging(log_level: int = logging.INFO):
+    logging.root.setLevel(log_level)
+    formatter = ColoredFormatter(LOGFORMAT)
+    stream = logging.StreamHandler()
+    stream.setLevel(log_level)
+    stream.setFormatter(formatter)
+    log = logging.getLogger()
+    log.setLevel(log_level)
+    log.addHandler(stream)
+
+
+_CLIP_SIZE = 384
+_CLIP_FPS = 8.0
+
+_SYNC_SIZE = 224
+_SYNC_FPS = 25.0
+
+
+def load_video(video_path: Path, duration_sec: float, load_all_frames: bool = True) -> VideoInfo:
+
+    clip_transform = v2.Compose([
+        v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
+        v2.ToImage(),
+        v2.ToDtype(torch.float32, scale=True),
+    ])
+
+    sync_transform = v2.Compose([
+        v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
+        v2.CenterCrop(_SYNC_SIZE),
+        v2.ToImage(),
+        v2.ToDtype(torch.float32, scale=True),
+        v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    ])
+
+    output_frames, all_frames, orig_fps = read_frames(video_path,
+                                                      list_of_fps=[_CLIP_FPS, _SYNC_FPS],
+                                                      start_sec=0,
+                                                      end_sec=duration_sec,
+                                                      need_all_frames=load_all_frames)
+
+    clip_chunk, sync_chunk = output_frames
+    clip_chunk = torch.from_numpy(clip_chunk).permute(0, 3, 1, 2)
+    sync_chunk = torch.from_numpy(sync_chunk).permute(0, 3, 1, 2)
+
+    clip_frames = clip_transform(clip_chunk)
+    sync_frames = sync_transform(sync_chunk)
+
+    clip_length_sec = clip_frames.shape[0] / _CLIP_FPS
+    sync_length_sec = sync_frames.shape[0] / _SYNC_FPS
+
+    if clip_length_sec < duration_sec:
+        log.warning(f'Clip video is too short: {clip_length_sec:.2f} < {duration_sec:.2f}')
+        log.warning(f'Truncating to {clip_length_sec:.2f} sec')
+        duration_sec = clip_length_sec
+
+    if sync_length_sec < duration_sec:
+        log.warning(f'Sync video is too short: {sync_length_sec:.2f} < {duration_sec:.2f}')
+        log.warning(f'Truncating to {sync_length_sec:.2f} sec')
+        duration_sec = sync_length_sec
+
+    clip_frames = clip_frames[:int(_CLIP_FPS * duration_sec)]
+    sync_frames = sync_frames[:int(_SYNC_FPS * duration_sec)]
+
+    video_info = VideoInfo(
+        duration_sec=duration_sec,
+        fps=orig_fps,
+        clip_frames=clip_frames,
+        sync_frames=sync_frames,
+        all_frames=all_frames if load_all_frames else None,
+    )
+    return video_info
+
+# todo Feb 24 simplified version 
+def load_full_video_frames(video_path: Path, duration_sec: float, load_all_frames: bool = True) -> VideoInfo:
+
+    all_frames, orig_fps = read_full_frames(video_path,
+                                          start_sec=0,
+                                          end_sec=duration_sec,
+                                          need_all_frames=load_all_frames)
+
+
+    video_info = VideoInfo(
+        duration_sec=duration_sec,
+        fps=orig_fps,
+        clip_frames=None,
+        sync_frames=None,
+        all_frames=all_frames if load_all_frames else None,
+    )
+    return video_info
+
+
+def load_image(image_path: Path) -> VideoInfo:
+    clip_transform = v2.Compose([
+        v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
+        v2.ToImage(),
+        v2.ToDtype(torch.float32, scale=True),
+    ])
+
+    sync_transform = v2.Compose([
+        v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
+        v2.CenterCrop(_SYNC_SIZE),
+        v2.ToImage(),
+        v2.ToDtype(torch.float32, scale=True),
+        v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    ])
+
+    frame = np.array(Image.open(image_path))
+
+    clip_chunk = torch.from_numpy(frame).unsqueeze(0).permute(0, 3, 1, 2)
+    sync_chunk = torch.from_numpy(frame).unsqueeze(0).permute(0, 3, 1, 2)
+
+    clip_frames = clip_transform(clip_chunk)
+    sync_frames = sync_transform(sync_chunk)
+
+    video_info = ImageInfo(
+        clip_frames=clip_frames,
+        sync_frames=sync_frames,
+        original_frame=frame,
+    )
+    return video_info
+
+
+def make_video(video_info: VideoInfo, output_path: Path, audio: torch.Tensor, sampling_rate: int):
+    reencode_with_audio(video_info, output_path, audio, sampling_rate)
+
+# todo Feb 24
+def make_video_new(all_frames, fps, width, height, output_path, audio, sampling_rate):
+    reencode_with_audio_new(all_frames, fps, width, height, output_path, audio, sampling_rate)

mmaudio/runner-Copy1.py

@@ -0,0 +1,947 @@
+"""
+trainer.py - wrapper and utility functions for network training
+Compute loss, back-prop, update parameters, logging, etc.
+"""
+import os
+from pathlib import Path
+from typing import Optional, Union
+
+import torch
+import torch.distributed
+import torch.optim as optim
+#from av_bench.evaluate import evaluate # todo Jan 12
+#from av_bench.extract import extract
+from nitrous_ema import PostHocEMA
+from omegaconf import DictConfig
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from mmaudio.model.flow_matching import FlowMatching
+#from mmaudio.model.networks import get_my_mmaudio
+from mmaudio.model.networks_new import get_my_mmaudio
+from mmaudio.model.sequence_config import CONFIG_16K, CONFIG_44K
+from mmaudio.model.utils.features_utils import FeaturesUtils
+from mmaudio.model.utils.parameter_groups import get_parameter_groups
+from mmaudio.model.utils.sample_utils import log_normal_sample
+from mmaudio.utils.dist_utils import (info_if_rank_zero, local_rank, string_if_rank_zero)
+from mmaudio.utils.log_integrator import Integrator
+from mmaudio.utils.logger import TensorboardLogger
+from mmaudio.utils.time_estimator import PartialTimeEstimator, TimeEstimator
+from mmaudio.utils.video_joiner import VideoJoiner
+
+import torch.nn.functional as F
+import copy # todo Mar 2
+
+class Runner:
+
+    def __init__(self,
+                 cfg: DictConfig,
+                 log: TensorboardLogger,
+                 run_path: Union[str, Path],
+                 for_training: bool = True,
+                 latent_mean: Optional[torch.Tensor] = None,
+                 latent_std: Optional[torch.Tensor] = None,
+                 dpo_train: bool = False):
+        self.exp_id = cfg.exp_id
+        self.use_amp = cfg.amp
+        self.for_training = for_training
+        self.cfg = cfg
+        self.dpo_train = dpo_train # todo Mar 2
+
+        if cfg.model.endswith('16k'):
+            self.seq_cfg = CONFIG_16K
+            mode = '16k'
+        elif cfg.model.endswith('44k'):
+            self.seq_cfg = CONFIG_44K
+            mode = '44k'
+        else:
+            raise ValueError(f'Unknown model: {cfg.model}')
+
+        self.sample_rate = self.seq_cfg.sampling_rate
+        self.duration_sec = self.seq_cfg.duration
+
+        # setting up the model
+        empty_string_feat = torch.load('./ext_weights/empty_string.pth', weights_only=True)[0]
+        
+        
+        if not dpo_train:
+            self.network = DDP(get_my_mmaudio(cfg.model,
+                                              latent_mean=latent_mean,
+                                              latent_std=latent_std,
+                                              empty_string_feat=empty_string_feat,
+                                              dpo_train=dpo_train).cuda(),
+                               device_ids=[local_rank],
+                               broadcast_buffers=False,
+                               find_unused_parameters=True)
+        else:
+            self.network = DDP(get_my_mmaudio(cfg.model,
+                                              latent_mean_dpo=latent_mean,
+                                              latent_std_dpo=latent_std,
+                                              empty_string_feat=empty_string_feat,
+                                              dpo_train=dpo_train).cuda(),
+                               device_ids=[local_rank],
+                               broadcast_buffers=False,
+                               find_unused_parameters=True)
+
+        if dpo_train: # todo Mar 2
+            self.beta_dpo = 2000
+            map_location = 'cuda:%d' % local_rank
+            pretrained_ckpt = torch.load(cfg['pretrained_ckpt_for_dpo'], map_location={'cuda:0': map_location}, weights_only=True)
+            self.network.module.load_state_dict(pretrained_ckpt, strict=False)
+            info_if_rank_zero(log, 'Loading pretrained weights from the disk')
+
+            self.ref_network = copy.deepcopy(self.network)
+            self.ref_network.requires_grad_ = False
+            self.ref_network.eval()
+            for param in self.ref_network.parameters():
+                param.requires_grad = False            
+        
+        if cfg.compile:
+            # NOTE: though train_fn and val_fn are very similar
+            # (early on they are implemented as a single function)
+            # keeping them separate and compiling them separately are CRUCIAL for high performance
+            self.train_fn = torch.compile(self.train_fn)
+            self.val_fn = torch.compile(self.val_fn)
+        
+        if cfg.compile and dpo_train: # todo Mar 2
+            self.train_fn_dpo = torch.compile(self.train_fn_dpo)
+
+        self.fm = FlowMatching(cfg.sampling.min_sigma,
+                               inference_mode=cfg.sampling.method,
+                               num_steps=cfg.sampling.num_steps)
+
+        # ema profile
+        if for_training and cfg.ema.enable and local_rank == 0:
+            self.ema = PostHocEMA(self.network.module,
+                                  sigma_rels=cfg.ema.sigma_rels,
+                                  update_every=cfg.ema.update_every,
+                                  checkpoint_every_num_steps=cfg.ema.checkpoint_every,
+                                  checkpoint_folder=cfg.ema.checkpoint_folder,
+                                  step_size_correction=True).cuda()
+            self.ema_start = cfg.ema.start
+        else:
+            self.ema = None
+
+        self.rng = torch.Generator(device='cuda')
+        self.rng.manual_seed(cfg['seed'] + local_rank)
+
+        # setting up feature extractors and VAEs
+        if mode == '16k':
+            self.features = FeaturesUtils(
+                tod_vae_ckpt=cfg['vae_16k_ckpt'],
+                bigvgan_vocoder_ckpt=cfg['bigvgan_vocoder_ckpt'],
+                synchformer_ckpt=cfg['synchformer_ckpt'],
+                enable_conditions=True,
+                mode=mode,
+                need_vae_encoder=False,
+            )
+        elif mode == '44k':
+            self.features = FeaturesUtils(
+                tod_vae_ckpt=cfg['vae_44k_ckpt'],
+                synchformer_ckpt=cfg['synchformer_ckpt'],
+                enable_conditions=True,
+                mode=mode,
+                need_vae_encoder=False,
+            )
+        self.features = self.features.cuda().eval()
+
+        if cfg.compile:
+            self.features.compile()
+
+        # hyperparameters
+        self.log_normal_sampling_mean = cfg.sampling.mean
+        self.log_normal_sampling_scale = cfg.sampling.scale
+        self.null_condition_probability = cfg.null_condition_probability
+        self.cfg_strength = cfg.cfg_strength
+
+        # todo add extra hyperparameters 
+        self.text_condition_drop_probability = cfg.text_condition_drop_probability # todo Jan 16 [0.3, 0.5]
+        self.text_condition_drop_enable = cfg.text_condition_drop_enable # todo Jan 16 True or False
+        self.text_drop_step = cfg.text_drop_step # todo Jan 16
+        
+        # setting up logging
+        self.log = log
+        self.run_path = Path(run_path)
+        vgg_cfg = cfg.dpo_data.VGGSound
+        if for_training:
+            self.val_video_joiner = VideoJoiner(vgg_cfg.root, self.run_path / 'val-sampled-videos',
+                                                self.sample_rate, self.duration_sec)
+        else:
+            self.test_video_joiner = VideoJoiner(vgg_cfg.root,
+                                                 self.run_path / 'test-sampled-videos',
+                                                 self.sample_rate, self.duration_sec)
+        string_if_rank_zero(self.log, 'model_size',
+                            f'{sum([param.nelement() for param in self.network.parameters()])}')
+        string_if_rank_zero(
+            self.log, 'number_of_parameters_that_require_gradient: ',
+            str(
+                sum([
+                    param.nelement()
+                    for param in filter(lambda p: p.requires_grad, self.network.parameters())
+                ])))
+        info_if_rank_zero(self.log, 'torch version: ' + torch.__version__)
+        self.train_integrator = Integrator(self.log, distributed=True)
+        self.val_integrator = Integrator(self.log, distributed=True)
+
+        # setting up optimizer and loss
+        if for_training:
+            self.enter_train()
+            parameter_groups = get_parameter_groups(self.network, cfg, print_log=(local_rank == 0))
+            self.optimizer = optim.AdamW(parameter_groups,
+                                         lr=cfg['learning_rate'],
+                                         weight_decay=cfg['weight_decay'],
+                                         betas=[0.9, 0.95],
+                                         eps=1e-6 if self.use_amp else 1e-8,
+                                         fused=True)
+            if self.use_amp:
+                self.scaler = torch.amp.GradScaler(init_scale=2048)
+            self.clip_grad_norm = cfg['clip_grad_norm']
+
+            # linearly warmup learning rate
+            linear_warmup_steps = cfg['linear_warmup_steps']
+
+            def warmup(currrent_step: int):
+                return (currrent_step + 1) / (linear_warmup_steps + 1)
+
+            warmup_scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=warmup)
+
+            # setting up learning rate scheduler
+            if cfg['lr_schedule'] == 'constant':
+                next_scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda _: 1)
+            elif cfg['lr_schedule'] == 'poly':
+                total_num_iter = cfg['iterations']
+                next_scheduler = optim.lr_scheduler.LambdaLR(self.optimizer,
+                                                             lr_lambda=lambda x:
+                                                             (1 - (x / total_num_iter))**0.9)
+            elif cfg['lr_schedule'] == 'step':
+                next_scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer,
+                                                                cfg['lr_schedule_steps'],
+                                                                cfg['lr_schedule_gamma'])
+            else:
+                raise NotImplementedError
+
+            self.scheduler = optim.lr_scheduler.SequentialLR(self.optimizer,
+                                                             [warmup_scheduler, next_scheduler],
+                                                             [linear_warmup_steps])
+
+            # Logging info
+            self.log_text_interval = cfg['log_text_interval']
+            self.log_extra_interval = cfg['log_extra_interval']
+            self.save_weights_interval = cfg['save_weights_interval']
+            self.save_checkpoint_interval = cfg['save_checkpoint_interval']
+            self.save_copy_iterations = cfg['save_copy_iterations']
+            self.num_iterations = cfg['num_iterations']
+            if cfg['debug']:
+                self.log_text_interval = self.log_extra_interval = 1
+
+            # update() is called when we log metrics, within the logger
+            self.log.batch_timer = TimeEstimator(self.num_iterations, self.log_text_interval)
+            # update() is called every iteration, in this script
+            self.log.data_timer = PartialTimeEstimator(self.num_iterations, 1, ema_alpha=0.9)
+        else:
+            self.enter_val()
+
+    def train_fn(
+        self,
+        clip_f: torch.Tensor,
+        sync_f: torch.Tensor,
+        text_f: torch.Tensor,
+        a_mean: torch.Tensor,
+        a_std: torch.Tensor,
+        it: int = 0,  # todo Jan 16
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # sample
+        a_randn = torch.empty_like(a_mean).normal_(generator=self.rng)
+        x1 = a_mean + a_std * a_randn
+        bs = x1.shape[0]  # batch_size * seq_len * num_channels
+
+        # normalize the latents
+        x1 = self.network.module.normalize(x1)
+
+        t = log_normal_sample(x1,
+                              generator=self.rng,
+                              m=self.log_normal_sampling_mean,
+                              s=self.log_normal_sampling_scale)
+        x0, x1, xt, (clip_f, sync_f, text_f) = self.fm.get_x0_xt_c(x1,
+                                                                   t,
+                                                                   Cs=[clip_f, sync_f, text_f],
+                                                                   generator=self.rng)
+
+        # classifier-free training
+        samples = torch.rand(bs, device=x1.device, generator=self.rng)
+
+        # null mask is for when a video is provided but we decided to ignore it
+        null_video = (samples < self.null_condition_probability)
+        # complete mask is for when a video is not provided or we decided to ignore it
+        clip_f[null_video] = self.network.module.empty_clip_feat
+        sync_f[null_video] = self.network.module.empty_sync_feat
+
+        samples = torch.rand(bs, device=x1.device, generator=self.rng)
+        # todo Jan 16: add text drop prob schedule
+        if self.text_condition_drop_enable: # todo Jan 16
+            drop_index = int(it // self.text_drop_step) # todo Jan 16
+            if drop_index >= len(self.text_condition_drop_probability): # todo Jan 16
+                text_condition_drop_prob = self.text_condition_drop_probability[-1] # todo Jan 16
+
+            else:
+                text_condition_drop_prob = self.text_condition_drop_probability[drop_index] # todo Jan 16
+
+            if it % self.text_drop_step == 0:
+                info_if_rank_zero(self.log, f'Text Condition Drop Prob is: {text_condition_drop_prob}') # todo Jan 16
+
+            null_text = (samples < text_condition_drop_prob) # todo Jan 16
+        else:
+            null_text = (samples < self.null_condition_probability)  # todo Jan 16 [0.1, 0.2, 0.3, 0.4, 0.5]
+        
+        #null_text = (samples < self.null_condition_probability) todo Jan 16 comment it when to use dynamic text condition drop
+        text_f[null_text] = self.network.module.empty_string_feat
+
+        pred_v = self.network(xt, clip_f, sync_f, text_f, t)
+        loss = self.fm.loss(pred_v, x0, x1)
+        mean_loss = loss.mean()
+        return x1, loss, mean_loss, t
+    
+    def train_fn_dpo(
+            self,
+            clip_f: torch.Tensor,
+            sync_f: torch.Tensor,
+            text_f: torch.Tensor,
+            chosen_a_mean: torch.Tensor,
+            chosen_a_std: torch.Tensor,
+            reject_a_mean: torch.Tensor,
+            reject_a_std: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # sample
+
+        a_randn_chosen = torch.empty_like(chosen_a_mean).normal_(generator=self.rng)
+        x1_chosen = chosen_a_mean + chosen_a_std * a_randn_chosen
+
+        a_randn_reject = torch.empty_like(reject_a_mean).normal_(generator=self.rng)
+        x1_reject = reject_a_mean + reject_a_std * a_randn_reject
+
+        # normalize the latents
+        x1_chosen, x1_reject = self.network.module.normalize_dpo(x1_chosen, x1_reject)  # todo Mar 2 (needs to change)
+
+        x1 = torch.cat((x1_chosen, x1_reject), dim=0)
+        bs = x1.shape[0]  # batch_size * seq_len * num_channels * 2 # todo Mar 2 needs to change
+
+
+        t = log_normal_sample(x1.chunk(2)[0], # todo keep the t same for both chosen and reject
+                              generator=self.rng,
+                              m=self.log_normal_sampling_mean,
+                              s=self.log_normal_sampling_scale)
+
+        t = t.repeat(2) # todo
+
+        x0, x1, xt, (clip_f, sync_f, text_f) = self.fm.get_x0_xt_c_dpo(x1,
+                                                                   t,
+                                                                   Cs=[clip_f, sync_f, text_f],
+                                                                   generator=self.rng)
+
+        # classifier-free training
+        samples = torch.rand(bs//2, device=x1.device, generator=self.rng)
+
+        # null mask is for when a video is provided but we decided to ignore it
+        null_video = (samples < self.null_condition_probability)
+        # complete mask is for when a video is not provided or we decided to ignore it
+        clip_f[null_video] = self.network.module.empty_clip_feat
+        sync_f[null_video] = self.network.module.empty_sync_feat
+
+        samples = torch.rand(bs//2, device=x1.device, generator=self.rng)
+        
+         # todo Jan 16: add text drop prob schedule
+        if self.text_condition_drop_enable: # todo Jan 16
+            drop_index = int(it // self.text_drop_step) # todo Jan 16
+            if drop_index >= len(self.text_condition_drop_probability): # todo Jan 16
+                text_condition_drop_prob = self.text_condition_drop_probability[-1] # todo Jan 16
+
+            else:
+                text_condition_drop_prob = self.text_condition_drop_probability[drop_index] # todo Jan 16
+
+            if it % self.text_drop_step == 0:
+                info_if_rank_zero(self.log, f'Text Condition Drop Prob is: {text_condition_drop_prob}') # todo Jan 16
+
+            null_text = (samples < text_condition_drop_prob) # todo Jan 16
+        else:
+            null_text = (samples < self.null_condition_probability)  # todo Jan 16 [0.1, 0.2, 0.3, 0.4, 0.5]
+        
+        #null_text = (samples < self.null_condition_probability) todo Jan 16 comment it when to use dynamic text condition drop
+        text_f[null_text] = self.network.module.empty_string_feat
+
+        clip_f = clip_f.repeat(2, 1, 1)
+        sync_f = sync_f.repeat(2, 1, 1)
+        text_f = text_f.repeat(2, 1, 1)
+
+        pred_v = self.network(xt, clip_f, sync_f, text_f, t)
+
+        model_loss = self.fm.loss(pred_v, x0, x1)
+
+        model_losses_w, model_losses_l = model_loss.chunk(2)
+
+        model_diff = model_losses_w - model_losses_l
+        raw_model_loss = 0.5 * (model_losses_w.mean() + model_losses_l.mean())
+
+        with torch.no_grad():
+            ref_pred_v = self.ref_network(xt, clip_f, sync_f, text_f, t)
+            ref_loss = self.fm.loss(ref_pred_v, x0, x1)
+            ref_losses_w, ref_losses_l = ref_loss.chunk(2)
+            ref_diff = ref_losses_w - ref_losses_l
+            raw_ref_loss = ref_loss.mean()
+
+        scale_term = -0.5 * self.beta_dpo
+        inside_term = scale_term * (model_diff - ref_diff)
+        implicit_acc = (
+                               scale_term * (model_diff - ref_diff) > 0
+                       ).sum().float() / inside_term.size(0)
+
+        loss = -1 * F.logsigmoid(inside_term) + model_losses_w
+        mean_loss = -1 * F.logsigmoid(inside_term).mean() + model_losses_w.mean()
+
+        return x1, loss, mean_loss, t.chunk(2)[0], raw_model_loss, raw_ref_loss, implicit_acc
+
+
+    def val_fn(
+        self,
+        clip_f: torch.Tensor,
+        sync_f: torch.Tensor,
+        text_f: torch.Tensor,
+        x1: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        bs = x1.shape[0]  # batch_size * seq_len * num_channels
+        # normalize the latents
+        x1 = self.network.module.normalize(x1)
+        t = log_normal_sample(x1,
+                              generator=self.rng,
+                              m=self.log_normal_sampling_mean,
+                              s=self.log_normal_sampling_scale)
+        x0, x1, xt, (clip_f, sync_f, text_f) = self.fm.get_x0_xt_c(x1,
+                                                                   t,
+                                                                   Cs=[clip_f, sync_f, text_f],
+                                                                   generator=self.rng)
+
+        # classifier-free training
+        samples = torch.rand(bs, device=x1.device, generator=self.rng)
+        # null mask is for when a video is provided but we decided to ignore it
+        null_video = (samples < self.null_condition_probability)
+        # complete mask is for when a video is not provided or we decided to ignore it
+        clip_f[null_video] = self.network.module.empty_clip_feat
+        sync_f[null_video] = self.network.module.empty_sync_feat
+
+        samples = torch.rand(bs, device=x1.device, generator=self.rng)
+        null_text = (samples < self.null_condition_probability)
+        text_f[null_text] = self.network.module.empty_string_feat
+
+        pred_v = self.network(xt, clip_f, sync_f, text_f, t)
+
+        loss = self.fm.loss(pred_v, x0, x1)
+        mean_loss = loss.mean()
+        return loss, mean_loss, t
+
+    def train_pass(self, data, it: int = 0):
+
+        if not self.for_training:
+            raise ValueError('train_pass() should not be called when not training.')
+
+        self.enter_train()
+        with torch.amp.autocast('cuda', enabled=self.use_amp, dtype=torch.bfloat16):
+            clip_f = data['clip_features'].cuda(non_blocking=True)
+            sync_f = data['sync_features'].cuda(non_blocking=True)
+            text_f = data['text_features'].cuda(non_blocking=True)
+            video_exist = data['video_exist'].cuda(non_blocking=True)
+            text_exist = data['text_exist'].cuda(non_blocking=True)
+            a_mean = data['a_mean'].cuda(non_blocking=True)
+            a_std = data['a_std'].cuda(non_blocking=True)
+
+            # these masks are for non-existent data; masking for CFG training is in train_fn
+            clip_f[~video_exist] = self.network.module.empty_clip_feat
+            sync_f[~video_exist] = self.network.module.empty_sync_feat
+            text_f[~text_exist] = self.network.module.empty_string_feat
+
+            self.log.data_timer.end()
+            if it % self.log_extra_interval == 0:
+                unmasked_clip_f = clip_f.clone()
+                unmasked_sync_f = sync_f.clone()
+                unmasked_text_f = text_f.clone()
+            x1, loss, mean_loss, t = self.train_fn(clip_f, sync_f, text_f, a_mean, a_std, it) # todo Jan 16
+ 
+            self.train_integrator.add_dict({'loss': mean_loss})
+
+        if it % self.log_text_interval == 0 and it != 0:
+            self.train_integrator.add_scalar('lr', self.scheduler.get_last_lr()[0])
+            self.train_integrator.add_binned_tensor('binned_loss', loss, t)
+            self.train_integrator.finalize('train', it)
+            self.train_integrator.reset_except_hooks()
+
+        # Backward pass
+        self.optimizer.zero_grad(set_to_none=True)
+        if self.use_amp:
+            self.scaler.scale(mean_loss).backward()
+            self.scaler.unscale_(self.optimizer)
+            grad_norm = torch.nn.utils.clip_grad_norm_(self.network.parameters(),
+                                                       self.clip_grad_norm)
+            self.scaler.step(self.optimizer)
+            self.scaler.update()
+        else:
+            mean_loss.backward()
+            grad_norm = torch.nn.utils.clip_grad_norm_(self.network.parameters(),
+                                                       self.clip_grad_norm)
+            self.optimizer.step()
+
+        if self.ema is not None and it >= self.ema_start:
+            self.ema.update()
+        self.scheduler.step()
+        self.integrator.add_scalar('grad_norm', grad_norm)
+
+        self.enter_val()
+        with torch.amp.autocast('cuda', enabled=self.use_amp,
+                                dtype=torch.bfloat16), torch.inference_mode():
+            try:
+                if it % self.log_extra_interval == 0:
+                    # save GT audio
+                    # unnormalize the latents
+                    x1 = self.network.module.unnormalize(x1[0:1])
+                    mel = self.features.decode(x1)
+                    audio = self.features.vocode(mel).cpu()[0]  # 1 * num_samples
+                    self.log.log_spectrogram('train', f'spec-gt-r{local_rank}', mel.cpu()[0], it)
+                    self.log.log_audio('train',
+                                       f'audio-gt-r{local_rank}',
+                                       audio,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+                    # save audio from sampling
+                    x0 = torch.empty_like(x1[0:1]).normal_(generator=self.rng)
+                    clip_f = unmasked_clip_f[0:1]
+                    sync_f = unmasked_sync_f[0:1]
+                    text_f = unmasked_text_f[0:1]
+                    conditions = self.network.module.preprocess_conditions(clip_f, sync_f, text_f)
+                    empty_conditions = self.network.module.get_empty_conditions(x0.shape[0])
+                    cfg_ode_wrapper = lambda t, x: self.network.module.ode_wrapper(
+                        t, x, conditions, empty_conditions, self.cfg_strength)
+                    x1_hat = self.fm.to_data(cfg_ode_wrapper, x0)
+                    x1_hat = self.network.module.unnormalize(x1_hat)
+                    mel = self.features.decode(x1_hat)
+                    audio = self.features.vocode(mel).cpu()[0]
+                    self.log.log_spectrogram('train', f'spec-r{local_rank}', mel.cpu()[0], it)
+                    self.log.log_audio('train',
+                                       f'audio-r{local_rank}',
+                                       audio,
+                                       it,
+                                       sample_rate=self.sample_rate)
+            except Exception as e:
+                self.log.warning(f'Error in extra logging: {e}')
+                if self.cfg.debug:
+                    raise
+
+        # Save network weights and checkpoint if needed
+        save_copy = it in self.save_copy_iterations
+
+        if (it % self.save_weights_interval == 0 and it != 0) or save_copy:
+            self.save_weights(it)
+
+        if it % self.save_checkpoint_interval == 0 and it != 0:
+            self.save_checkpoint(it, save_copy=save_copy)
+
+        self.log.data_timer.start()
+
+
+    def train_dpo_pass(self, data, it: int = 0):
+
+        if not self.for_training:
+            raise ValueError('train_pass() should not be called when not training.')
+
+        self.enter_train()
+        with torch.amp.autocast('cuda', enabled=self.use_amp, dtype=torch.bfloat16):
+            clip_f = data['clip_features'].cuda(non_blocking=True)
+            sync_f = data['sync_features'].cuda(non_blocking=True)
+            text_f = data['text_features'].cuda(non_blocking=True)
+            video_exist = data['video_exist'].cuda(non_blocking=True)
+            text_exist = data['text_exist'].cuda(non_blocking=True)
+            chosen_a_mean = data['chosen_a_mean'].cuda(non_blocking=True)
+            chosen_a_std = data['chosen_a_std'].cuda(non_blocking=True)
+
+            reject_a_mean = data['reject_a_mean'].cuda(non_blocking=True)
+            reject_a_std = data['reject_a_std'].cuda(non_blocking=True)
+
+            # these masks are for non-existent data; masking for CFG training is in train_fn
+            clip_f[~video_exist] = self.network.module.empty_clip_feat
+            sync_f[~video_exist] = self.network.module.empty_sync_feat
+            text_f[~text_exist] = self.network.module.empty_string_feat
+
+            self.log.data_timer.end()
+            if it % self.log_extra_interval == 0:
+                unmasked_clip_f = clip_f.clone()
+                unmasked_sync_f = sync_f.clone()
+                unmasked_text_f = text_f.clone()
+            x1, loss, mean_loss, t, raw_model_loss, raw_ref_loss, implicit_acc = self.train_fn_dpo(clip_f, sync_f, text_f, chosen_a_mean, chosen_a_std, reject_a_mean, reject_a_std)
+
+            self.train_integrator.add_dict({'loss': mean_loss})
+            self.train_integrator.add_dict({'raw_model_loss': raw_model_loss})
+            self.train_integrator.add_dict({'raw_ref_loss': raw_ref_loss})
+            self.train_integrator.add_dict({'implicit_acc': implicit_acc})
+
+
+        if it % self.log_text_interval == 0 and it != 0:
+            self.train_integrator.add_scalar('lr', self.scheduler.get_last_lr()[0])
+            self.train_integrator.add_binned_tensor('binned_loss', loss, t)
+            self.train_integrator.finalize('train', it)
+            self.train_integrator.reset_except_hooks()
+
+        # Backward pass
+        self.optimizer.zero_grad(set_to_none=True)
+        if self.use_amp:
+            self.scaler.scale(mean_loss).backward()
+            self.scaler.unscale_(self.optimizer)
+            grad_norm = torch.nn.utils.clip_grad_norm_(self.network.parameters(),
+                                                       self.clip_grad_norm)
+            self.scaler.step(self.optimizer)
+            self.scaler.update()
+        else:
+            mean_loss.backward()
+            grad_norm = torch.nn.utils.clip_grad_norm_(self.network.parameters(),
+                                                       self.clip_grad_norm)
+            self.optimizer.step()
+
+        if self.ema is not None and it >= self.ema_start:
+            self.ema.update()
+        self.scheduler.step()
+        self.integrator.add_scalar('grad_norm', grad_norm)
+
+        self.enter_val()
+        with torch.amp.autocast('cuda', enabled=self.use_amp,
+                                dtype=torch.bfloat16), torch.inference_mode():
+            try: # todo needs to be changed Mar 2
+                if it % self.log_extra_interval == 0:
+                    # save GT audio
+                    # unnormalize the latents
+                    x1_chosen = x1.chunk(2)[0]
+                    x1_reject = x1.chunk(2)[1]
+                    # todo need to be changed accordingly
+                    x1_chosen, x1_rekject = self.network.module.unnormalize_dpo(x1_chosen[0:1], x1_reject[0:1])
+                    mel_chosen = self.features.decode(x1_chosen)
+                    audio_chosen = self.features.vocode(mel_chosen).cpu()[0]  # 1 * num_samples
+                    mel_reject = self.features.decode(x1_reject)
+                    audio_reject = self.features.vocode(mel_reject).cpu()[0]  # 1 * num_samples
+
+                    self.log.log_spectrogram('train-chosen', f'spec-gt-chosen-r{local_rank}', mel_chosen.cpu()[0], it)
+                    self.log.log_audio('train-chosen',
+                                       f'audio-gt-chosen-r{local_rank}',
+                                       audio_chosen,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+                    self.log.log_spectrogram('train-reject', f'spec-gt-reject-r{local_rank}', mel_reject.cpu()[0], it)
+                    self.log.log_audio('train-reject',
+                                       f'audio-gt-reject-r{local_rank}',
+                                       audio_reject,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+                    # save audio from sampling
+                    x0_chosen = torch.empty_like(x1_chosen[0:1]).normal_(generator=self.rng)
+                    x0_reject = torch.empty_like(x1_reject[0:1]).normal_(generator=self.rng)
+                    clip_f = unmasked_clip_f[0:1]
+                    sync_f = unmasked_sync_f[0:1]
+                    text_f = unmasked_text_f[0:1]
+                    conditions = self.network.module.preprocess_conditions(clip_f, sync_f, text_f)
+                    empty_conditions = self.network.module.get_empty_conditions(x0_chosen.shape[0])
+                    cfg_ode_wrapper = lambda t, x: self.network.module.ode_wrapper(
+                        t, x, conditions, empty_conditions, self.cfg_strength)
+                    x1_hat_chosen = self.fm.to_data(cfg_ode_wrapper, x0_chosen)
+                    x1_hat_reject = self.fm.to_data(cfg_ode_wrapper, x0_reject)
+
+                    x1_hat_chosen, x1_hat_reject = self.network.module.unnormalize_dpo(x1_hat_chosen, x1_hat_reject)
+                    mel_chosen = self.features.decode(x1_hat_chosen)
+                    audio_chosen = self.features.vocode(mel_chosen).cpu()[0]
+
+                    mel_reject = self.features.decode(x1_hat_reject)
+                    audio_reject = self.features.vocode(mel_reject).cpu()[0]
+
+                    self.log.log_spectrogram('train-chosen', f'spec-chosen-r{local_rank}', mel_chosen.cpu()[0], it)
+                    self.log.log_audio('train-chosen',
+                                       f'audio-chosen-r{local_rank}',
+                                       audio_chosen,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+                    self.log.log_spectrogram('train-reject', f'spec-reject-r{local_rank}', mel_reject.cpu()[0], it)
+                    self.log.log_audio('train-reject',
+                                       f'audio-reject-r{local_rank}',
+                                       audio_reject,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+
+                    '''
+                    x1 = self.network.module.unnormalize(x1[0:1])
+                    mel = self.features.decode(x1)
+                    audio = self.features.vocode(mel).cpu()[0]  # 1 * num_samples
+                    self.log.log_spectrogram('train', f'spec-gt-r{local_rank}', mel.cpu()[0], it)
+                    self.log.log_audio('train',
+                                       f'audio-gt-r{local_rank}',
+                                       audio,
+                                       it,
+                                       sample_rate=self.sample_rate)
+
+                    # save audio from sampling
+                    x0 = torch.empty_like(x1[0:1]).normal_(generator=self.rng)
+                    clip_f = unmasked_clip_f[0:1]
+                    sync_f = unmasked_sync_f[0:1]
+                    text_f = unmasked_text_f[0:1]
+                    conditions = self.network.module.preprocess_conditions(clip_f, sync_f, text_f)
+                    empty_conditions = self.network.module.get_empty_conditions(x0.shape[0])
+                    cfg_ode_wrapper = lambda t, x: self.network.module.ode_wrapper(
+                        t, x, conditions, empty_conditions, self.cfg_strength)
+                    x1_hat = self.fm.to_data(cfg_ode_wrapper, x0)
+                    x1_hat = self.network.module.unnormalize(x1_hat)
+                    mel = self.features.decode(x1_hat)
+                    audio = self.features.vocode(mel).cpu()[0]
+                    self.log.log_spectrogram('train', f'spec-r{local_rank}', mel.cpu()[0], it)
+                    self.log.log_audio('train',
+                                       f'audio-r{local_rank}',
+                                       audio,
+                                       it,
+                                       sample_rate=self.sample_rate)'''
+            except Exception as e:
+                self.log.warning(f'Error in extra logging: {e}')
+                if self.cfg.debug:
+                    raise
+
+        # Save network weights and checkpoint if needed
+        save_copy = it in self.save_copy_iterations
+
+        if (it % self.save_weights_interval == 0 and it != 0) or save_copy:
+            self.save_weights(it)
+
+        if it % self.save_checkpoint_interval == 0 and it != 0:
+            self.save_checkpoint(it, save_copy=save_copy)
+
+        self.log.data_timer.start()
+
+
+
+    @torch.inference_mode()
+    def validation_pass(self, data, it: int = 0):
+        self.enter_val()
+        with torch.amp.autocast('cuda', enabled=self.use_amp, dtype=torch.bfloat16):
+            clip_f = data['clip_features'].cuda(non_blocking=True)
+            sync_f = data['sync_features'].cuda(non_blocking=True)
+            text_f = data['text_features'].cuda(non_blocking=True)
+            video_exist = data['video_exist'].cuda(non_blocking=True)
+            text_exist = data['text_exist'].cuda(non_blocking=True)
+            a_mean = data['a_mean'].cuda(non_blocking=True)
+            a_std = data['a_std'].cuda(non_blocking=True)
+
+            clip_f[~video_exist] = self.network.module.empty_clip_feat
+            sync_f[~video_exist] = self.network.module.empty_sync_feat
+            text_f[~text_exist] = self.network.module.empty_string_feat
+            a_randn = torch.empty_like(a_mean).normal_(generator=self.rng)
+            x1 = a_mean + a_std * a_randn
+
+            self.log.data_timer.end()
+            loss, mean_loss, t = self.val_fn(clip_f.clone(), sync_f.clone(), text_f.clone(), x1)
+
+            self.val_integrator.add_binned_tensor('binned_loss', loss, t)
+            self.val_integrator.add_dict({'loss': mean_loss})
+
+        self.log.data_timer.start()
+
+    @torch.inference_mode()
+    def inference_pass(self,
+                       data,
+                       it: int,
+                       data_cfg: DictConfig,
+                       *,
+                       save_eval: bool = True) -> Path:
+        self.enter_val()
+        with torch.amp.autocast('cuda', enabled=self.use_amp, dtype=torch.bfloat16):
+            clip_f = data['clip_features'].cuda(non_blocking=True)
+            sync_f = data['sync_features'].cuda(non_blocking=True)
+            text_f = data['text_features'].cuda(non_blocking=True)
+            video_exist = data['video_exist'].cuda(non_blocking=True)
+            text_exist = data['text_exist'].cuda(non_blocking=True)
+            a_mean = data['a_mean'].cuda(non_blocking=True)  # for the shape only
+
+            clip_f[~video_exist] = self.network.module.empty_clip_feat
+            sync_f[~video_exist] = self.network.module.empty_sync_feat
+            text_f[~text_exist] = self.network.module.empty_string_feat
+
+            # sample
+            x0 = torch.empty_like(a_mean).normal_(generator=self.rng)
+            conditions = self.network.module.preprocess_conditions(clip_f, sync_f, text_f)
+            empty_conditions = self.network.module.get_empty_conditions(x0.shape[0])
+            cfg_ode_wrapper = lambda t, x: self.network.module.ode_wrapper(
+                t, x, conditions, empty_conditions, self.cfg_strength)
+            x1_hat = self.fm.to_data(cfg_ode_wrapper, x0)
+            x1_hat = self.network.module.unnormalize(x1_hat)
+            mel = self.features.decode(x1_hat)
+            audio = self.features.vocode(mel).cpu()
+            for i in range(audio.shape[0]):
+                video_id = data['id'][i]
+                if (not self.for_training) and i == 0:
+                    # save very few videos
+                    self.test_video_joiner.join(video_id, f'{video_id}', audio[i].transpose(0, 1))
+
+                if data_cfg.output_subdir is not None:
+                    # validation
+                    if save_eval:
+                        iter_naming = f'{it:09d}'
+                    else:
+                        iter_naming = 'val-cache'
+                    audio_dir = self.log.log_audio(iter_naming,
+                                                   f'{video_id}',
+                                                   audio[i],
+                                                   it=None,
+                                                   sample_rate=self.sample_rate,
+                                                   subdir=Path(data_cfg.output_subdir))
+                    if save_eval and i == 0:
+                        self.val_video_joiner.join(video_id, f'{iter_naming}-{video_id}',
+                                                   audio[i].transpose(0, 1))
+                else:
+                    # full test set, usually
+                    audio_dir = self.log.log_audio(f'{data_cfg.tag}-sampled',
+                                                   f'{video_id}',
+                                                   audio[i],
+                                                   it=None,
+                                                   sample_rate=self.sample_rate)
+
+        return Path(audio_dir)
+
+    @torch.inference_mode()
+    def eval(self, audio_dir: Path, it: int, data_cfg: DictConfig) -> dict[str, float]:
+        # rank 0 model only, outside of AMP
+        info_if_rank_zero(self.log, 'Eval: entering barrier')
+        torch.distributed.barrier()
+        info_if_rank_zero(self.log, 'Eval: barrier resolved')
+        if local_rank == 0:
+            extract(audio_path=audio_dir,
+                    output_path=audio_dir / 'cache',
+                    device='cuda',
+                    batch_size=32,
+                    audio_length=8)
+            output_metrics = evaluate(gt_audio_cache=Path(data_cfg.gt_cache),
+                                      pred_audio_cache=audio_dir / 'cache')
+            for k, v in output_metrics.items():
+                # pad k to 10 characters
+                # pad v to 10 decimal places
+                self.log.log_scalar(f'{data_cfg.tag}/{k}', v, it)
+                self.log.info(f'{data_cfg.tag}/{k:<10}: {v:.10f}')
+        else:
+            output_metrics = None
+
+        return output_metrics
+
+    def save_weights(self, it, save_copy=False):
+        if local_rank != 0:
+            return
+
+        os.makedirs(self.run_path, exist_ok=True)
+        if save_copy:
+            model_path = self.run_path / f'{self.exp_id}_{it}.pth'
+            torch.save(self.network.module.state_dict(), model_path)
+            self.log.info(f'Network weights saved to {model_path}.')
+
+        # if last exists, move it to a shadow copy
+        model_path = self.run_path / f'{self.exp_id}_last.pth'
+        if model_path.exists():
+            shadow_path = model_path.with_name(model_path.name.replace('last', 'shadow'))
+            model_path.replace(shadow_path)
+            self.log.info(f'Network weights shadowed to {shadow_path}.')
+
+        torch.save(self.network.module.state_dict(), model_path)
+        self.log.info(f'Network weights saved to {model_path}.')
+
+    def save_checkpoint(self, it, save_copy=False):
+        if local_rank != 0:
+            return
+
+        checkpoint = {
+            'it': it,
+            'weights': self.network.module.state_dict(),
+            'optimizer': self.optimizer.state_dict(),
+            'scheduler': self.scheduler.state_dict(),
+            'ema': self.ema.state_dict() if self.ema is not None else None,
+        }
+
+        os.makedirs(self.run_path, exist_ok=True)
+        if save_copy:
+            model_path = self.run_path / f'{self.exp_id}_ckpt_{it}.pth'
+            torch.save(checkpoint, model_path)
+            self.log.info(f'Checkpoint saved to {model_path}.')
+
+        # if ckpt_last exists, move it to a shadow copy
+        model_path = self.run_path / f'{self.exp_id}_ckpt_last.pth'
+        if model_path.exists():
+            shadow_path = model_path.with_name(model_path.name.replace('last', 'shadow'))
+            model_path.replace(shadow_path)  # moves the file
+            self.log.info(f'Checkpoint shadowed to {shadow_path}.')
+
+        torch.save(checkpoint, model_path)
+        self.log.info(f'Checkpoint saved to {model_path}.')
+
+    def get_latest_checkpoint_path(self):
+        ckpt_path = self.run_path / f'{self.exp_id}_ckpt_last.pth'
+        if not ckpt_path.exists():
+            info_if_rank_zero(self.log, f'No checkpoint found at {ckpt_path}.')
+            return None
+        return ckpt_path
+
+    def get_latest_weight_path(self):
+        weight_path = self.run_path / f'{self.exp_id}_last.pth'
+        if not weight_path.exists():
+            self.log.info(f'No weight found at {weight_path}.')
+            return None
+        return weight_path
+
+    def get_final_ema_weight_path(self):
+        weight_path = self.run_path / f'{self.exp_id}_ema_final.pth'
+        if not weight_path.exists():
+            self.log.info(f'No weight found at {weight_path}.')
+            return None
+        return weight_path
+
+    def load_checkpoint(self, path):
+        # This method loads everything and should be used to resume training
+        map_location = 'cuda:%d' % local_rank
+        checkpoint = torch.load(path, map_location={'cuda:0': map_location}, weights_only=True)
+
+        it = checkpoint['it']
+        weights = checkpoint['weights']
+        optimizer = checkpoint['optimizer']
+        scheduler = checkpoint['scheduler']
+        if self.ema is not None:
+            self.ema.load_state_dict(checkpoint['ema'])
+            self.log.info(f'EMA states loaded from step {self.ema.step}')
+
+        map_location = 'cuda:%d' % local_rank
+        self.network.module.load_state_dict(weights)
+        self.optimizer.load_state_dict(optimizer)
+        self.scheduler.load_state_dict(scheduler)
+
+        self.log.info(f'Global iteration {it} loaded.')
+        self.log.info('Network weights, optimizer states, and scheduler states loaded.')
+
+        return it
+
+    def load_weights_in_memory(self, src_dict):
+        self.network.module.load_weights(src_dict)
+        self.log.info('Network weights loaded from memory.')
+
+    def load_weights(self, path):
+        # This method loads only the network weight and should be used to load a pretrained model
+        map_location = 'cuda:%d' % local_rank
+        src_dict = torch.load(path, map_location={'cuda:0': map_location}, weights_only=True)
+
+        self.log.info(f'Importing network weights from {path}...')
+        self.load_weights_in_memory(src_dict)
+
+    def weights(self):
+        return self.network.module.state_dict()
+
+    def enter_train(self):
+        self.integrator = self.train_integrator
+        self.network.train()
+        return self
+
+    def enter_val(self):
+        self.network.eval()
+        return self

mmaudio/sample.py

@@ -0,0 +1,90 @@
+import json
+import logging
+import os
+import random
+
+import numpy as np
+import torch
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, open_dict
+from tqdm import tqdm
+
+from mmaudio.data.data_setup import setup_test_datasets
+from mmaudio.runner import Runner
+from mmaudio.utils.dist_utils import info_if_rank_zero
+from mmaudio.utils.logger import TensorboardLogger
+
+local_rank = int(os.environ['LOCAL_RANK'])
+world_size = int(os.environ['WORLD_SIZE'])
+
+
+def sample(cfg: DictConfig):
+    # initial setup
+    num_gpus = world_size
+    run_dir = HydraConfig.get().run.dir
+
+    # wrap python logger with a tensorboard logger
+    log = TensorboardLogger(cfg.exp_id,
+                            run_dir,
+                            logging.getLogger(),
+                            is_rank0=(local_rank == 0),
+                            enable_email=cfg.enable_email and not cfg.debug)
+
+    info_if_rank_zero(log, f'All configuration: {cfg}')
+    info_if_rank_zero(log, f'Number of GPUs detected: {num_gpus}')
+
+    # cuda setup
+    torch.cuda.set_device(local_rank)
+    torch.backends.cudnn.benchmark = cfg.cudnn_benchmark
+
+    # number of dataloader workers
+    info_if_rank_zero(log, f'Number of dataloader workers (per GPU): {cfg.num_workers}')
+
+    # Set seeds to ensure the same initialization
+    torch.manual_seed(cfg.seed)
+    np.random.seed(cfg.seed)
+    random.seed(cfg.seed)
+
+    # setting up configurations
+    info_if_rank_zero(log, f'Configuration: {cfg}')
+    info_if_rank_zero(log, f'Batch size (per GPU): {cfg.batch_size}')
+
+    # construct the trainer
+    runner = Runner(cfg, log=log, run_path=run_dir, for_training=False).enter_val()
+
+    # load the last weights if needed
+    if cfg['weights'] is not None:
+        info_if_rank_zero(log, f'Loading weights from the disk: {cfg["weights"]}')
+        runner.load_weights(cfg['weights'])
+        cfg['weights'] = None
+    else:
+        weights = runner.get_final_ema_weight_path()
+        if weights is not None:
+            info_if_rank_zero(log, f'Automatically finding weight: {weights}')
+            runner.load_weights(weights)
+
+    # setup datasets
+    dataset, sampler, loader = setup_test_datasets(cfg)
+    data_cfg = cfg.data.ExtractedVGG_test
+    with open_dict(data_cfg):
+        if cfg.output_name is not None:
+            # append to the tag
+            data_cfg.tag = f'{data_cfg.tag}-{cfg.output_name}'
+
+    # loop
+    audio_path = None
+    for curr_iter, data in enumerate(tqdm(loader)):
+        new_audio_path = runner.inference_pass(data, curr_iter, data_cfg)
+        if audio_path is None:
+            audio_path = new_audio_path
+        else:
+            assert audio_path == new_audio_path, 'Different audio path detected'
+
+    info_if_rank_zero(log, f'Inference completed. Audio path: {audio_path}')
+    output_metrics = runner.eval(audio_path, curr_iter, data_cfg)
+
+    if local_rank == 0:
+        # write the output metrics to run_dir
+        output_metrics_path = os.path.join(run_dir, f'{data_cfg.tag}-output_metrics.json')
+        with open(output_metrics_path, 'w') as f:
+            json.dump(output_metrics, f, indent=4)

sets/vgg-val-filtered.tsv

@@ -0,0 +1,2048 @@
+id	label
+-2toZf00LvI_000012	bowling impact
+-8OE7Vydkl4_000221	bowling impact
+-AEZuuoyJug_000030	playing violin, fiddle
+-CUgrFw8TEI_000045	dog whimpering
+-CexapzRAPQ_000051	ferret dooking
+-DHGwygUsQc_000030	skateboarding
+-G-o-Y4WuaU_000139	playing harmonica
+-G_2v0L4U_s_000078	playing tennis
+-HIPq7T3eFI_000011	driving motorcycle
+-I8C3cRr5TY_000030	female singing
+-K232jBK8VQ_000030	car passing by
+-L_RH-nw11I_000025	vacuum cleaner cleaning floors
+-MfBpxtGQmE_000020	ambulance siren
+-NYZDjBz60I_000085	child singing
+-QWcNg6FCgE_000022	playing bass guitar
+-T06kz4MI20_000030	female singing
+-UJJsEdgqMQ_000011	horse clip-clop
+-VyLmfnIc5Q_000162	driving snowmobile
+-W3y3qz3yp8_000256	people eating crisps
+-WBvJuF2UOk_000030	playing acoustic guitar
+-Yep0TGjWmc_000140	subway, metro, underground
+-YrSxLTPdcA_000004	underwater bubbling
+-YwZOeyAQC8_000002	baby laughter
+-Zd-ZSnZ3so_000159	playing banjo
+-_mqzXgg5eQ_000046	ripping paper
+-c7lpU-_-V8_000030	motorboat, speedboat acceleration
+-c96lccP5nc_000200	skidding
+-eqkzAKGBZg_000030	playing drum kit
+-geN4ECfl0Q_000030	playing bass guitar
+-ibjrtJo9rY_000030	duck quacking
+-nEg1olBLcw_000030	male singing
+-s2G3Kto0Gw_000030	typing on computer keyboard
+-s6dPB8fyQQ_000030	playing electric guitar
+-tGOjLdrF6g_000087	playing squash
+-v12qcLw5u0_000187	machine gun shooting
+-vC3oqlxf4I_000010	slot machine
+-vY141CdTc4_000030	playing bass guitar
+-vmyjjovGXM_000116	cattle, bovinae cowbell
+-vra5dNsP4w_000080	playing bass guitar
+-w7WfMgSBD4_000047	lighting firecrackers
+-wJ_UfBsiR0_000280	playing accordion
+-xzWsDpVEiE_000060	child speech, kid speaking
+-yby37u00N4_000030	playing violin, fiddle
+-zHk3s6BkpA_000030	chainsawing trees
+-zZR-ps0nJY_000137	hail
+0-fd-lvizrY_000024	yodelling
+00eb49xIULo_000030	female speech, woman speaking
+01LPFe-13Aw_000030	playing electric guitar
+01W8XIz7KDM_000007	donkey, ass braying
+02t6zmS4RAk_000102	playing didgeridoo
+038-gneOcks_000309	people eating crisps
+04m_7jCGHko_000030	wind noise
+04sf3v7xOzo_000005	cat meowing
+055LCXe4pR8_000012	people whistling
+09qDi4Auiyo_000030	playing electric guitar
+0Ca2CTVwOxs_000019	cuckoo bird calling
+0CvAFdtyVlo_000023	underwater bubbling
+0G0mSrzOZ2M_000400	driving buses
+0IvNbabusiY_000030	playing flute
+0JPlNHX2HQ8_000049	playing accordion
+0Lro_JzyUX0_000030	male speech, man speaking
+0McmdH07r7w_000050	playing flute
+0OHWW60khJ4_000030	playing bass guitar
+0PZQL-Msz0s_000030	horse clip-clop
+0RFEHUrGOP0_000170	playing acoustic guitar
+0SsaL_YNyjY_000030	waterfall burbling
+0T4gZQwzyKY_000030	people crowd
+0U_Q9JTATCk_000044	owl hooting
+0WIzNXqWrZk_000204	playing hockey
+0XzJKHmoN6w_000019	duck quacking
+0cMnDz8SSwQ_000014	disc scratching
+0dkhsBmUZSY_000030	people cheering
+0fQJ9nShofs_000093	dinosaurs bellowing
+0hCiGC4c97g_000033	crow cawing
+0hWyQpwHNDU_000030	motorboat, speedboat acceleration
+0iVM2GY3R_c_000030	ambulance siren
+0kar1O-1Ckk_000114	playing french horn
+0m3kYCMUuCk_000000	cattle, bovinae cowbell
+0sY8RR7V_q4_000220	female singing
+0tJevlglhe4_000010	railroad car, train wagon
+0uHGQmkKMr0_000223	people marching
+0yAboI4QC6k_000109	hail
+1-2zGkXe070_000098	rope skipping
+10fjkn2eM_M_000050	slot machine
+12tsmtyIALQ_000009	cat meowing
+13LB6yibhQ8_000009	scuba diving
+1CIxzqH4zzM_000040	ice cracking
+1Fp6zPswdjI_000233	tapping guitar
+1JMgZaCb9WM_000204	playing steelpan
+1MCjHVRBDTk_000055	slot machine
+1MLUEfkJDSw_000001	beat boxing
+1MPwoS-R83A_000030	cat meowing
+1Mx2iDMsZj8_000018	playing french horn
+1NTsWn1Gir4_000103	playing snare drum
+1NvpdqTAf3U_000030	skidding
+1NwFHr4VHS0_000090	playing clarinet
+1RB0gsxkPBo_000020	lions growling
+1RSK3TFru0g_000000	sailing
+1T1PLOWu65c_000250	skiing
+1TARmg2FYJQ_000010	people whistling
+1V65GzuCqaw_000030	bird chirping, tweeting
+1Vn7SftZxS4_000030	rowboat, canoe, kayak rowing
+1WaTnza9cn0_000160	playing violin, fiddle
+1YGJDa3aCGo_000289	fire truck siren
+1_CC87jIhXk_000382	swimming
+1acVFuCvOJg_000512	canary calling
+1bBdyTowO-M_000041	parrot talking
+1dO7fONpkvE_000000	people farting
+1eYmBacWt3k_000027	civil defense siren
+1f9IgOjZjn4_000037	rapping
+1gVugA2dsi4_000332	dinosaurs bellowing
+1gXDaVse3SQ_000387	planing timber
+1inu4aoQFKM_000164	planing timber
+1kdGia7plHk_000030	playing electric guitar
+1nDhQKLRJbg_000030	playing marimba, xylophone
+1p8YDM6gG6Y_000014	dog howling
+1t63KIS6F4I_000070	people sobbing
+1x7wVFMW4dk_000030	playing acoustic guitar
+1zWc46eeWLU_000167	playing sitar
+2-Ipq91ns0k_000036	playing bass drum
+21OWtKgJlIE_000270	canary calling
+23ky1UGWeKg_000190	playing bass guitar
+26KmPM2YkmQ_000004	ambulance siren
+2A5eS9kMm-U_000018	owl hooting
+2CebaASg1m4_000030	male singing
+2EeOU7PgSck_000030	female singing
+2F2NSNlc6dQ_000030	male singing
+2FNZwK-4sUA_000030	female speech, woman speaking
+2Jt4iqSqNTg_000012	bird chirping, tweeting
+2LBEllUpWiA_000000	volcano explosion
+2MDjnJzuUaU_000015	skidding
+2NIaPAfScHM_000030	motorboat, speedboat acceleration
+2NjwuyNgNoE_000050	playing hammond organ
+2P7ZXBq5r04_000274	playing cornet
+2RPPKMapBWY_000036	ice cream truck, ice cream van
+2SlVaOyh69w_000219	cattle mooing
+2Sto24aXwao_000097	baltimore oriole calling
+2VdOQylRl08_000002	playing lacrosse
+2YIZLARm8sI_000201	parrot talking
+2d43OFDr5aI_000001	frog croaking
+2ehs70MWQTs_000050	waterfall burbling
+2fCC4BkdMT0_000106	basketball bounce
+2fn6GFSwTEw_000096	cap gun shooting
+2iwPgYGH_Ew_000400	railroad car, train wagon
+2jy1b77hxXc_000136	playing bass guitar
+2lALVOKDQNM_000059	dog howling
+2myGIZCgZ2g_000018	tractor digging
+2rSFLrwcvcY_000020	pheasant crowing
+2szJ9STQPUk_000030	male singing
+2w6jRF1Ekhs_000130	playing sitar
+2xlWTgqPUOA_000004	beat boxing
+2yeuzECPVUI_000033	playing badminton
+2zev5MpJKPc_000039	chicken clucking
+33NCPZjFuLE_000056	playing oboe
+35MtyyqqQyw_000030	playing acoustic guitar
+35c4EPiZ8JM_000030	horse clip-clop
+35iGp2g_U6A_000000	church bell ringing
+37Tl9YROdbA_000077	playing trombone
+3EcAiTE0JyE_000052	playing theremin
+3JyLYEjo4ok_000000	people giggling
+3LfWg5Be60Q_000163	people burping
+3MOG_CAcWkw_000142	playing badminton
+3NcIWxDdTW0_000050	dog growling
+3O8InHTYtk0_000020	male singing
+3Okx0T5vpFc_000192	airplane flyby
+3OxJ7KtIb2A_000100	playing saxophone
+3QHNbJ_XATY_000036	civil defense siren
+3S2-TODd__k_000090	train horning
+3VK-nOg0-RQ_000046	pheasant crowing
+3VSUuTABb3U_000074	wind chime
+3WUTEMZv3EI_000046	slot machine
+3YuBzhAU_Yc_000000	race car, auto racing
+3cMrwXYnjd4_000026	air horn
+3d5tPNd4Olk_000020	wind noise
+3dBQbWPOjjI_000030	playing acoustic guitar
+3djcJkGeJK8_000293	running electric fan
+3e8ECt9wF5Y_000015	playing saxophone
+3en9IzSPnNU_000027	driving snowmobile
+3gTMehPiQ9s_000150	playing harpsichord
+3kXROE2wcRA_000069	bowling impact
+3p9aVzs8aYA_000030	female singing
+3u3iunnXAOs_000432	playing hammond organ
+3wboiuBfavA_000172	people nose blowing
+3yolbg1tH9U_000030	male singing
+4-_AWdbZnzE_000005	playing trombone
+42Iss6TfcpQ_000742	lip smacking
+433xsSMNLf4_000070	playing electronic organ
+43ijm8y4z2o_000030	horse clip-clop
+44UMQ5ZFuuY_000030	engine accelerating, revving, vroom
+457yRHL0f2E_000030	female singing
+45iXudFVQ4E_000000	subway, metro, underground
+46LjKw-7mU0_000030	male singing
+47QYxqXGZ3w_000244	people shuffling
+47SP2azKv8Q_000030	playing electric guitar
+47YlecLyyK0_000030	playing acoustic guitar
+47y5k6vaUxE_000089	francolin calling
+49gi-iYJ1F0_000107	tap dancing
+4CLnZSI8aPs_000092	hair dryer drying
+4DcOTOS_LE0_000454	sliding door
+4DzuWR9ekko_000000	playing bugle
+4E6mA8Y2Be0_000060	using sewing machines
+4FOFcRJR9go_000084	playing glockenspiel
+4H29LCZTMBs_000050	using sewing machines
+4K345_DRFRk_000056	playing volleyball
+4Ofe_ManxZc_000047	playing french horn
+4OxCr981HvY_000016	ice cracking
+4SlcVylJxxk_000297	arc welding
+4WGMFP00rIg_000030	playing acoustic guitar
+4YnMOFstVnk_000066	parrot talking
+4_QGupz8UNA_000189	hail
+4aFirNGu_P8_000381	planing timber
+4dhyddSUAWg_000175	police radio chatter
+4dkU-c4g1VM_000111	dog barking
+4h9o2iL6nps_000050	child speech, kid speaking
+4hU6jqQQUto_000009	playing harpsichord
+4iBqpFUnPoA_000170	fireworks banging
+4j7GbxZQjB8_000024	car engine knocking
+4jHrFbnaVRc_000294	firing muskets
+4kvqtJEFqjw_000190	playing bagpipes
+4ldID97D-oU_000020	people coughing
+4n657Imjmjo_000015	sheep bleating
+4o2IRyXi-aY_000667	playing harpsichord
+4rehS_cPodk_000020	female speech, woman speaking
+4t_Qz9RyUm8_000006	alarm clock ringing
+4yUvIrchOzQ_000280	playing saxophone
+4zf3qRiZ3Ok_000030	child singing
+4zsLfdNLUD4_000033	cat hissing
+50OgBbJZUUc_000064	typing on typewriter
+50jxPCLUFdU_000002	cricket chirping
+53ohFLBl0iE_000052	alarm clock ringing
+542uea0zO1I_000036	sea lion barking
+54XBPEFJQc4_000076	playing djembe
+574NjiOGi5s_000030	female singing
+58KzLvK1OYs_000144	dog growling
+5901zjV6oAo_000006	swimming
+5AFKEd8nSpg_000050	people sniggering
+5CtoZvJaGAM_000096	woodpecker pecking tree
+5D201VjroT0_000229	sharpen knife
+5D2E7s9bEf0_000010	basketball bounce
+5EPnuy_sKHI_000010	singing bowl
+5IZv217s4_E_000049	playing badminton
+5KRxqVykvvI_000030	printer printing
+5S3QDnRCnOQ_000003	tapping guitar
+5Sv97J7mksY_000030	playing electric guitar
+5UqwkZ1XK18_000050	helicopter
+5VyCTHzLVdU_000011	playing bongo
+5WVhslWt1wU_000030	female singing
+5Wb1zMq_DiU_000020	fireworks banging
+5XK1Vgiwllc_000073	playing mandolin
+5X_B2L1-4Bc_000030	playing electric guitar
+5briopN06L8_000000	playing piano
+5eHlhJ-ZOpg_000030	playing hammond organ
+5fZn_7LbKSI_000020	people burping
+5hi4T4Gp6v4_000002	air horn
+5hjKe0FWq9E_000002	horse neighing
+5iEbFJkG6Xg_000557	bird squawking
+5jQLK4Z1EH4_000020	wind noise
+5jt7lR8WY3g_000172	playing castanets
+5lV59hZgwRM_000009	scuba diving
+5mBCF05DV5s_000280	church bell ringing
+5mJ7_05tlhs_000005	crow cawing
+5nscL4EBrXA_000030	male singing
+5r1zW38AWvs_000057	wind chime
+5rP9Z4jEq6s_000024	cap gun shooting
+5xJdFysNSf4_000110	race car, auto racing
+5xefixXFNwk_000020	playing bass guitar
+64eXDlUgPoA_000079	lighting firecrackers
+64lQIoDGX6o_000040	playing marimba, xylophone
+64ollREPrUw_000132	raining
+64zPbHPyiwE_000030	male speech, man speaking
+659mhmSPXWA_000276	bee, wasp, etc. buzzing
+65u3pwOEcBg_000002	frog croaking
+67hDkeQalow_000030	motorboat, speedboat acceleration
+68mXCuRvQkw_000045	people burping
+6ARTjahUaYY_000030	playing electric guitar
+6BQgJ0tvUkc_000162	baby babbling
+6CYhRsU4F34_000000	people whistling
+6EcmHiscsOc_000287	lighting firecrackers
+6GsamqJ5tFU_000075	airplane flyby
+6IMlkVOKxJw_000032	cap gun shooting
+6IQkdce9a7Q_000184	slot machine
+6KO3eMyEeOg_000000	race car, auto racing
+6LB-qRj_zW4_000030	horse clip-clop
+6LKFDTu9vRQ_000018	playing french horn
+6NxeHScEnJE_000000	dog bow-wow
+6OhTwJrVxXs_000028	playing timbales
+6RGa6DvWpt0_000035	people marching
+6UcuQgsHFCA_000142	playing french horn
+6Y6CvX7EP68_000030	singing choir
+6ZbVXBeNsX8_000125	playing didgeridoo
+6aYfccsgIjk_000094	baby crying
+6gTR_Avjz6g_000170	playing cymbal
+6j2g_OZnW74_000189	missile launch
+6mE_v9a5dbM_000030	male singing
+6o0mZVMfKss_000140	people clapping
+6of3tx7IOik_000030	wind noise
+6shIFnN-LsY_000141	playing flute
+6v53uAVpXC4_000071	people babbling
+6wpifZcwOJU_000023	underwater bubbling
+6xAClSJ21qA_000491	rapping
+6xgTrufXcCM_000126	wood thrush calling
+6yBZH5cV7GE_000030	playing electric guitar
+6z_pfZ6Rvfs_000023	playing table tennis
+7-7r-FRwp_w_000041	playing glockenspiel
+72d2TsdeSg8_000000	tap dancing
+75FLwnGZJTc_000125	playing oboe
+75m0cvRBGY0_000030	vehicle horn, car horn, honking
+76F-K-7HUXE_000010	lions roaring
+77rq4-p4vV8_000030	wind noise
+78hdsP0edMg_000030	railroad car, train wagon
+7Ck8cfF2rl0_000200	otter growling
+7ELF2dbWe5w_000010	female singing
+7I_wdG-eOc0_000106	playing hammond organ
+7JT43yyNGkk_000003	black capped chickadee calling
+7JX-Bx0BETQ_000205	rapping
+7LMkG7uISis_000102	playing gong
+7MuetSj86N0_000490	bird squawking
+7NyPcaVKao4_000025	dog growling
+7Odi8SKArQI_000030	playing saxophone
+7P-1-qzwyYA_000055	magpie calling
+7Qr1ncg86N4_000007	lions roaring
+7TMOCRG4EBA_000030	female singing
+7U5V5Teqo8Q_000000	dog barking
+7V6NAsZ86xw_000000	beat boxing
+7VT8p9Er3n8_000020	mynah bird singing
+7Y3u8Aj8UV4_000010	driving motorcycle
+7YGUQYRwnHs_000019	horse neighing
+7YTsyqVSEeI_000006	child singing
+7b__KH3VA_o_000035	people booing
+7caL9c6N1zc_000122	child singing
+7gdSJ30FfNU_000490	people hiccup
+7h7_U2q-VwY_000276	dog baying
+7hdXzJpOXiY_000018	police car (siren)
+7iT77hG1X18_000063	playing erhu
+7kIhqlZok8c_000074	running electric fan
+7kIhqlZok8c_000241	running electric fan
+7lz-THXCwi8_000030	male speech, man speaking
+7ogdSWU90s4_000100	opening or closing drawers
+7pc3c5ZGbwo_000030	ocean burbling
+7rLRSpEqgZk_000253	playing sitar
+7tsuYUeV7_k_000191	airplane flyby
+7vF2Qq0Pg6w_000024	ice cream truck, ice cream van
+7xZxYm27FdA_000020	toilet flushing
+7xaNqQ8FAwI_000100	mynah bird singing
+7yBOHsPAJgw_000040	vacuum cleaner cleaning floors
+7yXROxIZfeo_000053	raining
+80KT6bYCFkg_000077	playing tuning fork
+81ACguOEqoM_000042	electric shaver, electric razor shaving
+82ic2Xisrqg_000030	car engine knocking
+83mmLOdwZlA_000081	air conditioning noise
+85Nd7APr5Os_000028	ice cracking
+85Nd7APr5Os_000052	ice cracking
+87-ZrpDyRHE_000238	cat hissing
+88oLbuKd7Rg_000030	car passing by
+8906Y6i-h10_000102	playing cymbal
+89NzFtLSRSo_000030	engine accelerating, revving, vroom
+8DDro-N5-54_000029	swimming
+8GTkmen1bBg_000110	playing piano
+8IdUE6nhR3E_000030	playing violin, fiddle
+8N0GxZtk9wE_000051	playing didgeridoo
+8NMHjXutgVs_000333	electric shaver, electric razor shaving
+8Rh7NvJDexA_000068	tapping guitar
+8VEqGk0W4xY_000192	playing darts
+8XH7xIWWC6c_000090	people cheering
+8Y9VKxl-1gE_000063	rapping
+8ZWKl-_qHM0_000010	driving buses
+8_xdWIziFpI_000030	baby crying
+8b2ASj5nmos_000251	playing darts
+8c9PJLozdtA_000020	playing bass drum
+8jkr7bOR8ck_000146	playing table tennis
+8lIh0qRN7PE_000220	cattle mooing
+8m7VIFtS4gc_000000	typing on typewriter
+8n76LfbY3qo_000232	chainsawing trees
+8ngu3TPmfZQ_000110	playing drum kit
+8ugZkKeLL7Y_000030	male speech, man speaking
+8vE2wod7rhE_000030	horse neighing
+8ytjUazIdno_000023	playing glockenspiel
+9-N8v-cC0Tg_000002	air horn
+9-xW047dMpk_000125	missile launch
+913ItBzDHLQ_000124	playing synthesizer
+91kWVMnyKxA_000019	bird squawking
+92G0bdxj5ck_000091	mouse pattering
+93a7wS41kLc_000060	splashing water
+93rlsDDmFYo_000110	playing cymbal
+95UKs8K92C4_000110	playing timpani
+96wdXcwIbgk_001238	playing bongo
+97svDuqFctI_000139	playing steel guitar, slide guitar
+98Nc3x8U1JI_000187	playing badminton
+993A2y5lv-s_000030	bird chirping, tweeting
+99WZAe6QKUc_000030	people whispering
+99cGCS0ko2Q_000120	playing saxophone
+99ylFYthGcI_000004	donkey, ass braying
+9A8hgZdD__g_000030	horse clip-clop
+9CFR1VdlIMc_000142	rope skipping
+9CmEsDtIz_Q_000060	playing accordion
+9D9sfe1eaK8_000000	frog croaking
+9HtYErt1moA_000100	playing drum kit
+9IwjfATt51Y_000030	playing french horn
+9JwaE3BmICE_000061	female speech, woman speaking
+9LFGIpAO3NE_000161	tapping guitar
+9LY2BJ2fqts_000090	people gargling
+9Q1RM-pY2yY_000180	playing bongo
+9UvWyax1fEU_000000	people booing
+9Y3ausHODlk_000557	playing electronic organ
+9ZCgk2e7wZM_000269	woodpecker pecking tree
+9ZE18L9NN1Y_000105	striking pool
+9af_fvuAY8E_000167	barn swallow calling
+9exZEq85L1k_000260	playing trombone
+9fvJeyH-4II_000100	fireworks banging
+9gJ4NQYcakk_000030	male speech, man speaking
+9k0OwVahe5Y_000066	playing cymbal
+9pBp5wd9rpw_000043	firing cannon
+9s6jvP1V56w_000080	people crowd
+9t7YT0OKpaM_000130	playing cello
+9tyf9HGsIe4_000000	people finger snapping
+9zriIjvwqJw_000480	people burping
+A08MfFxzmxo_000030	playing accordion
+A0tXM5fSFrw_000062	alligators, crocodiles hissing
+A1-T0wdI8Nw_000070	sloshing water
+A1vf6We9a_Q_000290	mouse pattering
+A2GEU2r5KnQ_000030	playing acoustic guitar
+A551qSirV68_000298	alligators, crocodiles hissing
+A55rHYLkwQk_000000	singing bowl
+A95vqV9oM6g_000081	people marching
+A9pIMNKQWCk_000005	footsteps on snow
+AApTo3l6NfA_000030	train horning
+AFmF56HVvVg_000151	cat purring
+AI3om1uyCH0_000009	fire truck siren
+AJhEl41TC5s_000443	lathe spinning
+AM8-hH1Oahw_000510	driving buses
+AOPgsB4hsH8_000206	electric grinder grinding
+ARFBC4LeCFY_000019	chicken clucking
+ARrb06s5a0Y_000082	donkey, ass braying
+AUufm-TAVg8_000101	playing french horn
+AW-JhveJXFw_000030	typing on typewriter
+AXDomj6KnkE_000141	playing tabla
+AZgG_6NE8j4_000240	parrot talking
+Ac0OxSV8Nqk_000030	female speech, woman speaking
+ActIkLSW20Y_000350	railroad car, train wagon
+AefLmdFYR6k_000136	playing tambourine
+AfrcYQw5mXw_000010	telephone bell ringing
+Agl-AQmIYBE_000030	rowboat, canoe, kayak rowing
+AhUYTb14QZU_000020	chimpanzee pant-hooting
+AiriN8WOgiI_000123	playing bass drum
+AjD1BiY0o8E_000001	pheasant crowing
+AlTNj6IWey4_000112	airplane flyby
+AlebU-Vdy18_000022	playing theremin
+ApMojxDfms0_000273	magpie calling
+AteFCZfJfLY_000011	vehicle horn, car horn, honking
+AvguIvLb0GY_000027	electric shaver, electric razor shaving
+Aw5BwrqdmHc_000010	canary calling
+Aw9arRIoBR4_000030	sliding door
+Aygl9-ur8NU_000001	gibbon howling
+Az1M0iLYjIg_000030	ice cream truck, ice cream van
+B1ax5dX6XrU_000215	wood thrush calling
+B7cF_In3_-c_000030	horse neighing
+B7zgWPjx8hg_000026	wind noise
+B9Mk5n5Zwjg_000240	driving buses
+BBumD37-y80_000110	train horning
+BC4LglYv70Q_000108	playing oboe
+BH8QYqAvO2k_000020	playing vibraphone
+BJ31LCL3Dy4_000100	crow cawing
+BLWCHd07ATw_000080	playing electronic organ
+BM1fw080pSs_000030	female speech, woman speaking
+BM4YyahEm8Q_000078	spraying water
+BO1K4wXy2CI_000299	mosquito buzzing
+BPD7Qj1U_Bo_000131	playing theremin
+BPxW7nP4loQ_000003	eagle screaming
+BTdIM1mncyA_000030	female speech, woman speaking
+BWw6dgq07Qo_000053	playing clarinet
+BYJ2UHIHCLU_000009	hail
+BbUBFko93XE_000031	people sneezing
+BbfDej2cM2I_000001	volcano explosion
+BdIWeKYKIzk_000000	train horning
+BfyYYuE12dw_000006	goose honking
+BkXyLmdb8Yw_000290	playing hammond organ
+BkwStRX3xE0_000010	fireworks banging
+BmsTQHrCwB8_000215	heart sounds, heartbeat
+BnRtoIC87Po_000030	female speech, woman speaking
+BniijKHywXM_000103	playing tambourine
+Bo271H1XM40_000127	arc welding
+BpV7n-YUtos_000248	rope skipping
+BpfM3evN6H8_000009	people eating apple
+BsHgr_sj6ec_000058	playing bongo
+BvdvbeIdUtk_000072	people booing
+C1fdGRZRPtU_000040	barn swallow calling
+C2kKMjYETRQ_000050	playing acoustic guitar
+C5ik_rcugw8_000040	people marching
+C7zLftUgskY_000035	playing harp
+C85lxZAStBk_000056	playing hammond organ
+CA3sbGHEE3c_000001	people screaming
+CFazHdGsxcU_000155	lighting firecrackers
+CG-2XtQI6sM_000000	cat hissing
+CG_qvz_V1Jo_000374	playing gong
+CJjTs72p1gI_000002	alarm clock ringing
+CKwtP-eN1Zk_000014	striking pool
+CUY3hob5V_o_000010	opening or closing car doors
+CUqga7lwvfM_000080	subway, metro, underground
+CVVrs_KA6sU_000030	rowboat, canoe, kayak rowing
+CViouHw-mfQ_000122	playing cello
+CYTTsSPohw0_000122	planing timber
+CdcfD8mg-k4_000030	singing choir
+CeD6RlRSr8M_000099	cat purring
+Cg3XzrFzzpM_000060	playing accordion
+CgevwvZLE3c_000219	running electric fan
+CheeUmf4IhE_000030	fireworks banging
+ChhTVgWMxiI_000030	duck quacking
+CiLwbeRDj8E_000000	people crowd
+CjwqjkkoJHY_000199	car engine starting
+CkYUBci5xEM_000070	using sewing machines
+Cntxv6aE3DY_000030	sliding door
+Co1qXvuwkes_000146	arc welding
+CpW7umx_bi0_000067	playing mandolin
+CqgPmVXNdNQ_000058	striking pool
+Csr7c9uFvQk_000028	dog whimpering
+CvN_oC0AGvM_000340	toilet flushing
+Cvgc82TDNnE_000025	lions roaring
+CvxL2n9DX6w_000251	lighting firecrackers
+CyW4FoAJ1MU_000260	police car (siren)
+CzGGyIj84Hs_000030	pigeon, dove cooing
+D-HXQTcZNGU_000130	female speech, woman speaking
+D109-sQNo1k_000028	sliding door
+D6BCygx6jcs_000000	dog howling
+D7kL3EEOyR4_000050	helicopter
+DAy_bV1d9c4_000046	playing squash
+DGU-HbuX6rs_000230	people crowd
+DJan9OSSF7c_000060	plastic bottle crushing
+DO-9yuU9brk_000028	sea lion barking
+DOi5UxxTknA_000041	driving snowmobile
+DPpo_Whnuqc_000022	missile launch
+DQelhAtUyHY_000030	playing electric guitar
+DR7TdSc2ahQ_000030	sliding door
+DSThhOKXU-c_000250	playing bass guitar
+DSgKhbtDWWo_000400	playing flute
+DX5_AglGFMw_000349	metronome
+DXfTYgSGLac_000177	alligators, crocodiles hissing
+DZo15IMYpmA_000206	vacuum cleaner cleaning floors
+DaMG8zJSkuw_000100	playing trumpet
+DbgRhWmYTJk_000002	frog croaking
+Dbi2L5z8U-w_000020	driving motorcycle
+DdZ6PSUQoQA_000050	female speech, woman speaking
+DfZmOeeF_CI_000024	lathe spinning
+DhaOFNnOC8o_000102	playing steel guitar, slide guitar
+DhxWWDGdF8I_000159	frog croaking
+Dj3sIimPrCk_000330	pigeon, dove cooing
+DmSsL0Xde-I_000005	missile launch
+DpIKdB4c_JU_000030	sloshing water
+DqnMEAN1GVc_000098	baby laughter
+DrPa82cqlSM_000008	playing mandolin
+DroorVxOn5s_000030	engine accelerating, revving, vroom
+DsVtCIaWv-Y_000377	hedge trimmer running
+DtRqBLRUTRo_000069	playing clarinet
+Dtiv9RNaA4U_000106	train horning
+Duk5ikgbUfU_000030	playing violin, fiddle
+DuyL15HJn6M_000036	elk bugling
+DvascfU3OM4_000233	playing bongo
+DwE0cQ3Xz70_000030	chainsawing trees
+Dxxg6NenmBQ_000153	playing oboe
+E0ocfyjk1lw_000129	scuba diving
+E22HBR9rEkI_000030	lawn mowing
+E22UuQ6SRf4_000001	fire truck siren
+E4IHTinI-3k_000010	people whistling
+E4dvhMWr7K0_000140	playing didgeridoo
+E5ICgH7JVFI_000003	driving snowmobile
+E67GhkgB8Jc_000033	cell phone buzzing
+E6tu_4cO7ok_000107	playing cornet
+E8LoFlcAC-M_000051	playing vibraphone
+EDtJ88ZJtWo_000008	playing bagpipes
+EEJp_Ssp0No_000004	dog howling
+EG2bfvkpzjk_000136	playing steelpan
+EGKE_rOo-Gg_000030	playing violin, fiddle
+EHHBn9EAtg4_000040	people booing
+EHHefsog-aM_000069	black capped chickadee calling
+EHkkma0y1T8_000030	people sneezing
+EJudk9RWsZI_000000	car engine starting
+EKkFWhdVAOU_000032	woodpecker pecking tree
+ETcwLdOldMg_000000	blowtorch igniting
+EU3OmHbOUo0_000000	cattle mooing
+EbnPPw9P3MQ_000409	snake rattling
+Ee1Glgpx3YE_000038	scuba diving
+EeUHgSkCSi8_000666	turkey gobbling
+EhDl29RiF74_000085	black capped chickadee calling
+EhaE7gijT78_000119	baby laughter
+EkbcNbEn1Z8_000063	opening or closing car doors
+EoubRuwDlrw_000038	canary calling
+ErdH1gc3ZmU_000003	playing cornet
+F186zkBSFjE_000110	helicopter
+F1ZVQSywml4_000040	skateboarding
+F3yETAYfYZg_000009	playing theremin
+F6xLA2AA2GA_000090	people crowd
+FAdeuN1uc-M_000230	subway, metro, underground
+FCir2lQei8M_000030	playing harpsichord
+FEltES9TUEU_000008	hammering nails
+FGWcwpr_SeM_000133	fire truck siren
+FGoXt7LIK3U_000010	police car (siren)
+FHz8YQy4q5A_000027	tractor digging
+FIPu0jd8I28_000030	people screaming
+FIpCyWCy9Qc_000030	playing violin, fiddle
+FRxNI559-Xs_000280	railroad car, train wagon
+FUVXK29tUwQ_000000	owl hooting
+FWuYLFTe3_8_000000	playing trombone
+FXzP5bUz-Lo_000017	horse clip-clop
+FauD2eg73V8_000030	playing electronic organ
+Fd_SXrGw6ag_000030	playing marimba, xylophone
+Fe9YJozRi78_000148	child singing
+FfpD5XC8b5w_000137	playing bongo
+FglnuP1jpRY_000030	playing cello
+FhHBIlZ_5T8_000035	wood thrush calling
+Fj34VCzy_Og_000030	horse clip-clop
+Fpqf057G_SY_000000	chipmunk chirping
+FpwtNUX45qU_000047	snake hissing
+Frs4_Uf8Tq4_000127	ice cracking
+FtCT62fiyrU_000270	church bell ringing
+FtNV_Gq62l8_000019	cat meowing
+FudSk5EUbAY_000156	playing ukulele
+FvZqgCIbO2Q_000003	hail
+FyszP9lfbDk_000001	playing didgeridoo
+G-5AgMNzjv4_000017	vehicle horn, car horn, honking
+G-Eokh465wM_000030	printer printing
+G-IdABSxeHI_000097	dinosaurs bellowing
+G-jsAK9ITwM_000030	ocean burbling
+G6FhQuR3_88_000000	playing congas
+G6nSnVQCxBQ_000095	elephant trumpeting
+G7E7D2Z_Juo_000070	people burping
+G7F8HVNw1lI_000081	scuba diving
+G9AKWSzZtWI_000030	people eating
+G9F38sObAns_000025	playing harpsichord
+GAFJeF_AqZA_000086	hail
+GBf5DgubSuE_000030	wind noise
+GD8dVFZaWNU_000030	skateboarding
+GDQjuDpqnJI_000030	wind noise
+GL1TqKjpv1Q_000047	playing theremin
+GLA-upuVPSA_000057	police radio chatter
+GLtFkIbCZOY_000140	pigeon, dove cooing
+GMNJCJ0ykfc_000050	male singing
+GOFDdcvXq40_000030	goose honking
+GPl4twCSrLQ_000001	coyote howling
+GS_JqZCyqOc_000050	stream burbling
+GT2frI8BMMM_000013	vehicle horn, car horn, honking
+GTZkjw4aVn0_000030	engine accelerating, revving, vroom
+GUSlicDnqIA_000045	playing congas
+GX4kLN3hW4Y_000149	planing timber
+GXIPKWMIVhs_000072	playing oboe
+GXRHmy5Bqas_000008	vehicle horn, car horn, honking
+GYJCyn2piCc_000329	lip smacking
+GZoVDjx9ltQ_000235	playing erhu
+GZoypVKRpCo_000003	cuckoo bird calling
+G_hP5gvRfNw_000033	cat growling
+GaFqib8bCLM_000019	tapping guitar
+GbTzdC4mOtQ_000030	machine gun shooting
+GbUoljsX3lg_000672	people gargling
+GgUkhedV5e0_000190	female speech, woman speaking
+GhizOxu0ZpI_000060	people belly laughing
+GidBfE5JU3s_000005	vehicle horn, car horn, honking
+GjKjnplphn4_000200	playing acoustic guitar
+Gjide6V8U-E_000039	dog growling
+GoMH9AL7YRA_000050	ambulance siren
+GvPc1ncg0OY_000138	people booing
+Gwp62TNrER0_000014	barn swallow calling
+GxUovR3d2aM_000019	car engine knocking
+GzAdcTtwkM0_000011	missile launch
+H-ZKdWCEhbI_000140	fire crackling
+H-jnsSCa-c8_000090	playing lacrosse
+H-rd3O5haG8_000070	playing bass guitar
+H0zmJjMoV-4_000012	playing squash
+H1lx8lLLceQ_000120	machine gun shooting
+H2r4JHm00Vg_000260	sheep bleating
+H6onyc5r6os_000024	heart sounds, heartbeat
+H6z_gPH8m2A_000055	people crowd
+H7BcUVlPDsg_000026	parrot talking
+HCPKDz63_s4_000000	child speech, kid speaking
+HCuORBJf-Ho_000027	playing cornet
+HL36YvzbFYs_000210	goose honking
+HL_E1j069EI_000030	female speech, woman speaking
+HOD29VAXJD8_000030	car engine knocking
+HOI0ZaKLAMM_000030	fireworks banging
+HOI7KapLzz4_000030	playing violin, fiddle
+HOupeg-QhHk_000073	yodelling
+HQSafj2aCNI_000100	playing banjo
+HQlV2jYCz5k_000030	playing violin, fiddle
+HR35d67Dhts_000108	singing bowl
+HRaGv5q3P3E_000000	opening or closing drawers
+HTRRMT1NQOc_000060	playing saxophone
+HTqUtEGJ0As_000030	people whistling
+HUP72tlgzyE_000066	playing badminton
+HUWvhtKby-A_000033	car engine knocking
+HW2o3t3fE_k_000062	francolin calling
+HX2ccFGAuMU_000163	electric shaver, electric razor shaving
+HX5BeffFwV0_000008	smoke detector beeping
+HaABMNzUOvo_000030	wind rustling leaves
+Hakqd6g2jaY_000110	helicopter
+HcO60nHH4W0_000023	playing bass drum
+HckqMrtU3dg_000133	playing double bass
+HebxWsaO-LA_000115	train whistling
+HkCt4hh_x58_000030	rowboat, canoe, kayak rowing
+HlUvoEXQZYk_000007	playing tambourine
+Hlp5qKMfdYk_000180	playing bass guitar
+Honj-TQHx3U_000129	airplane
+Hqhi7LioGyM_000030	playing marimba, xylophone
+HsCj9l5Barg_000045	fire truck siren
+HsX5XlPFOWI_000380	lawn mowing
+Hum53_V1zw8_000001	wind noise
+Hwp_62TYhDk_000110	playing marimba, xylophone
+I-WMZh-ieC8_000280	playing harp
+I-qeWJGSXuQ_000083	playing bassoon
+I4ffG1Bh-d8_000156	playing oboe
+I5wV1AFabIA_000029	frog croaking
+I6_30m_TQ2o_000000	playing tuning fork
+IBy30oL3yxw_000399	playing harpsichord
+ICajcUYAan8_000410	people babbling
+IEiseWb8Tao_000080	playing acoustic guitar
+IF92YmTMtdk_000089	cattle, bovinae cowbell
+IFGbGcs3bQQ_000034	chinchilla barking
+IHaWOJuekYY_000109	tap dancing
+IINqN6L2NsY_000285	tapping guitar
+IJvYFkrfjBg_000049	tornado roaring
+IKj9E33H8e8_000012	pig oinking
+ILBWV9AFKDU_000115	playing ukulele
+IN-9DFoS3fM_000007	bird squawking
+IWVztd9QsXg_000005	owl hooting
+IWhgJgeUQuA_000090	playing bagpipes
+IYhq5aun18M_000181	police radio chatter
+IZAasx5KIKE_000010	fireworks banging
+IaAKobKeOtU_000271	people marching
+IeD5tKVhuI4_000030	playing synthesizer
+IeK6EDl8Z_k_000033	people clapping
+IeW36MTcnBs_000117	dog growling
+Ieca4fwxfyY_000049	tractor digging
+IicM8tOXAFg_000146	pheasant crowing
+Ik40yoz30vE_000068	woodpecker pecking tree
+Il82kphC6es_000172	dinosaurs bellowing
+IlLCyGNjG3M_000060	playing harp
+InxgcOFzxWY_000070	chicken clucking
+IpDU10kKguU_000311	vacuum cleaner cleaning floors
+IqCRbzhPkvU_000000	lawn mowing
+IrcX151sayY_000098	tapping guitar
+IrkyGrHjygY_000020	tapping guitar
+Irx-WWFsQYU_000667	people eating
+ItnOPd_CktY_000020	people coughing
+IuTgZQVcMBg_000007	sloshing water
+Ivho6H4q1zk_000017	typing on typewriter
+Iylzuk-0j64_000163	slot machine
+J0ZBjy_EEtg_000015	people clapping
+J18R3qBnJtA_000120	waterfall burbling
+J1kAKMeULF8_000500	subway, metro, underground
+J3K5HEX3gko_000030	playing banjo
+J4VeWujsLJg_000030	typing on computer keyboard
+J5ugw2GUbnY_000001	dog whimpering
+J7fVkoC-Ha8_000711	people eating crisps
+J82OaPeyioI_000030	horse clip-clop
+JC33o6YxH9c_000220	playing piano
+JHNBF0WJ-EM_000029	people belly laughing
+JIdUC1zZb9M_000060	rowboat, canoe, kayak rowing
+JK4YikH2myA_000161	playing vibraphone
+JKrghKg6UBU_000260	ocean burbling
+JKxdjXEI9Wc_000015	eagle screaming
+JLPpMZlBOEI_000038	playing accordion
+JQ3bFZbatGk_000030	people running
+JQr-BRXrjN4_000002	airplane
+JVevxopJjU8_000823	playing tabla
+JXi1ZtJecYo_000001	bowling impact
+J_k6z7_YVJU_000090	playing piano
+Jbiig_IQdIo_000282	cap gun shooting
+JcXhB_4B32o_000090	playing clarinet
+JeJGThFGm80_000001	lighting firecrackers
+JfAjUMKjoVI_000460	playing harp
+JfiFq8tn5Pk_000009	playing steel guitar, slide guitar
+Jk-SBbw7Afg_000140	driving buses
+K-B9CIVeQ_U_000030	horse clip-clop
+K-MCXLQmnFA_000004	playing banjo
+K3KsP-m_c5I_000353	basketball bounce
+K5HBK1c7noI_000010	cat meowing
+KBc_FdBzN2U_000017	wind chime
+KFFJI_TZmoY_000047	crow cawing
+KJwga4gMEzU_000239	people slurping
+KJxSJR3v6oE_000013	church bell ringing
+KKd2qSxww1o_000002	typing on typewriter
+KM_VudA7hgo_000030	people running
+KOzRB30gxpE_000362	planing timber
+KQbCjNzlYPs_000082	writing on blackboard with chalk
+KU5WQZsoKRE_000079	child singing
+K_8tBU1LYxU_000000	chicken crowing
+Kbc8ioemPlA_000081	tractor digging
+KdD8xho7ymw_000037	cat purring
+KfqdB93utIg_000000	waterfall burbling
+KfyYM6nq--A_000011	playing vibraphone
+KnwgxGWxp7Y_000025	people whistling
+Kp0W7S-oExs_000030	driving buses
+Kq0Dbp3C4d0_000017	dog howling
+KrUuPSM4LxM_000215	magpie calling
+KsuQWEN0COQ_000199	playing darts
+Kus5SmqOIrA_000024	mynah bird singing
+Kwha8UYndzI_000090	playing didgeridoo
+Kz4Jm9_iFeg_000038	hail
+KzK6d6Qpu_o_000010	dog barking
+KztFbSJPxg0_000197	planing timber
+L4u9LOjcXoE_000000	people sobbing
+LAaJfzvvlTI_000053	lions roaring
+LAx_fanEB_g_000168	arc welding
+LB2EbSmDSKw_000007	baby laughter
+LBH_D9h18bw_000042	rope skipping
+LCcPzeH_Cn4_000160	sailing
+LE49c8e5VMU_000049	mynah bird singing
+LEpzp8DnWyY_000026	sharpen knife
+LGMZ9c7q8tE_000168	cat purring
+LHYHo8wJF74_000342	playing oboe
+LJsSbG5A1y0_000000	lighting firecrackers
+LL618LsL2zY_000030	pig oinking
+LMbyOx04l9E_000036	vehicle horn, car horn, honking
+LNl3ANFth4Y_000021	mynah bird singing
+LOLFOiNiS1o_000067	sharpen knife
+LPTsZZVr06o_000030	people eating
+LSsYBN_RvPc_000122	rapping
+LWrztDg2BGI_000245	playing synthesizer
+LYUkVukRObA_000236	pigeon, dove cooing
+L_Da1Sv1iKU_000028	playing didgeridoo
+L_OvLmH_feU_000021	dog growling
+LaGhL-3ctOc_000048	playing double bass
+LbjkUR-ERQw_000049	opening or closing drawers
+LciaPQ1XV3c_000217	playing badminton
+Lfmcj5VW6VE_000050	playing acoustic guitar
+LgdtTzvKnT4_000030	rowboat, canoe, kayak rowing
+Lj4Ngu0ars8_000138	electric shaver, electric razor shaving
+LlRZR8xPOEw_000021	frog croaking
+Lmp51YN-7wc_000466	people marching
+LtqXpk2YGls_000010	chainsawing trees
+LuxrhiicesU_000000	donkey, ass braying
+LvzMerRGbCE_000099	bouncing on trampoline
+LxdOWpwSzi0_000400	mouse pattering
+Lz8Ytz12MrU_000120	chopping wood
+M0EaEBlx5fk_000126	yodelling
+M9cNmb9HKPc_000110	turkey gobbling
+MApvC99wovc_000159	car engine starting
+MEtdxR3RdEA_000180	playing piano
+MFEhejrPVmw_000040	dog barking
+MMTvsiahcsc_000002	fire crackling
+MMjEIFDYQvc_000117	yodelling
+MQPNvRDVuUs_000100	playing french horn
+MQPggq37uX8_000003	scuba diving
+MQcS6DqCjKQ_000030	playing vibraphone
+MRnnE9MTm64_000052	driving snowmobile
+MTD6-1mrtP8_000072	owl hooting
+MVmJujaAocY_000030	baby crying
+MXmetP4F-EU_000019	door slamming
+MdUG2H5K5eg_000117	roller coaster running
+MenAsca8z6s_000137	slot machine
+Mf6bCl5HKgc_000000	wind chime
+MfSXrFJt6d4_000007	motorboat, speedboat acceleration
+Mhzz75z8mbY_000166	playing ukulele
+Mk8fhA3DAsA_000030	turkey gobbling
+MkrFhq3F_z4_000100	playing accordion
+MlX7I-OZIyk_000062	playing timpani
+Mmyr6Gpclbk_000070	bird chirping, tweeting
+Mnv4KVEt18I_000018	people giggling
+Msh94MTYC6A_000290	chainsawing trees
+MshXUve673A_000363	elk bugling
+Mvn2oFoKxwI_000128	people booing
+Mvue0y_EsDU_000000	orchestra
+MwVghEDjyQM_000030	people sobbing
+MwsoiJOqg_g_000030	duck quacking
+MwyzEfk2xbA_000054	playing double bass
+Mzc3DajWA0k_000030	ice cracking
+Mzgas545UXU_000090	playing snare drum
+N09QFSbvIC4_000150	playing electronic organ
+N2DQWIePoLs_000030	playing violin, fiddle
+N3_jZV1ejnA_000030	crow cawing
+N5CNEOKptjo_000000	splashing water
+N8cNWpCL0Rs_000183	owl hooting
+N9cM9BdATNs_000081	people booing
+NAETplWD64g_000030	playing harpsichord
+NAk-PU3X_DQ_000026	mynah bird singing
+NBeonGAqO84_000032	playing bugle
+NCdkXluu-D8_000350	playing harpsichord
+NFd5Zot-0_c_000006	heart sounds, heartbeat
+NJfJ4E9EVoM_000120	people whispering
+NN6mOUDBjEM_000042	dog howling
+NWSsGcjVRDw_000245	playing tabla
+NZs6RgHZOoI_000013	firing muskets
+NfcCnLiHlqU_000134	playing erhu
+NhO6B0zM9Pc_000030	playing electronic organ
+NjKRF79wl5Y_000110	wind noise
+Nkz9_eGsHKY_000057	people booing
+NmdqThtOVro_000160	beat boxing
+NnNm_oqkG0o_000050	people sobbing
+NniPHshHj9M_000068	playing didgeridoo
+NqxCX4G3N2g_000107	playing volleyball
+NrCNo4V7RVM_000030	lawn mowing
+NrWxMrh7cGw_000210	playing cornet
+NtJQ6W2o0EI_000075	canary calling
+NwIDavS0llk_000010	chicken crowing
+O-C9p_sK_eI_000030	horse clip-clop
+O0QV4_JRM0M_000002	car engine knocking
+O15FUv56iCc_000040	playing cymbal
+O3geFV-GoqM_000031	fire truck siren
+O5LFB39yCA4_000085	missile launch
+O5TMWyFd1DQ_000180	playing vibraphone
+O6_sGC3v96g_000006	wood thrush calling
+O7KCtFRaWck_000080	alarm clock ringing
+OEu8pZpN8ZA_000000	using sewing machines
+OIqUka8BOS8_021217	warbler chirping
+OLtTuBhG-og_000075	playing squash
+OTVFQoNRQTs_000060	playing bass guitar
+OWlCVuOznw0_000019	arc welding
+OZ14CiqpJL8_000010	child speech, kid speaking
+Ocdu7Lz0IuU_000003	francolin calling
+OdGHvGlSUcM_000157	playing timpani
+Of59qi5xxkM_000050	playing drum kit
+OiIaJb68Haw_000050	playing banjo
+OjOQ0K6lza8_000018	playing tuning fork
+Okd7ksWR-fc_000547	swimming
+Oljdv3iSTBc_000047	people eating noodle
+Om-Uc7ia1f0_000320	playing bass guitar
+OrueZOVOAD8_000010	motorboat, speedboat acceleration
+OtDVd-1zaqU_000030	motorboat, speedboat acceleration
+Ow1ZEhmP3qU_000116	typing on typewriter
+OyoJ99jDQdo_000147	playing didgeridoo
+P1eMMIK0cTs_000011	mynah bird singing
+P2taxpwuzcw_000053	wood thrush calling
+P2wbv4C6bBA_000210	barn swallow calling
+P35m_Rn7HbA_000030	motorboat, speedboat acceleration
+P5Y1D-fSVfg_000054	lip smacking
+P6sG1m6C4zI_000081	mouse pattering
+PavKY6YlSl4_000026	people whistling
+PawUc0pqf9M_000260	car engine starting
+PbomocKzqKU_000109	splashing water
+PeJxiP0CPn4_000025	playing didgeridoo
+PfwBOCxEst8_000243	cheetah chirrup
+PjbxRjKvzw4_000030	wind noise
+Pll-TpbHen4_000067	airplane
+Pp61sP7bols_000076	cap gun shooting
+PrIQbadXX74_000692	playing oboe
+PsmihTl5Cx8_000060	pig oinking
+Pu4BCOv6e5Q_000020	fireworks banging
+PvE48Ub_CgA_000034	bird chirping, tweeting
+PvpA8y7-ZC4_000101	people burping
+Pvt8VUQ_Bso_000030	playing vibraphone
+PxEpiEid_c8_000177	slot machine
+Py5s1uL46L0_000100	male singing
+Pz618GchhGI_000001	otter growling
+Pz9BhPMUzv8_000258	lathe spinning
+Q38lPvwj5Gw_000234	swimming
+Q57DFiTwcM4_000221	people eating noodle
+Q5jnMD1z86k_000287	people eating noodle
+Q7X3fyId2U0_000090	tornado roaring
+Q7ZPnRQraJk_000200	playing clarinet
+Q9AvyaxgRRo_000141	playing steel guitar, slide guitar
+QBFaKTDXCCQ_000120	playing acoustic guitar
+QBfcf-k5U28_000007	vehicle horn, car horn, honking
+QCX3H9wXgpo_000053	cap gun shooting
+QEcQtxP1fdg_000056	playing bongo
+QGkUBiVG8-Y_000034	owl hooting
+QH5ZtCI9Hts_000125	chopping wood
+QL6Ws4i07is_000040	goat bleating
+QO9sbXhMq08_000220	people hiccup
+QOFuXRetSLI_000064	arc welding
+QT1nE5lR7wA_000035	cat growling
+QTqN9c6661s_000000	forging swords
+QUMzyZRYpWs_000019	playing steel guitar, slide guitar
+QXEq7sE7dqg_000030	police car (siren)
+QXjaLCotbpY_000055	playing zither
+QZ-cG6VdBHM_000070	helicopter
+QaBmzAFivPQ_000096	people marching
+QcL-X7hJJYQ_000000	people whistling
+Qd9_UMNMhcA_000010	typing on computer keyboard
+QdEYMboSweA_000001	playing oboe
+Qdrcv-ZjC-g_000037	car engine starting
+QgEi6pAW36g_000150	male speech, man speaking
+QgHYiH6ES08_000120	basketball bounce
+QgQKqaMqRgs_000062	playing bongo
+QhRcayuLZ48_000390	playing piano
+Qlj2HEcX05Q_000136	playing saxophone
+QpA1_cezBwA_000123	mouse clicking
+Qr2PeUXBJu4_000197	playing erhu
+Qs8RjZlOcdU_000030	car passing by
+QsHeqaa4Ckc_000072	people whistling
+QsNHM92SIvo_000000	people whistling
+QvEMTs9_RQE_000010	lawn mowing
+Qwxa7ZCEBQs_000006	cricket chirping
+QyRrtn5AoSg_000280	playing saxophone
+QypTigdvLWU_000017	firing cannon
+R0YwusOkMx0_000008	bowling impact
+R1h5rRHM3oI_000000	donkey, ass braying
+R29qwv_mh4E_000018	playing bassoon
+R3VnztSX-k8_000200	ice cream truck, ice cream van
+R7KnzEqUGAc_000040	playing cymbal
+R7bSeIfRG-Y_000590	eating with cutlery
+R8cWq9GoEpE_000037	pheasant crowing
+RBHqcDacio0_000182	beat boxing
+RCIMcizSSZU_000044	francolin calling
+RDuDqEmKucQ_000030	motorboat, speedboat acceleration
+RJNjaPizyKg_000099	playing theremin
+RKZmAYXXWbg_000247	canary calling
+RM6uf-sdVQI_000043	playing bass drum
+RN96eLdMN_I_000005	bull bellowing
+ROsAOQe62gs_000050	playing electronic organ
+RVqCdL7_G2Y_000030	car engine knocking
+RWnvolYKQ2o_000414	lip smacking
+R_SNrPUIa1A_000140	playing bassoon
+R_yW6SKe_-M_000080	people booing
+RaawVrMvP7k_000048	pheasant crowing
+Rb0IEIeJTKY_000002	basketball bounce
+Rc_exQXrUG0_000100	skidding
+ReZUlDwGaLY_000080	playing marimba, xylophone
+Ria-XrpfgsA_000089	people marching
+Rifu8nB2cCs_000043	cat purring
+Riu9TpsQ_mk_000009	pig oinking
+RmGGiQMURcQ_000022	people sniggering
+RoLNzNAv-Ig_000030	motorboat, speedboat acceleration
+RrpMoJrp4AY_000180	people crowd
+RsYAulhucVI_000011	lions roaring
+RtHMCINXA0s_000052	cricket chirping
+Rur-IfwPZho_000051	dog howling
+RwE9JAktTvU_000580	people coughing
+RyV40yhlOeU_000419	people marching
+S29c6T__5HU_000003	playing timpani
+S3Ipyd9HHLk_000185	magpie calling
+S45cdr4x-mc_000080	chainsawing trees
+S9fw7NHd2eo_000380	playing electric guitar
+SBYzwBhUpYs_000166	playing badminton
+SBwOIJoGChM_000116	hammering nails
+SCjdlZSW8nY_000111	playing table tennis
+SGkzdDWFIHI_000085	playing bass drum
+SHebWHn0c2Y_000005	chopping wood
+SPnZIDCnKwM_000030	orchestra
+SS6iMabGB1Y_000020	chimpanzee pant-hooting
+ST33aEP5Hbc_000006	train horning
+SXC13GS87Co_000031	woodpecker pecking tree
+SXHYr-7nPaw_000030	playing drum kit
+SYDQX7Whjm4_000061	woodpecker pecking tree
+SYWqIfMOmGE_000051	hammering nails
+S_0v5j4S100_000039	cat purring
+S_qPgRNSkIw_000370	people clapping
+SbXyRN0DD-g_000080	dog bow-wow
+Sc-Ld96kbN0_000144	playing synthesizer
+SdCzaAUA6Xs_000005	playing djembe
+SeZm-iy9n8M_000150	playing electronic organ
+Sf0aZczIZVU_000040	playing cello
+SgYh5Lb7tlM_000130	playing flute
+SifYJFmSSRw_000123	playing marimba, xylophone
+Sl4weBj8xfc_000030	typing on computer keyboard
+SoVEYhxQabk_000103	canary calling
+Spm_zrjedzk_000392	cap gun shooting
+Sqq2dUA8t3A_000586	playing harmonica
+SvJ0kUY22C8_000055	turkey gobbling
+Sw6qDVMsR5M_000030	playing violin, fiddle
+SwQie7apk78_000198	playing darts
+SyEVBFw_9oE_000120	people screaming
+SyfyWK7dKXA_000021	playing squash
+SzmORuHD4g4_000059	wind chime
+T-AN31N4LD0_000050	people screaming
+T0NMgZC7CDU_000011	chipmunk chirping
+T19Xf5-OTHw_000130	playing piano
+T2zZbnu_NtM_000029	playing table tennis
+T4KEGH_8lY8_000119	playing timpani
+TAdH0kUJj9k_000050	helicopter
+TCUnK4k7QZ0_000000	telephone bell ringing
+TDh8_ixGzIo_000030	printer printing
+TGngN3n7EMw_000024	airplane flyby
+TLSmnnnyhEk_000030	people shuffling
+TMyd50KWyNo_000311	people slurping
+TNCcQfbselM_000120	francolin calling
+TQapWHNS5FE_000024	car engine knocking
+TRW01xXMMqg_000210	playing accordion
+TRt_14JcRWQ_000080	playing bass drum
+TTElms_ZWqI_000428	hair dryer drying
+TTstWFDMmqc_000030	people whistling
+TUPEF6PQxow_000132	rapping
+T_iuImHtqUI_000010	people sobbing
+Ta__Ev0mkBk_000030	chainsawing trees
+TakDv24Tiq0_000032	plastic bottle crushing
+TcN0QofoTvg_000221	playing erhu
+TdkhMZZvdgc_000006	owl hooting
+Tdyh5ziqH-U_000007	lions roaring
+TiaGOZ-ibxw_000411	people booing
+TriRWR9YiNk_000016	frog croaking
+Tse5rzNV5dk_000084	pheasant crowing
+Tze9ybKops4_000020	playing synthesizer
+U3-h9ZARqD4_000264	police radio chatter
+U34oQw93afs_000219	playing tambourine
+U3zsgbf9WHQ_000194	horse neighing
+U4RRMpX2wCU_000010	toilet flushing
+U55bYLMVKiw_000193	pheasant crowing
+U6vVDGaKL3Q_000354	bouncing on trampoline
+U9qUXBqIoZ0_000106	dog howling
+UA62hwIBgGY_000020	chicken clucking
+UFIi1OuMx0o_000302	rope skipping
+UGwl5VOHuaw_000200	playing accordion
+UIFxlzHYPBM_000060	gibbon howling
+UJ1lZOY9LSY_000035	playing didgeridoo
+UM1j8kFaxi8_000020	motorboat, speedboat acceleration
+UOL-hbkzUN4_000010	barn swallow calling
+UOlwg402_r4_000070	people clapping
+UPUwaW8jfhA_000030	ice cream truck, ice cream van
+UQonGRRRpv4_000024	goose honking
+UUKyUUjv8qg_000030	church bell ringing
+UZAB21OSorM_000007	electric shaver, electric razor shaving
+UZYfRXafn9I_000005	ferret dooking
+UZp0AcdimvA_000021	cattle, bovinae cowbell
+UeCkRYU_SuM_000100	playing accordion
+Uf2j1VbOk8c_000055	pheasant crowing
+UfG4dP0szuY_000040	fireworks banging
+UjTYiJ0dm8s_000002	vehicle horn, car horn, honking
+UkdS0cwAGYE_000010	car engine starting
+UnGLtJX29Hc_000043	planing timber
+UoFgJXGWJXA_000111	playing congas
+UpWivODbpIY_000059	owl hooting
+UsJAb6aftq8_000580	playing bagpipes
+UuuQH-TFxMo_000034	missile launch
+UzKZijSs4-A_000004	fox barking
+UzPSMiqeH3Y_000118	singing choir
+V-ZbY0SL2XI_000040	people sniggering
+V1ALglq7_x8_000018	dog growling
+V6lQVpw888U_000590	machine gun shooting
+V6y-jCli4I4_000000	cuckoo bird calling
+V7SGeTSJz9w_000090	skateboarding
+V82SmRI0GHY_000030	playing clarinet
+V83lIhKVraY_000125	playing darts
+VCEicqV_2Xw_000030	ambulance siren
+VDXN0xwWgRA_000083	playing bass guitar
+VDzkPfnI1g4_000093	playing djembe
+VEER910vqMk_000002	duck quacking
+VEhmvrgrZb0_000000	chicken clucking
+VFj1vFMV3dQ_000025	playing darts
+VGrI3TMjWog_000120	playing vibraphone
+VHQjG81NcXE_000030	crow cawing
+VS9R3iOc4Vk_000027	pheasant crowing
+VU9W8Y1E5u4_000030	bouncing on trampoline
+VdxslFvStdo_000370	female speech, woman speaking
+VfXlyIjtfo4_000117	baby babbling
+Vgs_XjEqKl0_000020	people sobbing
+Vh4E5JPTMBM_000146	typing on typewriter
+VhLn9pUFwXw_000039	chopping wood
+VhUG4vTpPUo_000324	ripping paper
+VhsFniEZO-k_000026	mynah bird singing
+Vkbp8VmL3pM_000040	people sobbing
+VkgLWYydiPE_000125	tractor digging
+VlGuwiKwJAM_000027	playing sitar
+VlkgwzKAamE_000051	ripping paper
+Vnnw7lK63rg_000041	playing snare drum
+Vt3qBXzyS5k_000280	eating with cutlery
+VwZ8gzI3qNE_000106	people slapping
+VwqcV76E6Nk_000000	people booing
+VwqqmiiznQU_000028	woodpecker pecking tree
+Vxs0xCJI92Y_000080	driving motorcycle
+Vzb427ZmWvw_000220	fireworks banging
+W0PwVllBxkI_000114	playing steel guitar, slide guitar
+W1o_XgU8lec_000050	skateboarding
+W2_8zRHaEPk_000150	playing vibraphone
+W2gkFTFR8mw_000047	rope skipping
+W4eT7fj-aIA_000201	driving snowmobile
+W5oXrz8dqBk_000030	playing piano
+W5wBkCwEEmY_000140	playing banjo
+W7OJevEgq7w_000000	dog bow-wow
+W7u5kEt-q-8_000000	playing tennis
+W9L5rTbcMFA_000004	people eating noodle
+WABbXpAT_UA_000049	playing bagpipes
+WAhoodHHm2w_000001	playing squash
+WBOqGIqUwGg_000090	people sniggering
+WD0aVtBqoxo_000120	goose honking
+WDmJ4ZtLuNU_000102	playing timbales
+WGHTlOM4-3w_000050	sheep bleating
+WH7LBLKyEkA_000241	playing mandolin
+WIWRYG4vJC4_000020	people burping
+WIZTFH-LGpo_000001	planing timber
+WJQ27fShKvk_000000	playing tennis
+WQFZLDitkkM_000067	eletric blender running
+WQuoH_HyUAk_000030	playing cello
+WRvPzjj5uoE_000134	ice cream truck, ice cream van
+WWzD6E9Wp_k_000260	playing cornet
+WXMt58sLsf8_000028	zebra braying
+WZ568vdA7bU_000070	plastic bottle crushing
+We-E7-Sx3Zo_000260	barn swallow calling
+Wg86ercBjY0_000002	playing clarinet
+Wh8A7CAuLe0_000028	barn swallow calling
+Whjk5Fvue1o_000030	singing choir
+Wj0qIPUjTfE_000008	lions roaring
+WqKP-0cSKgs_000030	dog bow-wow
+WvRkqVmRH0g_000088	playing harp
+WvcM0ueEjfo_000050	people burping
+Ww3CMatNd84_000721	cat purring
+WxQHtaD0Yqg_000028	tractor digging
+X-o1Twh5SFY_000032	playing steelpan
+X0gT3reH8A8_000120	people sniggering
+X17lq90OIO8_000020	dog barking
+X5C9NY9MjA4_000105	train whistling
+X7EGSxA-aCI_000132	child singing
+XBAwcPvVSoA_000068	lathe spinning
+XDMTylVtYx4_000190	race car, auto racing
+XEOUYLlaef4_000003	rope skipping
+XJnKU_SXYlM_000049	playing tabla
+XK4Ws-xvt10_000267	vacuum cleaner cleaning floors
+XKp4HCxVmaI_000017	vehicle horn, car horn, honking
+XLTqSk1Z3D0_000000	police radio chatter
+XM6eeVHjmLk_000001	dog growling
+XNgq-cDV7FI_000101	dinosaurs bellowing
+XOTSovKwxLk_000030	child speech, kid speaking
+XSJzshsMz30_000030	chainsawing trees
+XTDo4OaFapg_000100	hammering nails
+XU8dCEdiGWc_000010	crow cawing
+XUyBxCbiv7A_000073	playing bassoon
+XVveRibUh18_000023	frog croaking
+XWp8qMpnD00_000026	electric shaver, electric razor shaving
+XYZ4Nd4qV-I_000101	people humming
+XdSCT_cQDbE_000010	splashing water
+Xgm17YbPztk_000022	playing didgeridoo
+XiExpKM1Hpo_000160	playing trombone
+XlJ-tAbzzSg_000234	alligators, crocodiles hissing
+XtExs7nIzts_000034	people booing
+Xv4AVT2QYhA_000100	rowboat, canoe, kayak rowing
+Xxq7CElxJLc_000063	singing choir
+Y798EuJZaPU_000017	playing squash
+Y9Oee-VRfVA_000339	airplane
+YC_k4W1YaDw_000030	race car, auto racing
+YD41QET24SM_000125	playing badminton
+YD7jTek7yVU_000206	arc welding
+YEatlg_b0BY_000054	people burping
+YISopDKuQ0k_000050	playing accordion
+YJ5xLJ85AwM_000106	tractor digging
+YOTnbp40tf4_000030	male singing
+YOrImbuhsQ8_000027	lions roaring
+YS_zTwf-FRo_000092	playing ukulele
+YU78jPcU6FI_000070	playing trumpet
+YUXZVAQ1iJ4_000007	volcano explosion
+YUcdJy-rpD8_000590	raining
+YVOmkmjoT40_000030	ocean burbling
+YYgYiO9DjEY_000161	tap dancing
+YbALYr-5WpM_000000	playing harmonica
+YbOztklOkF0_000023	goose honking
+YcvHv44MYiU_000027	barn swallow calling
+YdjsatpizhE_000023	airplane flyby
+Ye72yJyWxs8_000021	airplane flyby
+YeEySSrxwpg_000078	barn swallow calling
+YfZp5C7xrKs_000181	playing bassoon
+YgySYOAi8JQ_000396	skiing
+YhJwTBFij48_000015	motorboat, speedboat acceleration
+YjCLRifFCj0_000010	skateboarding
+YjJioclqdQ8_000150	wind noise
+Ys1P04EjGH4_000196	playing bassoon
+Ys9j6IBcFBo_000024	opening or closing car doors
+YvBCKb1LbCk_000095	fire truck siren
+Yvq8WrFpXhE_000057	people crowd
+YwNdDHEhm2g_000005	duck quacking
+YwTFxcWCac8_000381	electric grinder grinding
+YyqqXEmYPIA_000020	ambulance siren
+YzBaTwjmikc_000018	hammering nails
+Z-V-1iUbMWI_000520	lions growling
+Z1BhAXfiZtU_000037	vacuum cleaner cleaning floors
+Z4QR8uvx_Wk_000169	reversing beeps
+Z5SyUJSDCOA_000562	ripping paper
+Z7Hzc1Yw2aY_000060	sloshing water
+Z93pTtHnDXo_000110	playing vibraphone
+Z9nG2fIh214_000075	chinchilla barking
+ZALP7Di4HaM_000180	playing saxophone
+ZAZZ1wImM9M_000010	singing choir
+ZCA_NapBTlg_000060	dog barking
+ZDDnEdzjyrE_000597	playing tambourine
+ZFGcmmpt1bs_000094	playing bagpipes
+ZL_MxixlnHE_000079	reversing beeps
+ZNboftBNdyY_000406	cap gun shooting
+ZPODO-Ehl_M_000030	male singing
+ZQO_uhrJPNA_000110	playing violin, fiddle
+ZUjum5gZMKM_000140	playing accordion
+Z_Bk_CnpWsY_000198	people sneezing
+Z_sW4UxpbbY_000050	using sewing machines
+ZbtuNDtoyOI_000030	sliding door
+ZcskQV2A2cQ_000030	playing flute
+ZdtaSkUkrIE_000256	police radio chatter
+ZeDa5hT2ffk_000071	police radio chatter
+Zgbuj3y2iuY_000210	cattle mooing
+Zh2whhvFWsM_000016	pigeon, dove cooing
+ZhLwVzOZziA_000368	blowtorch igniting
+Zi3FOnx4nuk_000001	playing table tennis
+Zj73Wh6LEiU_000120	skateboarding
+ZjN9CL7B-9I_000239	playing timbales
+ZkfUo4l9ruc_000090	chainsawing trees
+Zl_ZWSLB8Ic_000024	sheep bleating
+Zs8liAFeuuQ_000058	smoke detector beeping
+ZtPoTqVxVvU_000050	helicopter
+Zu0BpngzT_Q_000007	bowling impact
+ZuwSkX0RQQY_000343	playing tennis
+ZxmKMSUpbvc_000065	car engine idling
+ZxpiZiSAm9I_000060	turkey gobbling
+Zy70U6w0yXw_000088	mynah bird singing
+ZyUqhIDVuNc_000541	scuba diving
+Zz0fhQuHZEE_000012	penguins braying
+_0iRtZRG6UA_000047	woodpecker pecking tree
+_4RRKzDUd60_000079	lathe spinning
+_7GnnuKVVCM_000023	engine accelerating, revving, vroom
+_8FhgH9k7Rw_000120	vacuum cleaner cleaning floors
+_9wN5d1Z1ak_000024	lions roaring
+_CF34A0RrPs_000018	horse neighing
+_Cks36T64zE_000061	striking pool
+_DdVu5sPsjk_000490	people whispering
+_GaEZe-Z73k_000233	fire crackling
+_HRn4aOhjhU_000016	canary calling
+_H_W34UobYU_000459	bouncing on trampoline
+_HcIHVLRzpM_000450	female singing
+_NShiXyBmsY_000270	train wheels squealing
+_Ow1h1eTNk0_000178	playing trombone
+_SfaPFwwJHs_000026	train wheels squealing
+_T0iCBHWKt0_000101	pig oinking
+_T5ZUrmRiQI_000108	playing ukulele
+_Uyw_Legahg_000045	tap dancing
+_VOx5BWJsyQ_000030	raining
+_WQQ3QvGrYw_000340	child speech, kid speaking
+_WUAz2RAZZc_000201	planing timber
+_YF3aFSsgUk_000093	playing steel guitar, slide guitar
+_YhSeML8rQo_000109	alligators, crocodiles hissing
+_aX_UzkXRd0_000140	helicopter
+_cvucKdFb5I_000043	people booing
+_dIzu78Ld2w_000166	lathe spinning
+_gQFB_Utuf0_000077	cat caterwauling
+_j8zzvBts98_000000	splashing water
+_m6lwfMU8Eo_000272	electric shaver, electric razor shaving
+_pSMw5FKHX0_000040	people sobbing
+_pfccpy7Cqc_000180	typing on typewriter
+_ru-n--PRNA_000030	police car (siren)
+_t-Abwz6JG4_000031	baby babbling
+_t259gootxc_000190	female speech, woman speaking
+_u9zUuBdo1k_000000	cat growling
+_vkXDgupDN8_000250	sailing
+_wvB2HlVn1I_000050	engine accelerating, revving, vroom
+_xGLwynjhSs_000010	playing french horn
+_xq-9GZBfrg_000014	pheasant crowing
+_yVgX3hi1OQ_000195	driving snowmobile
+_zTmqhuLwAM_000001	donkey, ass braying
+a0LIemH5Cw0_000010	people clapping
+a3ZAFViNYyk_000000	swimming
+a57DUeBMeHY_000320	rowboat, canoe, kayak rowing
+a6CPpulnJ2A_000420	stream burbling
+a8fa79w2aIQ_000023	lighting firecrackers
+aC3nlLHFOfk_000030	playing violin, fiddle
+aCnLa_H0-P0_000000	magpie calling
+aDXQSTbKlIc_000010	playing cornet
+aE32elV-Jtk_000210	people crowd
+aG1wGSIqGR4_000013	frog croaking
+aHzkCSXsrqg_000038	vacuum cleaner cleaning floors
+aJ41sea1s0U_000080	people farting
+aNArqTW4cbc_000025	vehicle horn, car horn, honking
+aNOELrfjAYY_000000	vehicle horn, car horn, honking
+aRI4l67ZlYQ_000063	planing timber
+aSYCwv_hda8_000030	subway, metro, underground
+aSleAKgkDDk_000000	playing accordion
+aVs2QBhLIhY_000162	playing didgeridoo
+acYp_SYmHs8_000164	running electric fan
+aclGsdr83pM_000400	playing saxophone
+aezIOAga5V8_000070	child speech, kid speaking
+agMolFR_pFc_000075	train whistling
+agrdgrC2cdI_001076	dinosaurs bellowing
+ah5cSy0yXs0_000178	slot machine
+aiTXGmkpfnk_000030	playing trumpet
+ainzK7QuseU_000001	dog whimpering
+aj6kdMafoek_000693	hair dryer drying
+aju2z1N0aOo_000030	wind rustling leaves
+ap3PdrjChdo_000040	playing bassoon
+apTvGua1-FY_000271	playing guiro
+asXWEB_SBEI_000060	playing cello
+atT7DPwTkds_000130	people clapping
+auHL-4XCFAk_000030	driving buses
+b-8lh_tfhLQ_000124	hair dryer drying
+b-gza98ikBo_000020	playing snare drum
+b2bpNgK0Cnc_000250	orchestra
+b4Bu0AHwBWs_000084	woodpecker pecking tree
+b4WK1A7DK18_000018	crow cawing
+b8q6Z7dtRvg_000030	playing flute
+bBMcsO6IeDE_000021	lions roaring
+bF89h31EEzg_000000	golf driving
+bFmIV3pNJPY_000001	basketball bounce
+bI_4_x735PA_000020	typing on computer keyboard
+bJtu55jpzNc_000140	playing violin, fiddle
+bJzkn2kRh8g_000070	helicopter
+bLAz_kbihLE_000147	elk bugling
+bMNcdb3Eeds_000064	civil defense siren
+bN9fXjHalIY_000065	playing timpani
+bPNt6iVmemQ_000504	playing bongo
+bPfP2rjJfDY_000609	playing ukulele
+bQV7q5VRaH0_000174	car engine knocking
+bT8QfAM9NRA_000197	cutting hair with electric trimmers
+bVdI6laTOXI_000480	people screaming
+bVskpqAJF8E_000116	people eating crisps
+bYT-N-_u448_000217	civil defense siren
+bZUN1tQnuDQ_000001	child singing
+b_C-fNIS8aI_000000	cat purring
+baVILr18Y9A_000015	civil defense siren
+bd-swxc3o4w_000260	playing hammond organ
+bo9sSwEqnzs_000030	orchestra
+bokQgOSQ2OA_000001	playing squash
+bpF6KhK8El0_000030	police car (siren)
+bsM-z2joYss_000030	child speech, kid speaking
+bsUBSFHXY0g_000040	helicopter
+bukJZ1FxymQ_000390	male speech, man speaking
+bw3GIZLj6kM_000000	playing piano
+bx5BUbiIXFw_000107	child singing
+bzxjT3h2ir8_000105	lip smacking
+c3UPyEZ1yQY_000070	typing on computer keyboard
+c4M3JIyAPcM_000020	playing bass drum
+c5dPZoWwmC0_000020	driving motorcycle
+c6e4pxgoCls_000105	magpie calling
+c84w0ECD-Lc_000010	ocean burbling
+cAI0pcOwk2g_000346	elk bugling
+cEddS8Y-qZc_000510	people clapping
+cFNcpddGRno_000340	fireworks banging
+cIHKR2E1uiQ_000303	smoke detector beeping
+cJSWXGTJMcc_000018	rowboat, canoe, kayak rowing
+cL_nCiBnlbk_000001	playing bugle
+cMdnie91zp4_000000	playing trombone
+cNUIc68WpD4_000075	people marching
+cRiW0u0QY18_000030	playing trumpet
+cSym5f2jySA_000005	chicken crowing
+cUBHfozbsao_000044	playing harp
+cV4QlanVa9w_000070	basketball bounce
+cVhWB3IniBo_000014	playing tuning fork
+cZfuBCVV6n8_000390	eating with cutlery
+ces9pc_r6Wo_000036	child singing
+ckwEyopmfKs_000024	crow cawing
+cmkEW0KJDYI_000165	arc welding
+cp-ZI_fQ1l0_000154	airplane flyby
+cwQY1bck2G8_000070	playing bagpipes
+cx4QSvep_wE_000009	train horning
+cxFdK2G6wq0_000030	playing bagpipes
+d-UQr-8UEUY_000069	playing saxophone
+d05lXeFKDn0_000275	pheasant crowing
+d4yBeEbVp1Y_000030	typing on computer keyboard
+d5HmVBPY1Qc_000230	playing saxophone
+d66pNyYB6WY_000013	people burping
+d8gWsmBdBhE_000097	playing sitar
+dBivnkxNOOc_000175	playing vibraphone
+dECLS-JHWYA_000000	vacuum cleaner cleaning floors
+dK46EdcZFzg_000030	playing trumpet
+dNMCURn41wU_000179	playing djembe
+dN_EzmXbsu8_000016	playing bass drum
+dSeWq0Qd9Hs_000318	playing tambourine
+dVg4IEbk-l8_000010	cat meowing
+d_OIBYBwexQ_000160	playing accordion
+daHwPM2azrc_000036	wood thrush calling
+dfr1OFz20sI_000000	goat bleating
+dgSOnxqNtFE_000246	people coughing
+dgS_Fy1FiNA_000110	people burping
+dhG_GSGW_RI_000004	volcano explosion
+dlJm9R5t_qg_000030	playing hammond organ
+dlWrMn_RDg0_000120	playing bassoon
+dqymshfwGEE_000030	playing saxophone
+duca08sjlbQ_000001	playing bongo
+dugd_OSzghs_000203	ice cracking
+e0LMGLr-T-I_000029	air conditioning noise
+e3ZJnO3s53o_000016	child singing
+eANsaSAzHm8_000010	driving motorcycle
+eBFPD8YrqiA_000140	driving buses
+eCpA_7B-k94_000030	dog bow-wow
+eDqfHtuB8Hk_000015	snake rattling
+eEUsoUKPxy8_000187	basketball bounce
+eFaLkcfCzos_000140	playing cello
+eK97_rb6BsY_000072	playing gong
+eLyQDSo2NAM_000129	opening or closing drawers
+eOJQsk_kdWI_000032	ice cracking
+eS8Tf1hfwxk_000205	sea waves
+eSEIPV-qSj0_000020	squishing water
+e_3GUZmPFBI_000020	playing erhu
+ebhtW1tIXRY_000002	donkey, ass braying
+ecTDu-EX3WE_000019	car engine knocking
+ecq96FWbCF0_000037	gibbon howling
+ed4wVB_RhHw_000011	baby crying
+ehw6y3_g-8A_000757	ripping paper
+ej6jlkTeobU_000002	car engine knocking
+el3i-oj08Q4_000173	playing oboe
+f-XD-BgLWk0_000000	skidding
+f5c5KuWylig_000343	vacuum cleaner cleaning floors
+f6Wl-9pzib0_000032	cattle mooing
+f8bMURZiPiU_000019	people whistling
+f9U7g3g4voA_000026	golf driving
+f9c9YZ8WgjM_000037	bull bellowing
+fD362l9P3u8_000041	tornado roaring
+fFn2P7ZRIeM_000480	playing clarinet
+fNlGlh1GaeA_000013	heart sounds, heartbeat
+fS17RfJYjS4_000001	pig oinking
+fTT_D_d_5FA_000080	people clapping
+f_55S5G8M2s_000000	playing harmonica
+faFCcN6y-C8_000020	ferret dooking
+fcyUlEGvMdc_000037	playing volleyball
+fj0qlDdWt1M_000158	playing squash
+fknz5hZg_3I_000295	playing darts
+fmc6hwse-IA_000085	skiing
+g-CydtX7btM_000086	eagle screaming
+g-u5YOJu_gY_000230	lawn mowing
+g1n-ZaW0QHQ_000095	reversing beeps
+g5OBeqvOmRU_000001	bathroom ventilation fan running
+g8E9gBfe8B4_000180	female speech, woman speaking
+gH25X_mj6mc_000210	ocean burbling
+gJbMwvsUyA8_000000	driving motorcycle
+gL2i_DTGUEY_000028	cattle, bovinae cowbell
+gLTvwzBktxE_000015	airplane
+gLj93C9rRsg_000055	playing tambourine
+gLokxx-ruH8_000230	playing piano
+gM9WSjAPDVc_000030	people babbling
+gPwtTVH44OY_000030	people shuffling
+gW-1oOsNGJs_000010	playing harp
+g_axbxP7Amc_000071	playing harp
+gaFtxq1hBU4_000118	spraying water
+gcBUpboDmjc_000033	hammering nails
+gfgv17hOPIM_000040	playing marimba, xylophone
+gjJ4nqwlgnE_000010	playing hammond organ
+gm0HkvshnPk_000340	cattle mooing
+goS6rwhPth4_000026	mynah bird singing
+goz-IQ8s6uk_000050	skidding
+gpaX15tTUoc_000017	cat growling
+guvnNwCkhcs_000030	people sniggering
+gwzqjVCFNqA_000040	playing clarinet
+gyioxO7fWzI_000046	lions roaring
+gyjZ7tnnZeA_000132	playing theremin
+gyt54t3R_BU_000032	blowtorch igniting
+gzqq0knK2FA_000003	cat meowing
+h-Z5cTyu4LE_000150	wind rustling leaves
+h0025UfxME0_000308	sharpen knife
+h0V51dolEjA_000194	airplane flyby
+h5Gq0y3qkX0_000063	volcano explosion
+h7EWw2n5D5I_000050	train horning
+h7pz6niHZuw_000006	donkey, ass braying
+hEePXITb26o_000042	playing harp
+hFVd2Em9-cc_000100	toilet flushing
+hQvIg0t546Q_000146	playing vibraphone
+hUlqIdQFuxE_000030	basketball bounce
+hV_CjOK-mME_000030	people sniggering
+hW-RxgLN2l0_000007	owl hooting
+h_tdr4t6unw_000300	typing on typewriter
+ha-5LhgpVmQ_000946	playing tympani
+hc9aQ8VL9o0_000083	playing steel guitar, slide guitar
+hcR4BiG8sZs_000150	playing clarinet
+hdphUn6ihrA_000450	lawn mowing
+hdqCHBTwnuQ_000253	playing badminton
+hgcLJFz2WKQ_000512	police radio chatter
+hlKkLqHpJ_s_000400	chicken crowing
+ht3jNf66nbo_000286	missile launch
+htRB8f0r2rg_000027	playing bassoon
+hvoOSCZo2-E_000030	cattle, bovinae cowbell
+hy87-XUmhkE_000004	playing timbales
+i-SmzP7T_E8_000295	skiing
+i3nEgFq4yfo_000578	heart sounds, heartbeat
+i4IsKRvCLi0_000036	lions roaring
+i5dz5NV4Vpc_000007	crow cawing
+i6BBre7xV-c_000937	dinosaurs bellowing
+i9PvGS9Xr9k_000332	lions roaring
+iAqJ9lPCU4w_000024	chicken crowing
+iD8gRmmiiqU_000130	driving motorcycle
+iDODqIflQ1Q_000061	parrot talking
+iFU48OcnO7k_000000	woodpecker pecking tree
+iLaLf95DcQk_000040	duck quacking
+iQMIGLrKlTI_000260	playing accordion
+iSjvZiygjCQ_000510	playing cello
+iTd7hOI27BE_000048	playing harp
+iXslVMHwkTU_000212	people eating noodle
+iZR9dpO64NA_000011	cap gun shooting
+i_-LCRDriig_000030	ocean burbling
+i_hhSKWxzeU_000038	frog croaking
+ibd7CKcSiTI_000122	playing bass drum
+icK4IQb2KsE_000000	hail
+ieRU5f5P4B8_000350	cap gun shooting
+ieXdQlIBgLk_000030	playing marimba, xylophone
+iiUvfvkeo0c_000237	disc scratching
+ijirbb9m05k_000285	swimming
+ipo5U5Grsno_000020	people cheering
+irUkV1DP7Cs_000030	playing cello
+irhsdhRIUwI_000010	fireworks banging
+itH-fbb9Ook_000250	ambulance siren
+ixv1jovJe3c_000151	playing timpani
+j-GF_0RxUlg_000176	playing bass guitar
+j-hyPaKjCAU_000030	playing accordion
+j0NNSluEaS0_000150	heart sounds, heartbeat
+j15Ldqb_XVw_000020	fireworks banging
+j2OhKQ6sm0o_000077	people eating noodle
+j3A_ekLNu1Y_000008	car passing by
+j4GHwj1Yqz8_000076	ice cream truck, ice cream van
+j5oZYOBOppQ_000003	mouse squeaking
+j6f4pheXNDE_000108	tractor digging
+jB-OcexH1n0_000033	cat caterwauling
+jBCKFPXuFOw_000086	strike lighter
+jBZ1C1ihCIY_000005	playing bongo
+jL4h1-_LECU_000022	church bell ringing
+jQRurvUk2xs_000051	writing on blackboard with chalk
+jVG2LQ2kA1Q_000067	playing glockenspiel
+j_WKRbDVZhs_000071	barn swallow calling
+j_vtU1U9rg0_000042	playing volleyball
+jb92NmGYNbU_000279	police radio chatter
+jd2ENRtbxRQ_000010	people coughing
+ji-27X81tIs_000133	playing bassoon
+ji4T1ArqCz0_000017	fire truck siren
+ji8HeUiTfoU_000030	orchestra
+jld-wHLRUWM_000020	playing accordion
+jmLX2yQ4eKk_000007	hail
+jsw5soBYfsc_000020	people farting
+jt7w_UY4yUI_000040	lions growling
+jxnPU7Okb5U_000043	playing snare drum
+jzw_Wa_TXVo_000018	playing squash
+k-AKVEheu4g_000096	alligators, crocodiles hissing
+k-jDS1jp_AA_000014	firing cannon
+k4h2VtrPwus_000161	rapping
+kAWAs_7SaKw_000000	bird chirping, tweeting
+kBmcp8nL6Kg_000195	playing didgeridoo
+kCsmvK06SCA_000254	playing sitar
+kDwFyUvAi4w_000077	playing bongo
+kEQJJyYkYTY_000200	child speech, kid speaking
+kL6xemyurI8_000140	people eating apple
+kPp7CwFBl1c_000030	playing violin, fiddle
+kPpaeW3DObU_000481	playing castanets
+kPus6xz6fN8_000030	car engine knocking
+kSdqIpAMz_M_000175	playing snare drum
+kSwrdM7UD98_000057	owl hooting
+kTyaqJIhX6Q_000020	playing accordion
+kVMXMaTyEbE_000116	playing theremin
+kVtj0bAYAF8_000000	people sobbing
+kW23iJgtyfk_000002	raining
+k_NIUqHoNz4_000037	playing bassoon
+khZPuH00RNc_000332	yodelling
+kjtZNsHp_a0_000330	lawn mowing
+kkgjiCKHvoY_000449	firing cannon
+kmPmQ6aylRc_000012	reversing beeps
+koTbsmbqyxo_000103	people booing
+kp_7Sd6s0h8_000306	people eating apple
+kqvpyaIls0c_000090	playing cello
+ksaiDSSJeOg_000030	playing cymbal
+ktBzLsiL6l0_000157	playing steelpan
+kxl_ZU3j99A_000415	missile launch
+ky92PHpUpEA_000050	playing accordion
+kyEDPVvDQt4_000040	bird wings flapping
+kz849EPouys_000318	magpie calling
+kzntbWmyWBg_000074	playing squash
+l0DQpxoSr2Q_000040	playing banjo
+l3i-cKkVL-o_000007	car engine knocking
+l3rzkrm98J0_000001	alligators, crocodiles hissing
+l3uGoel_Ats_000000	people crowd
+l4XYVX79H58_000400	people babbling
+l5LnwNRK7Bw_000030	playing cello
+l6uZDuUsdpc_000010	people burping
+l7ELBtiVtQ8_000190	striking pool
+l8bdmlXL-Lk_000197	playing didgeridoo
+l9ple4xWo3w_000193	chopping food
+lAF2dHM7Tyc_000170	playing electric guitar
+lEzMz9odWXM_000058	lathe spinning
+lGsxnfOPaUw_000022	baby crying
+lGtRJjnC4PI_000210	airplane
+lKhe8BxkRnU_000025	wind rustling leaves
+lLme6yedI6w_000040	cricket chirping
+lN2kwc34bo0_000050	train horning
+lP5znTMLevo_000030	playing bagpipes
+lQG8CRumj3g_000560	playing cello
+lUWrhn9z9FI_000096	pumping water
+lXIaZksDY38_000030	people shuffling
+lXwEV2S1rt4_000150	using sewing machines
+lc1QTC0R_CQ_000018	people shuffling
+ld9b7tfnqTE_000109	playing erhu
+ldF2EJCVY3g_000147	playing theremin
+ldvcH7bOy_o_000184	playing french horn
+levuF973w8s_000250	playing french horn
+lg6X9iqcqXI_000233	playing table tennis
+lg7DqdnmkmE_000130	skateboarding
+lj-PczKzEaw_000040	using sewing machines
+ljXTXoBG9rg_000077	pheasant crowing
+lnatlhCU5kI_000420	singing choir
+loMPOYNM66g_000123	playing timpani
+lqVp4OJ4hbY_000044	lions roaring
+lr1RLADQXNg_000110	helicopter
+lrFFGvB03Fw_000071	golf driving
+lsBttXzhPHw_000144	playing sitar
+lt5H2iH9Ln8_000120	chicken crowing
+lwgKXn21ymc_000774	people whispering
+lxFVAc2dHVM_000152	fire crackling
+lzLgjt8VRmU_000000	skateboarding
+m-4-BAv8cCQ_000380	lawn mowing
+m-NpPmAkncw_000030	male singing
+m0g-zWJJClA_000150	playing banjo
+m1lFSuSixy8_000350	people marching
+m1lFSuSixy8_000613	people marching
+m2E4i-EzHIE_000085	people finger snapping
+m4j5XY09HlE_000021	car engine idling
+mCyvq9TF5Ms_000052	typing on typewriter
+mInTDyk6c2A_000012	writing on blackboard with chalk
+mPnRdL1sC48_000240	people eating crisps
+mQ60N4HdDyI_000102	machine gun shooting
+mRCzIaqRG_c_000000	using sewing machines
+mWGLXbNhuB4_000096	hammering nails
+m_7BjYa44lo_000030	child speech, kid speaking
+ma0P7XOsBgE_000030	people running
+ma2RuCUufcI_000036	fox barking
+maUlA8WWTEQ_000004	hail
+maVHGHl01Yc_000034	lathe spinning
+mcVY3xsxgcU_000060	playing bagpipes
+mi9AokZ8m5s_000849	shot football
+mjK1vNF3lKE_000023	playing theremin
+mlihNhHFGTM_000030	playing harpsichord
+mt13n4XleGY_000030	orchestra
+mwu46g-jnac_000170	bird chirping, tweeting
+n-PjT4mDn9Y_000173	playing bagpipes
+n0PnM0u47m4_000042	mynah bird singing
+n0gO6pPICi4_000065	playing mandolin
+n21m6N5UmNk_000002	firing cannon
+n2CgftHGLJ0_000030	driving buses
+n3bX64Z_Yds_000000	playing clarinet
+n4wpVSIu7c0_000087	beat boxing
+n6PQq584nWA_000010	playing trumpet
+n8vhraccEnc_000009	dog howling
+nAtvzIyRwnU_000100	playing saxophone
+nEBUuVsMtGE_000000	church bell ringing
+nGIVQLeZ76E_000103	bowling impact
+nHDsu69zzSA_000000	skidding
+nIHYEEVzuzE_000095	canary calling
+nJ7TBigS5bY_000018	people booing
+nLOOmtvC9Hc_000066	playing steel guitar, slide guitar
+nLVmclZYZMY_000200	people screaming
+nP0vO3Xv10M_000010	dog barking
+nPCYkMhaLYs_000024	roller coaster running
+nTo6W-50CDg_000018	whale calling
+nXc-dHK2A2A_000016	playing theremin
+n_F_tRGGoEA_000107	frog croaking
+ngJ_Us2C19g_000040	police car (siren)
+niYH8Dpt4uE_000140	cattle mooing
+nnyll58-lrA_000009	wind chime
+nowY2-6reIk_000030	pigeon, dove cooing
+nz0qYNbFGD4_000030	people coughing
+o2-6TSqWPCY_000170	people clapping
+o2qd4hsquvE_000056	bird squawking
+o4F5dtUXivA_000034	playing steelpan
+o6kY64rTk2k_000291	singing bowl
+o7mBR043UCs_000014	pig oinking
+o8iHgGRzcTE_000020	people clapping
+o8oMY-WgW9Y_000030	wind rustling leaves
+o9uGfNn4JyU_000062	lions roaring
+oBrRQ5SiJTQ_000210	driving motorcycle
+oCZ3WCK5BZU_000000	driving motorcycle
+oDAI33ybJlo_000029	playing theremin
+oDuiwpaep1k_000035	sliding door
+oEEOscuru6s_000280	playing flute
+oEXqWoSZ9Ww_000024	playing erhu
+oG6EUnQjeF8_000077	swimming
+oIUi8gFI_XY_000178	cat purring
+oIXRSpjo7vk_000170	wood thrush calling
+oJ4m2OvhA8Q_000100	playing cello
+oSsLQCIJjyE_000030	singing bowl
+oVxKyGnz-IA_000230	chainsawing trees
+oXXHkjFLN3E_000237	electric shaver, electric razor shaving
+oX_XdxqTE9Y_000110	bird chirping, tweeting
+oYe46obCJhc_000039	alarm clock ringing
+oZ6l0EStee4_000011	police car (siren)
+oZKVPzRyn50_000432	playing electronic organ
+oad_agP1oJU_000287	playing harpsichord
+od2HXuT_NuI_000100	playing cello
+oePtbOc8Hqs_000000	foghorn
+oeSxlmkPj78_000030	ocean burbling
+ofFtXFnfebQ_000684	cat purring
+ohh7mWALd_k_000473	pheasant crowing
+olZa2vOpbD4_000110	male speech, man speaking
+omiGYobPra4_000100	toilet flushing
+onqGNrWQ7us_000587	machine gun shooting
+or7ikBeUhBg_000020	driving buses
+osA1JXFL2Gk_000021	parrot talking
+otp3r8SfygA_000102	people shuffling
+p-DcPCo7Swo_000086	playing double bass
+p4RWTSRg6Bg_000290	people crowd
+p5LsBog-XRk_000130	playing saxophone
+p5j91ecL43Y_000030	people whispering
+p8HTTAhm5ic_000100	waterfall burbling
+pAe8kcpjZII_000010	playing theremin
+pKUzj3ckXvI_000010	toilet flushing
+pNiB5w3JBVI_000003	spraying water
+pQrnDC-kPHk_000106	sharpen knife
+pRdi3oChUR4_000020	baltimore oriole calling
+pUMZEzdKmPM_000136	owl hooting
+pVJY1Q137cw_000681	cat purring
+pX_Sg3xDAUg_000000	people burping
+p_KsZsJwH0w_000555	sharpen knife
+pdzAs6Be2sY_000139	people gargling
+piYKrS14dxA_000113	mynah bird singing
+pnFtPlslgGw_000019	plastic bottle crushing
+ppDvhlGr5nI_000003	golf driving
+ppLjxFk8C4M_000023	heart sounds, heartbeat
+pqDHX5R4sdg_000220	female singing
+pqElMm80SX8_000025	airplane flyby
+prq7EqBGWaY_000035	playing harpsichord
+psz3LAhSi9U_000001	yodelling
+pu9pO-rCzy4_000153	people farting
+pugRM2Nsnyo_000283	church bell ringing
+pukny4fvbOQ_000040	playing clarinet
+pxpIsajKD-Y_000042	reversing beeps
+pxpIsajKD-Y_000065	reversing beeps
+pyHJrlNMYwo_000350	sheep bleating
+pzixqhh0xG4_000175	golf driving
+q0Hz09My-_E_000018	lions roaring
+q0R8KXxZOZM_000070	people farting
+q0lahEg486Y_000295	tractor digging
+q1oBXqEFXy4_000070	sloshing water
+q5fUdJoUrAE_000257	beat boxing
+q7cvNFoT9nQ_000027	lighting firecrackers
+qA-yeGwsVn4_000018	pheasant crowing
+qBDrrE6LnUo_000103	bird chirping, tweeting
+qBmsSZQ7HNg_000360	railroad car, train wagon
+qCcC7n2mOC0_000074	playing harpsichord
+qIcEYC46zmI_000087	playing cornet
+qJJEBEajF1M_000017	air conditioning noise
+qL-4fJyDGXc_000893	people eating noodle
+qNi5Xlf2ZVY_000510	people clapping
+qORUGCczq74_000042	swimming
+qRm5Yh3JPSg_000016	playing tambourine
+qRwun6pFuNA_000010	playing banjo
+qTRrHj-DNYc_000137	dinosaurs bellowing
+qW9b8qu_KrU_000180	lions growling
+qXFgtkhWLgM_000134	child singing
+q_ZMlkVS740_000222	playing congas
+qbmNcYH52eo_000516	striking pool
+qdl6t1bDb-8_000400	eating with cutlery
+qgv0riPveBQ_000030	bird chirping, tweeting
+qiw2I1oQIVQ_000057	playing snare drum
+qjBkiP7mBNI_000597	ripping paper
+qmjK_Wi0IK8_000080	people cheering
+qoPAdSFZ4f0_000370	chopping wood
+qpjOCvQEHdo_000080	people cheering
+qrNCI310T9Y_000018	chicken clucking
+qsj_OgZZDvQ_000080	tap dancing
+qsrNWdcjwwY_000320	female speech, woman speaking
+quF2HA3u2JY_000101	cupboard opening or closing
+quZSWDeSywg_000040	toilet flushing
+qv51EqZA8eE_000291	train horning
+qxeCxC_zpvU_000202	playing french horn
+r24KMnV5Rrk_000030	people running
+r42dJt0hxro_000010	gibbon howling
+r47N9mdOeXc_000030	playing violin, fiddle
+r4Zm5lEsI-M_000110	vehicle horn, car horn, honking
+r7e4wJy4NP8_000090	motorboat, speedboat acceleration
+r96LZqBtlwg_000050	dog whimpering
+r9uN-AltjDQ_000130	lawn mowing
+rAXnOxWHaLs_000030	playing french horn
+rAth9ueRqM4_000040	whale calling
+rD4zq3CvJSo_000130	people slapping
+rEdr-j9oAN0_000074	playing french horn
+rFA1GBcIGN4_000067	playing ukulele
+rFgrOflwKPg_000290	playing trombone
+rLuNw3Cm7rs_000024	lighting firecrackers
+rMDnGZU7jzE_000001	dog baying
+rQthEYYXM-k_000030	people sniggering
+rRP810El--s_000958	fire truck siren
+rSHvW5dGanw_000150	fireworks banging
+rSWPVWkAbec_000000	bee, wasp, etc. buzzing
+rTNSzUXd3wk_000180	playing double bass
+rVnkDOvLWm8_000180	cap gun shooting
+raz3OUu768k_000068	playing clarinet
+rfqqBv3eriU_000160	stream burbling
+rgdMDo5TBic_000355	playing squash
+rn381TUMxyE_000298	arc welding
+rs2FL8HJfGE_000030	people sniggering
+rwVhTlLcBO0_000099	playing erhu
+rx2lqMvj2Wo_000052	squishing water
+rz9PZZA04z8_000183	playing badminton
+s2QrQdxzLwQ_000074	playing glockenspiel
+s8zSSYQM0Tc_000127	footsteps on snow
+s9gzcUg_nlM_000030	playing drum kit
+sFTyeq295xU_000041	people humming
+sIHApNhq2Ik_000002	bird squawking
+sLEEurjCsAY_000051	typing on typewriter
+sLOjC8EWrHA_000070	driving buses
+sOg4MNTWx_0_000000	skateboarding
+sUHlRRyS2YM_000009	pigeon, dove cooing
+sUs8O9toO4M_000311	dinosaurs bellowing
+sXDJvBEzqjs_000000	dog bow-wow
+sYy0lPjLEXQ_000100	playing cymbal
+s_FLZ-ekB2A_000088	telephone bell ringing
+sa6B5XyFYIg_000040	playing bagpipes
+scm7r0uBepU_000467	mouse clicking
+smBHJiEPCRI_000030	duck quacking
+snbtH1P3MVA_000119	playing timbales
+snyzyJlTBbg_000003	dog baying
+surXSGAnpM0_000000	playing harmonica
+sxiVIGK5AEc_000010	people crowd
+syysO74ja30_000007	playing gong
+szQ-4VQQQsI_000020	railroad car, train wagon
+t0XoS_8YVP4_000728	magpie calling
+t2xJjZp1D1E_000030	dog growling
+t3YfjKEmei4_000080	race car, auto racing
+t3u3ykowlvs_000030	raining
+tD9rMw8YPBI_000030	child speech, kid speaking
+tDayTL0ivzU_000014	playing timbales
+tJChPvDD-hI_000035	parrot talking
+tLFNgY5NBMk_000001	playing bassoon
+tRw0KL6PMFU_000060	skateboarding
+tTePTFQV52M_000030	pig oinking
+tV0sIqEryIY_000037	wind chime
+tWDG6UsiG3s_000090	people babbling
+tYBxgXg8yxw_000046	woodpecker pecking tree
+tYzH5rkbuBQ_000000	frog croaking
+tm9rnG0455k_000010	skidding
+tuqcWxh_mdc_000012	baby crying
+twWBQjLyuxw_000014	bull bellowing
+u1nAQ6GgJ7Y_000154	playing volleyball
+u6AV24u4OMQ_000052	rope skipping
+u6c5tvrkqVA_000187	playing timbales
+u88CrTGAqbo_000000	lawn mowing
+uEPueBOV06U_000109	yodelling
+uGQ0TW02gBo_000004	frog croaking
+uI5eona1hc4_000000	elk bugling
+uIHnphQWVRA_000169	opening or closing drawers
+uIg0I7pAjvM_000030	race car, auto racing
+uJSDmIF4dhE_000260	driving buses
+uK0jcVxT-Pg_000030	driving buses
+uLm5oUt3XG4_000031	playing tabla
+uSmduC6gJxg_000050	rowboat, canoe, kayak rowing
+uWdgdlJqI2Y_000019	basketball bounce
+uWq8Q_cIEwE_000086	playing ukulele
+uZghS49MC1k_000180	skidding
+u_85N9h_cGs_000050	car passing by
+udVSYrFacsc_000072	playing cornet
+ugUyp_keJO4_000022	mouse clicking
+uiPC88KDlW4_000022	engine accelerating, revving, vroom
+unF6DdqG4l8_000050	people whistling
+upZ0sKmaZrI_000167	playing lacrosse
+uvUEfRqpEQU_000145	singing choir
+uyNyWLJIci8_000000	fire truck siren
+v5OdaMw5hhk_000030	playing snare drum
+vADdI9YTMRs_000243	playing timbales
+vJk_Jzr2YIs_000080	playing hammond organ
+vLLiaCDHSPY_000010	dog barking
+vUORRJqXp7A_000036	playing table tennis
+vXupVqDfK34_000116	cricket chirping
+v_cxwPhwaBQ_000000	people farting
+varD0b9CTgs_000020	people belly laughing
+vcwXIa-QB8A_000025	sailing
+vdXavSaj8-M_000070	playing accordion
+vgIgTWqXtms_000023	child singing
+vhqkCDgsuh4_000255	people booing
+vkA-v4DSriM_000229	playing tabla
+vktUwc0Cs7w_000170	playing clarinet
+vpAGr_NrM_w_000050	fireworks banging
+vzoQdjPITKw_000030	pigeon, dove cooing
+w-9xoB74oF0_000004	opening or closing car electric windows
+w-JaJ11OqQY_000345	people slurping
+w3kMt-zQ9t4_000215	playing table tennis
+w5T582MCzlY_000011	running electric fan
+w5vaBVSxgKg_000030	lawn mowing
+w8puug1pEUA_000170	stream burbling
+w9K_AmeWhlo_000071	fire crackling
+wAnqT37UgYY_000034	dog growling
+wEbJ-9cmSaE_000003	playing cornet
+wHdgExbL6dA_000034	playing badminton
+wOYLWY6UCu8_000262	playing ukulele
+wP-96GP6bsU_000000	vehicle horn, car horn, honking
+wT6-Isia2PQ_000149	child singing
+wTQ-1cd8owI_000181	dog bow-wow
+wUNpHu61l7Q_000190	male singing
+wVJ-S2zYxug_000040	playing drum kit
+wX4Ya3D20H8_000039	scuba diving
+wXsrff4No40_000237	playing hockey
+wYZc2-3ViXs_000155	civil defense siren
+wZj294W4RVU_000094	fire crackling
+w_yGhgrow38_000091	eletric blender running
+wdk-RmsGdyw_000310	driving buses
+wdlfOAR03iY_000000	playing glockenspiel
+we-ONoZIkWE_000018	dog howling
+wegIxELjtz4_000334	people eating noodle
+whIS2UodgLI_000002	gibbon howling
+wkwjx0oMAjw_000021	beat boxing
+wnW4qgQQg3g_000050	playing cello
+wrFyu2T1XOo_000000	hail
+wsHPe19Y9Nc_000081	electric shaver, electric razor shaving
+wtyuiWygNTc_000000	zebra braying
+wuAcPWyHMXo_000008	lions roaring
+wwQPX3zjV4s_000028	elk bugling
+x0_AiAhfeV0_000068	eagle screaming
+x0bbH2Tao_0_000000	dog howling
+x1Rt2zN-oXo_000000	dog growling
+x1bXQS9dUAc_000140	playing violin, fiddle
+x2uCcPNM6Nw_000030	pigeon, dove cooing
+x3cLaiaaF0M_000032	skiing
+x68R1rmvKgc_000060	female singing
+x6d8ytnWNDI_000045	barn swallow calling
+x8yymm3DtVA_000022	playing cello
+xK1vy_6H2VM_000010	scuba diving
+xMa1vAUhTfM_000429	ice cream truck, ice cream van
+xN_CePbfjVg_000004	playing bass drum
+xPIhTw0fbzI_000010	train horning
+xQaYumd1O48_000004	lions growling
+xS4brO1qu0g_000591	playing hockey
+xUCKcoE3K6Q_000313	lip smacking
+xVDGIF1pFvQ_000030	driving buses
+xVEXWvj0iWo_000060	rowboat, canoe, kayak rowing
+xWBMt4fI95M_000063	scuba diving
+xWgd4OMcKbs_000263	people nose blowing
+xY9mlbn2IhY_000000	people burping
+xYAHwbhWEgM_000030	playing violin, fiddle
+xbNNxwGRG20_000062	cattle, bovinae cowbell
+xdUbCcEbipM_000290	people crowd
+xeS25F6uHic_000162	airplane flyby
+xetF74UUCGk_000001	ice cream truck, ice cream van
+xf0cheS5wFM_000090	playing piano
+xfT0HF1Pbxk_000003	playing sitar
+xg_3Uas3z40_000240	skateboarding
+xibFeibkfWM_000036	alligators, crocodiles hissing
+xj0Xi47RC88_000200	lawn mowing
+xkUzsvSImy4_000306	people eating crisps
+xm0N3HXnSWc_000361	rope skipping
+xoViga6dJa4_000141	playing steelpan
+xocKilOzrb4_000065	reversing beeps
+xq5kMmAFYx8_000030	playing double bass
+xqv96EPg7so_000200	railroad car, train wagon
+xtvQjd6cwC4_000040	playing bagpipes
+y3TRiYwDbHo_000287	playing oboe
+y6wsRU2aNx4_000040	railroad car, train wagon
+y95ml0IYGr4_000440	chainsawing trees
+yA_63YfQ034_000022	dog growling
+yBwMu2NueR0_000284	rapping
+yE_SP127xy8_000010	people crowd
+yEfhYsMd1yc_000006	playing double bass
+yH3PJfYi_gs_000109	car engine starting
+yJGtoH8INnA_000084	tapping guitar
+yJN5_1tfqXo_000075	magpie calling
+yMMmjb3BRi0_000030	dog bow-wow
+yOhdod2Kg40_000210	playing bassoon
+yPJiPWkeT3U_000254	playing gong
+yPUYU6t3rwo_000370	bee, wasp, etc. buzzing
+yQzzdP-4iBU_000002	planing timber
+yUL9UefoANU_000128	tractor digging
+yVzIaZzLH38_000130	bee, wasp, etc. buzzing
+yYPNrg-s-NI_000060	child singing
+ybnXdQfSNZs_000001	police radio chatter
+ycN30BUfzeo_000070	playing clarinet
+ygOHZ_55jME_000174	electric shaver, electric razor shaving
+yjyZgzYuuSQ_000089	cat purring
+yo5I2MTqv9E_000030	playing marimba, xylophone
+ywD_am3uZh8_000020	splashing water
+ywYLMe6y-S0_000040	playing piano
+z9CCSNKepA8_000537	striking pool
+z9crgUIWcmA_000000	dog barking
+zBgR_gj8NGg_000083	striking pool
+zGbJAz-3Ao8_000070	playing banjo
+zGn9k6j8kVo_000049	rope skipping
+zILE3kr9nIU_000030	mouse pattering
+zJPgE79wkE4_000000	playing tennis
+zMKJFnBr1Gw_000013	reversing beeps
+zPlyG_ryFpg_000006	sliding door
+zRU8A0m9Op8_000145	driving snowmobile
+zVCqTRlc7NU_000020	fire truck siren
+zYPY3Fh1Xjo_000000	skidding
+zcZ0WVQ8t8s_000210	splashing water
+zhPLdAMVAuo_000257	church bell ringing
+zl6hP51zURM_000075	playing oboe
+zlt2EGxum58_000174	bouncing on trampoline
+zmSPCArJHB0_000190	bird squawking
+zpqGedo-jm4_000043	cell phone buzzing
+zrKMC4fAKp0_000202	playing cello
+zsnU7rt_Qq0_000005	baby laughter
+zw7dTh-Lx3o_000074	canary calling
+zzP5qr-ZxHY_000199	people marching
+zzftU8z4aOI_000230	skateboarding

sets/vgg-val.tsv

@@ -0,0 +1,2049 @@
+id	label
+--96EN9NUQM_000242	alarm clock ringing
+-2toZf00LvI_000012	bowling impact
+-8OE7Vydkl4_000221	bowling impact
+-AEZuuoyJug_000030	playing violin, fiddle
+-CUgrFw8TEI_000045	dog whimpering
+-CexapzRAPQ_000051	ferret dooking
+-DHGwygUsQc_000030	skateboarding
+-G-o-Y4WuaU_000139	playing harmonica
+-G_2v0L4U_s_000078	playing tennis
+-HIPq7T3eFI_000011	driving motorcycle
+-I8C3cRr5TY_000030	female singing
+-K232jBK8VQ_000030	car passing by
+-L_RH-nw11I_000025	vacuum cleaner cleaning floors
+-MfBpxtGQmE_000020	ambulance siren
+-NYZDjBz60I_000085	child singing
+-QWcNg6FCgE_000022	playing bass guitar
+-T06kz4MI20_000030	female singing
+-UJJsEdgqMQ_000011	horse clip-clop
+-VyLmfnIc5Q_000162	driving snowmobile
+-W3y3qz3yp8_000256	people eating crisps
+-WBvJuF2UOk_000030	playing acoustic guitar
+-Yep0TGjWmc_000140	subway, metro, underground
+-YrSxLTPdcA_000004	underwater bubbling
+-YwZOeyAQC8_000002	baby laughter
+-Zd-ZSnZ3so_000159	playing banjo
+-_mqzXgg5eQ_000046	ripping paper
+-c7lpU-_-V8_000030	motorboat, speedboat acceleration
+-c96lccP5nc_000200	skidding
+-eqkzAKGBZg_000030	playing drum kit
+-geN4ECfl0Q_000030	playing bass guitar
+-ibjrtJo9rY_000030	duck quacking
+-nEg1olBLcw_000030	male singing
+-s2G3Kto0Gw_000030	typing on computer keyboard
+-s6dPB8fyQQ_000030	playing electric guitar
+-tGOjLdrF6g_000087	playing squash
+-v12qcLw5u0_000187	machine gun shooting
+-vC3oqlxf4I_000010	slot machine
+-vY141CdTc4_000030	playing bass guitar
+-vmyjjovGXM_000116	cattle, bovinae cowbell
+-vra5dNsP4w_000080	playing bass guitar
+-w7WfMgSBD4_000047	lighting firecrackers
+-wJ_UfBsiR0_000280	playing accordion
+-xzWsDpVEiE_000060	child speech, kid speaking
+-yby37u00N4_000030	playing violin, fiddle
+-zHk3s6BkpA_000030	chainsawing trees
+-zZR-ps0nJY_000137	hail
+0-fd-lvizrY_000024	yodelling
+00eb49xIULo_000030	female speech, woman speaking
+01LPFe-13Aw_000030	playing electric guitar
+01W8XIz7KDM_000007	donkey, ass braying
+02t6zmS4RAk_000102	playing didgeridoo
+038-gneOcks_000309	people eating crisps
+04m_7jCGHko_000030	wind noise
+04sf3v7xOzo_000005	cat meowing
+055LCXe4pR8_000012	people whistling
+09qDi4Auiyo_000030	playing electric guitar
+0Ca2CTVwOxs_000019	cuckoo bird calling
+0CvAFdtyVlo_000023	underwater bubbling
+0G0mSrzOZ2M_000400	driving buses
+0IvNbabusiY_000030	playing flute
+0JPlNHX2HQ8_000049	playing accordion
+0Lro_JzyUX0_000030	male speech, man speaking
+0McmdH07r7w_000050	playing flute
+0OHWW60khJ4_000030	playing bass guitar
+0PZQL-Msz0s_000030	horse clip-clop
+0RFEHUrGOP0_000170	playing acoustic guitar
+0SsaL_YNyjY_000030	waterfall burbling
+0T4gZQwzyKY_000030	people crowd
+0U_Q9JTATCk_000044	owl hooting
+0WIzNXqWrZk_000204	playing hockey
+0XzJKHmoN6w_000019	duck quacking
+0cMnDz8SSwQ_000014	disc scratching
+0dkhsBmUZSY_000030	people cheering
+0fQJ9nShofs_000093	dinosaurs bellowing
+0hCiGC4c97g_000033	crow cawing
+0hWyQpwHNDU_000030	motorboat, speedboat acceleration
+0iVM2GY3R_c_000030	ambulance siren
+0kar1O-1Ckk_000114	playing french horn
+0m3kYCMUuCk_000000	cattle, bovinae cowbell
+0sY8RR7V_q4_000220	female singing
+0tJevlglhe4_000010	railroad car, train wagon
+0uHGQmkKMr0_000223	people marching
+0yAboI4QC6k_000109	hail
+1-2zGkXe070_000098	rope skipping
+10fjkn2eM_M_000050	slot machine
+12tsmtyIALQ_000009	cat meowing
+13LB6yibhQ8_000009	scuba diving
+1CIxzqH4zzM_000040	ice cracking
+1Fp6zPswdjI_000233	tapping guitar
+1JMgZaCb9WM_000204	playing steelpan
+1MCjHVRBDTk_000055	slot machine
+1MLUEfkJDSw_000001	beat boxing
+1MPwoS-R83A_000030	cat meowing
+1Mx2iDMsZj8_000018	playing french horn
+1NTsWn1Gir4_000103	playing snare drum
+1NvpdqTAf3U_000030	skidding
+1NwFHr4VHS0_000090	playing clarinet
+1RB0gsxkPBo_000020	lions growling
+1RSK3TFru0g_000000	sailing
+1T1PLOWu65c_000250	skiing
+1TARmg2FYJQ_000010	people whistling
+1V65GzuCqaw_000030	bird chirping, tweeting
+1Vn7SftZxS4_000030	rowboat, canoe, kayak rowing
+1WaTnza9cn0_000160	playing violin, fiddle
+1YGJDa3aCGo_000289	fire truck siren
+1_CC87jIhXk_000382	swimming
+1acVFuCvOJg_000512	canary calling
+1bBdyTowO-M_000041	parrot talking
+1dO7fONpkvE_000000	people farting
+1eYmBacWt3k_000027	civil defense siren
+1f9IgOjZjn4_000037	rapping
+1gVugA2dsi4_000332	dinosaurs bellowing
+1gXDaVse3SQ_000387	planing timber
+1inu4aoQFKM_000164	planing timber
+1kdGia7plHk_000030	playing electric guitar
+1nDhQKLRJbg_000030	playing marimba, xylophone
+1p8YDM6gG6Y_000014	dog howling
+1t63KIS6F4I_000070	people sobbing
+1x7wVFMW4dk_000030	playing acoustic guitar
+1zWc46eeWLU_000167	playing sitar
+2-Ipq91ns0k_000036	playing bass drum
+21OWtKgJlIE_000270	canary calling
+23ky1UGWeKg_000190	playing bass guitar
+26KmPM2YkmQ_000004	ambulance siren
+2A5eS9kMm-U_000018	owl hooting
+2CebaASg1m4_000030	male singing
+2EeOU7PgSck_000030	female singing
+2F2NSNlc6dQ_000030	male singing
+2FNZwK-4sUA_000030	female speech, woman speaking
+2Jt4iqSqNTg_000012	bird chirping, tweeting
+2LBEllUpWiA_000000	volcano explosion
+2MDjnJzuUaU_000015	skidding
+2NIaPAfScHM_000030	motorboat, speedboat acceleration
+2NjwuyNgNoE_000050	playing hammond organ
+2P7ZXBq5r04_000274	playing cornet
+2RPPKMapBWY_000036	ice cream truck, ice cream van
+2SlVaOyh69w_000219	cattle mooing
+2Sto24aXwao_000097	baltimore oriole calling
+2VdOQylRl08_000002	playing lacrosse
+2YIZLARm8sI_000201	parrot talking
+2d43OFDr5aI_000001	frog croaking
+2ehs70MWQTs_000050	waterfall burbling
+2fCC4BkdMT0_000106	basketball bounce
+2fn6GFSwTEw_000096	cap gun shooting
+2iwPgYGH_Ew_000400	railroad car, train wagon
+2jy1b77hxXc_000136	playing bass guitar
+2lALVOKDQNM_000059	dog howling
+2myGIZCgZ2g_000018	tractor digging
+2rSFLrwcvcY_000020	pheasant crowing
+2szJ9STQPUk_000030	male singing
+2w6jRF1Ekhs_000130	playing sitar
+2xlWTgqPUOA_000004	beat boxing
+2yeuzECPVUI_000033	playing badminton
+2zev5MpJKPc_000039	chicken clucking
+33NCPZjFuLE_000056	playing oboe
+35MtyyqqQyw_000030	playing acoustic guitar
+35c4EPiZ8JM_000030	horse clip-clop
+35iGp2g_U6A_000000	church bell ringing
+37Tl9YROdbA_000077	playing trombone
+3EcAiTE0JyE_000052	playing theremin
+3JyLYEjo4ok_000000	people giggling
+3LfWg5Be60Q_000163	people burping
+3MOG_CAcWkw_000142	playing badminton
+3NcIWxDdTW0_000050	dog growling
+3O8InHTYtk0_000020	male singing
+3Okx0T5vpFc_000192	airplane flyby
+3OxJ7KtIb2A_000100	playing saxophone
+3QHNbJ_XATY_000036	civil defense siren
+3S2-TODd__k_000090	train horning
+3VK-nOg0-RQ_000046	pheasant crowing
+3VSUuTABb3U_000074	wind chime
+3WUTEMZv3EI_000046	slot machine
+3YuBzhAU_Yc_000000	race car, auto racing
+3cMrwXYnjd4_000026	air horn
+3d5tPNd4Olk_000020	wind noise
+3dBQbWPOjjI_000030	playing acoustic guitar
+3djcJkGeJK8_000293	running electric fan
+3e8ECt9wF5Y_000015	playing saxophone
+3en9IzSPnNU_000027	driving snowmobile
+3gTMehPiQ9s_000150	playing harpsichord
+3kXROE2wcRA_000069	bowling impact
+3p9aVzs8aYA_000030	female singing
+3u3iunnXAOs_000432	playing hammond organ
+3wboiuBfavA_000172	people nose blowing
+3yolbg1tH9U_000030	male singing
+4-_AWdbZnzE_000005	playing trombone
+42Iss6TfcpQ_000742	lip smacking
+433xsSMNLf4_000070	playing electronic organ
+43ijm8y4z2o_000030	horse clip-clop
+44UMQ5ZFuuY_000030	engine accelerating, revving, vroom
+457yRHL0f2E_000030	female singing
+45iXudFVQ4E_000000	subway, metro, underground
+46LjKw-7mU0_000030	male singing
+47QYxqXGZ3w_000244	people shuffling
+47SP2azKv8Q_000030	playing electric guitar
+47YlecLyyK0_000030	playing acoustic guitar
+47y5k6vaUxE_000089	francolin calling
+49gi-iYJ1F0_000107	tap dancing
+4CLnZSI8aPs_000092	hair dryer drying
+4DcOTOS_LE0_000454	sliding door
+4DzuWR9ekko_000000	playing bugle
+4E6mA8Y2Be0_000060	using sewing machines
+4FOFcRJR9go_000084	playing glockenspiel
+4H29LCZTMBs_000050	using sewing machines
+4K345_DRFRk_000056	playing volleyball
+4Ofe_ManxZc_000047	playing french horn
+4OxCr981HvY_000016	ice cracking
+4SlcVylJxxk_000297	arc welding
+4WGMFP00rIg_000030	playing acoustic guitar
+4YnMOFstVnk_000066	parrot talking
+4_QGupz8UNA_000189	hail
+4aFirNGu_P8_000381	planing timber
+4dhyddSUAWg_000175	police radio chatter
+4dkU-c4g1VM_000111	dog barking
+4h9o2iL6nps_000050	child speech, kid speaking
+4hU6jqQQUto_000009	playing harpsichord
+4iBqpFUnPoA_000170	fireworks banging
+4j7GbxZQjB8_000024	car engine knocking
+4jHrFbnaVRc_000294	firing muskets
+4kvqtJEFqjw_000190	playing bagpipes
+4ldID97D-oU_000020	people coughing
+4n657Imjmjo_000015	sheep bleating
+4o2IRyXi-aY_000667	playing harpsichord
+4rehS_cPodk_000020	female speech, woman speaking
+4t_Qz9RyUm8_000006	alarm clock ringing
+4yUvIrchOzQ_000280	playing saxophone
+4zf3qRiZ3Ok_000030	child singing
+4zsLfdNLUD4_000033	cat hissing
+50OgBbJZUUc_000064	typing on typewriter
+50jxPCLUFdU_000002	cricket chirping
+53ohFLBl0iE_000052	alarm clock ringing
+542uea0zO1I_000036	sea lion barking
+54XBPEFJQc4_000076	playing djembe
+574NjiOGi5s_000030	female singing
+58KzLvK1OYs_000144	dog growling
+5901zjV6oAo_000006	swimming
+5AFKEd8nSpg_000050	people sniggering
+5CtoZvJaGAM_000096	woodpecker pecking tree
+5D201VjroT0_000229	sharpen knife
+5D2E7s9bEf0_000010	basketball bounce
+5EPnuy_sKHI_000010	singing bowl
+5IZv217s4_E_000049	playing badminton
+5KRxqVykvvI_000030	printer printing
+5S3QDnRCnOQ_000003	tapping guitar
+5Sv97J7mksY_000030	playing electric guitar
+5UqwkZ1XK18_000050	helicopter
+5VyCTHzLVdU_000011	playing bongo
+5WVhslWt1wU_000030	female singing
+5Wb1zMq_DiU_000020	fireworks banging
+5XK1Vgiwllc_000073	playing mandolin
+5X_B2L1-4Bc_000030	playing electric guitar
+5briopN06L8_000000	playing piano
+5eHlhJ-ZOpg_000030	playing hammond organ
+5fZn_7LbKSI_000020	people burping
+5hi4T4Gp6v4_000002	air horn
+5hjKe0FWq9E_000002	horse neighing
+5iEbFJkG6Xg_000557	bird squawking
+5jQLK4Z1EH4_000020	wind noise
+5jt7lR8WY3g_000172	playing castanets
+5lV59hZgwRM_000009	scuba diving
+5mBCF05DV5s_000280	church bell ringing
+5mJ7_05tlhs_000005	crow cawing
+5nscL4EBrXA_000030	male singing
+5r1zW38AWvs_000057	wind chime
+5rP9Z4jEq6s_000024	cap gun shooting
+5xJdFysNSf4_000110	race car, auto racing
+5xefixXFNwk_000020	playing bass guitar
+64eXDlUgPoA_000079	lighting firecrackers
+64lQIoDGX6o_000040	playing marimba, xylophone
+64ollREPrUw_000132	raining
+64zPbHPyiwE_000030	male speech, man speaking
+659mhmSPXWA_000276	bee, wasp, etc. buzzing
+65u3pwOEcBg_000002	frog croaking
+67hDkeQalow_000030	motorboat, speedboat acceleration
+68mXCuRvQkw_000045	people burping
+6ARTjahUaYY_000030	playing electric guitar
+6BQgJ0tvUkc_000162	baby babbling
+6CYhRsU4F34_000000	people whistling
+6EcmHiscsOc_000287	lighting firecrackers
+6GsamqJ5tFU_000075	airplane flyby
+6IMlkVOKxJw_000032	cap gun shooting
+6IQkdce9a7Q_000184	slot machine
+6KO3eMyEeOg_000000	race car, auto racing
+6LB-qRj_zW4_000030	horse clip-clop
+6LKFDTu9vRQ_000018	playing french horn
+6NxeHScEnJE_000000	dog bow-wow
+6OhTwJrVxXs_000028	playing timbales
+6RGa6DvWpt0_000035	people marching
+6UcuQgsHFCA_000142	playing french horn
+6Y6CvX7EP68_000030	singing choir
+6ZbVXBeNsX8_000125	playing didgeridoo
+6aYfccsgIjk_000094	baby crying
+6gTR_Avjz6g_000170	playing cymbal
+6j2g_OZnW74_000189	missile launch
+6mE_v9a5dbM_000030	male singing
+6o0mZVMfKss_000140	people clapping
+6of3tx7IOik_000030	wind noise
+6shIFnN-LsY_000141	playing flute
+6v53uAVpXC4_000071	people babbling
+6wpifZcwOJU_000023	underwater bubbling
+6xAClSJ21qA_000491	rapping
+6xgTrufXcCM_000126	wood thrush calling
+6yBZH5cV7GE_000030	playing electric guitar
+6z_pfZ6Rvfs_000023	playing table tennis
+7-7r-FRwp_w_000041	playing glockenspiel
+72d2TsdeSg8_000000	tap dancing
+75FLwnGZJTc_000125	playing oboe
+75m0cvRBGY0_000030	vehicle horn, car horn, honking
+76F-K-7HUXE_000010	lions roaring
+77rq4-p4vV8_000030	wind noise
+78hdsP0edMg_000030	railroad car, train wagon
+7Ck8cfF2rl0_000200	otter growling
+7ELF2dbWe5w_000010	female singing
+7I_wdG-eOc0_000106	playing hammond organ
+7JT43yyNGkk_000003	black capped chickadee calling
+7JX-Bx0BETQ_000205	rapping
+7LMkG7uISis_000102	playing gong
+7MuetSj86N0_000490	bird squawking
+7NyPcaVKao4_000025	dog growling
+7Odi8SKArQI_000030	playing saxophone
+7P-1-qzwyYA_000055	magpie calling
+7Qr1ncg86N4_000007	lions roaring
+7TMOCRG4EBA_000030	female singing
+7U5V5Teqo8Q_000000	dog barking
+7V6NAsZ86xw_000000	beat boxing
+7VT8p9Er3n8_000020	mynah bird singing
+7Y3u8Aj8UV4_000010	driving motorcycle
+7YGUQYRwnHs_000019	horse neighing
+7YTsyqVSEeI_000006	child singing
+7b__KH3VA_o_000035	people booing
+7caL9c6N1zc_000122	child singing
+7gdSJ30FfNU_000490	people hiccup
+7h7_U2q-VwY_000276	dog baying
+7hdXzJpOXiY_000018	police car (siren)
+7iT77hG1X18_000063	playing erhu
+7kIhqlZok8c_000074	running electric fan
+7kIhqlZok8c_000241	running electric fan
+7lz-THXCwi8_000030	male speech, man speaking
+7ogdSWU90s4_000100	opening or closing drawers
+7pc3c5ZGbwo_000030	ocean burbling
+7rLRSpEqgZk_000253	playing sitar
+7tsuYUeV7_k_000191	airplane flyby
+7vF2Qq0Pg6w_000024	ice cream truck, ice cream van
+7xZxYm27FdA_000020	toilet flushing
+7xaNqQ8FAwI_000100	mynah bird singing
+7yBOHsPAJgw_000040	vacuum cleaner cleaning floors
+7yXROxIZfeo_000053	raining
+80KT6bYCFkg_000077	playing tuning fork
+81ACguOEqoM_000042	electric shaver, electric razor shaving
+82ic2Xisrqg_000030	car engine knocking
+83mmLOdwZlA_000081	air conditioning noise
+85Nd7APr5Os_000028	ice cracking
+85Nd7APr5Os_000052	ice cracking
+87-ZrpDyRHE_000238	cat hissing
+88oLbuKd7Rg_000030	car passing by
+8906Y6i-h10_000102	playing cymbal
+89NzFtLSRSo_000030	engine accelerating, revving, vroom
+8DDro-N5-54_000029	swimming
+8GTkmen1bBg_000110	playing piano
+8IdUE6nhR3E_000030	playing violin, fiddle
+8N0GxZtk9wE_000051	playing didgeridoo
+8NMHjXutgVs_000333	electric shaver, electric razor shaving
+8Rh7NvJDexA_000068	tapping guitar
+8VEqGk0W4xY_000192	playing darts
+8XH7xIWWC6c_000090	people cheering
+8Y9VKxl-1gE_000063	rapping
+8ZWKl-_qHM0_000010	driving buses
+8_xdWIziFpI_000030	baby crying
+8b2ASj5nmos_000251	playing darts
+8c9PJLozdtA_000020	playing bass drum
+8jkr7bOR8ck_000146	playing table tennis
+8lIh0qRN7PE_000220	cattle mooing
+8m7VIFtS4gc_000000	typing on typewriter
+8n76LfbY3qo_000232	chainsawing trees
+8ngu3TPmfZQ_000110	playing drum kit
+8ugZkKeLL7Y_000030	male speech, man speaking
+8vE2wod7rhE_000030	horse neighing
+8ytjUazIdno_000023	playing glockenspiel
+9-N8v-cC0Tg_000002	air horn
+9-xW047dMpk_000125	missile launch
+913ItBzDHLQ_000124	playing synthesizer
+91kWVMnyKxA_000019	bird squawking
+92G0bdxj5ck_000091	mouse pattering
+93a7wS41kLc_000060	splashing water
+93rlsDDmFYo_000110	playing cymbal
+95UKs8K92C4_000110	playing timpani
+96wdXcwIbgk_001238	playing bongo
+97svDuqFctI_000139	playing steel guitar, slide guitar
+98Nc3x8U1JI_000187	playing badminton
+993A2y5lv-s_000030	bird chirping, tweeting
+99WZAe6QKUc_000030	people whispering
+99cGCS0ko2Q_000120	playing saxophone
+99ylFYthGcI_000004	donkey, ass braying
+9A8hgZdD__g_000030	horse clip-clop
+9CFR1VdlIMc_000142	rope skipping
+9CmEsDtIz_Q_000060	playing accordion
+9D9sfe1eaK8_000000	frog croaking
+9HtYErt1moA_000100	playing drum kit
+9IwjfATt51Y_000030	playing french horn
+9JwaE3BmICE_000061	female speech, woman speaking
+9LFGIpAO3NE_000161	tapping guitar
+9LY2BJ2fqts_000090	people gargling
+9Q1RM-pY2yY_000180	playing bongo
+9UvWyax1fEU_000000	people booing
+9Y3ausHODlk_000557	playing electronic organ
+9ZCgk2e7wZM_000269	woodpecker pecking tree
+9ZE18L9NN1Y_000105	striking pool
+9af_fvuAY8E_000167	barn swallow calling
+9exZEq85L1k_000260	playing trombone
+9fvJeyH-4II_000100	fireworks banging
+9gJ4NQYcakk_000030	male speech, man speaking
+9k0OwVahe5Y_000066	playing cymbal
+9pBp5wd9rpw_000043	firing cannon
+9s6jvP1V56w_000080	people crowd
+9t7YT0OKpaM_000130	playing cello
+9tyf9HGsIe4_000000	people finger snapping
+9zriIjvwqJw_000480	people burping
+A08MfFxzmxo_000030	playing accordion
+A0tXM5fSFrw_000062	alligators, crocodiles hissing
+A1-T0wdI8Nw_000070	sloshing water
+A1vf6We9a_Q_000290	mouse pattering
+A2GEU2r5KnQ_000030	playing acoustic guitar
+A551qSirV68_000298	alligators, crocodiles hissing
+A55rHYLkwQk_000000	singing bowl
+A95vqV9oM6g_000081	people marching
+A9pIMNKQWCk_000005	footsteps on snow
+AApTo3l6NfA_000030	train horning
+AFmF56HVvVg_000151	cat purring
+AI3om1uyCH0_000009	fire truck siren
+AJhEl41TC5s_000443	lathe spinning
+AM8-hH1Oahw_000510	driving buses
+AOPgsB4hsH8_000206	electric grinder grinding
+ARFBC4LeCFY_000019	chicken clucking
+ARrb06s5a0Y_000082	donkey, ass braying
+AUufm-TAVg8_000101	playing french horn
+AW-JhveJXFw_000030	typing on typewriter
+AXDomj6KnkE_000141	playing tabla
+AZgG_6NE8j4_000240	parrot talking
+Ac0OxSV8Nqk_000030	female speech, woman speaking
+ActIkLSW20Y_000350	railroad car, train wagon
+AefLmdFYR6k_000136	playing tambourine
+AfrcYQw5mXw_000010	telephone bell ringing
+Agl-AQmIYBE_000030	rowboat, canoe, kayak rowing
+AhUYTb14QZU_000020	chimpanzee pant-hooting
+AiriN8WOgiI_000123	playing bass drum
+AjD1BiY0o8E_000001	pheasant crowing
+AlTNj6IWey4_000112	airplane flyby
+AlebU-Vdy18_000022	playing theremin
+ApMojxDfms0_000273	magpie calling
+AteFCZfJfLY_000011	vehicle horn, car horn, honking
+AvguIvLb0GY_000027	electric shaver, electric razor shaving
+Aw5BwrqdmHc_000010	canary calling
+Aw9arRIoBR4_000030	sliding door
+Aygl9-ur8NU_000001	gibbon howling
+Az1M0iLYjIg_000030	ice cream truck, ice cream van
+B1ax5dX6XrU_000215	wood thrush calling
+B7cF_In3_-c_000030	horse neighing
+B7zgWPjx8hg_000026	wind noise
+B9Mk5n5Zwjg_000240	driving buses
+BBumD37-y80_000110	train horning
+BC4LglYv70Q_000108	playing oboe
+BH8QYqAvO2k_000020	playing vibraphone
+BJ31LCL3Dy4_000100	crow cawing
+BLWCHd07ATw_000080	playing electronic organ
+BM1fw080pSs_000030	female speech, woman speaking
+BM4YyahEm8Q_000078	spraying water
+BO1K4wXy2CI_000299	mosquito buzzing
+BPD7Qj1U_Bo_000131	playing theremin
+BPxW7nP4loQ_000003	eagle screaming
+BTdIM1mncyA_000030	female speech, woman speaking
+BWw6dgq07Qo_000053	playing clarinet
+BYJ2UHIHCLU_000009	hail
+BbUBFko93XE_000031	people sneezing
+BbfDej2cM2I_000001	volcano explosion
+BdIWeKYKIzk_000000	train horning
+BfyYYuE12dw_000006	goose honking
+BkXyLmdb8Yw_000290	playing hammond organ
+BkwStRX3xE0_000010	fireworks banging
+BmsTQHrCwB8_000215	heart sounds, heartbeat
+BnRtoIC87Po_000030	female speech, woman speaking
+BniijKHywXM_000103	playing tambourine
+Bo271H1XM40_000127	arc welding
+BpV7n-YUtos_000248	rope skipping
+BpfM3evN6H8_000009	people eating apple
+BsHgr_sj6ec_000058	playing bongo
+BvdvbeIdUtk_000072	people booing
+C1fdGRZRPtU_000040	barn swallow calling
+C2kKMjYETRQ_000050	playing acoustic guitar
+C5ik_rcugw8_000040	people marching
+C7zLftUgskY_000035	playing harp
+C85lxZAStBk_000056	playing hammond organ
+CA3sbGHEE3c_000001	people screaming
+CFazHdGsxcU_000155	lighting firecrackers
+CG-2XtQI6sM_000000	cat hissing
+CG_qvz_V1Jo_000374	playing gong
+CJjTs72p1gI_000002	alarm clock ringing
+CKwtP-eN1Zk_000014	striking pool
+CUY3hob5V_o_000010	opening or closing car doors
+CUqga7lwvfM_000080	subway, metro, underground
+CVVrs_KA6sU_000030	rowboat, canoe, kayak rowing
+CViouHw-mfQ_000122	playing cello
+CYTTsSPohw0_000122	planing timber
+CdcfD8mg-k4_000030	singing choir
+CeD6RlRSr8M_000099	cat purring
+Cg3XzrFzzpM_000060	playing accordion
+CgevwvZLE3c_000219	running electric fan
+CheeUmf4IhE_000030	fireworks banging
+ChhTVgWMxiI_000030	duck quacking
+CiLwbeRDj8E_000000	people crowd
+CjwqjkkoJHY_000199	car engine starting
+CkYUBci5xEM_000070	using sewing machines
+Cntxv6aE3DY_000030	sliding door
+Co1qXvuwkes_000146	arc welding
+CpW7umx_bi0_000067	playing mandolin
+CqgPmVXNdNQ_000058	striking pool
+Csr7c9uFvQk_000028	dog whimpering
+CvN_oC0AGvM_000340	toilet flushing
+Cvgc82TDNnE_000025	lions roaring
+CvxL2n9DX6w_000251	lighting firecrackers
+CyW4FoAJ1MU_000260	police car (siren)
+CzGGyIj84Hs_000030	pigeon, dove cooing
+D-HXQTcZNGU_000130	female speech, woman speaking
+D109-sQNo1k_000028	sliding door
+D6BCygx6jcs_000000	dog howling
+D7kL3EEOyR4_000050	helicopter
+DAy_bV1d9c4_000046	playing squash
+DGU-HbuX6rs_000230	people crowd
+DJan9OSSF7c_000060	plastic bottle crushing
+DO-9yuU9brk_000028	sea lion barking
+DOi5UxxTknA_000041	driving snowmobile
+DPpo_Whnuqc_000022	missile launch
+DQelhAtUyHY_000030	playing electric guitar
+DR7TdSc2ahQ_000030	sliding door
+DSThhOKXU-c_000250	playing bass guitar
+DSgKhbtDWWo_000400	playing flute
+DX5_AglGFMw_000349	metronome
+DXfTYgSGLac_000177	alligators, crocodiles hissing
+DZo15IMYpmA_000206	vacuum cleaner cleaning floors
+DaMG8zJSkuw_000100	playing trumpet
+DbgRhWmYTJk_000002	frog croaking
+Dbi2L5z8U-w_000020	driving motorcycle
+DdZ6PSUQoQA_000050	female speech, woman speaking
+DfZmOeeF_CI_000024	lathe spinning
+DhaOFNnOC8o_000102	playing steel guitar, slide guitar
+DhxWWDGdF8I_000159	frog croaking
+Dj3sIimPrCk_000330	pigeon, dove cooing
+DmSsL0Xde-I_000005	missile launch
+DpIKdB4c_JU_000030	sloshing water
+DqnMEAN1GVc_000098	baby laughter
+DrPa82cqlSM_000008	playing mandolin
+DroorVxOn5s_000030	engine accelerating, revving, vroom
+DsVtCIaWv-Y_000377	hedge trimmer running
+DtRqBLRUTRo_000069	playing clarinet
+Dtiv9RNaA4U_000106	train horning
+Duk5ikgbUfU_000030	playing violin, fiddle
+DuyL15HJn6M_000036	elk bugling
+DvascfU3OM4_000233	playing bongo
+DwE0cQ3Xz70_000030	chainsawing trees
+Dxxg6NenmBQ_000153	playing oboe
+E0ocfyjk1lw_000129	scuba diving
+E22HBR9rEkI_000030	lawn mowing
+E22UuQ6SRf4_000001	fire truck siren
+E4IHTinI-3k_000010	people whistling
+E4dvhMWr7K0_000140	playing didgeridoo
+E5ICgH7JVFI_000003	driving snowmobile
+E67GhkgB8Jc_000033	cell phone buzzing
+E6tu_4cO7ok_000107	playing cornet
+E8LoFlcAC-M_000051	playing vibraphone
+EDtJ88ZJtWo_000008	playing bagpipes
+EEJp_Ssp0No_000004	dog howling
+EG2bfvkpzjk_000136	playing steelpan
+EGKE_rOo-Gg_000030	playing violin, fiddle
+EHHBn9EAtg4_000040	people booing
+EHHefsog-aM_000069	black capped chickadee calling
+EHkkma0y1T8_000030	people sneezing
+EJudk9RWsZI_000000	car engine starting
+EKkFWhdVAOU_000032	woodpecker pecking tree
+ETcwLdOldMg_000000	blowtorch igniting
+EU3OmHbOUo0_000000	cattle mooing
+EbnPPw9P3MQ_000409	snake rattling
+Ee1Glgpx3YE_000038	scuba diving
+EeUHgSkCSi8_000666	turkey gobbling
+EhDl29RiF74_000085	black capped chickadee calling
+EhaE7gijT78_000119	baby laughter
+EkbcNbEn1Z8_000063	opening or closing car doors
+EoubRuwDlrw_000038	canary calling
+ErdH1gc3ZmU_000003	playing cornet
+F186zkBSFjE_000110	helicopter
+F1ZVQSywml4_000040	skateboarding
+F3yETAYfYZg_000009	playing theremin
+F6xLA2AA2GA_000090	people crowd
+FAdeuN1uc-M_000230	subway, metro, underground
+FCir2lQei8M_000030	playing harpsichord
+FEltES9TUEU_000008	hammering nails
+FGWcwpr_SeM_000133	fire truck siren
+FGoXt7LIK3U_000010	police car (siren)
+FHz8YQy4q5A_000027	tractor digging
+FIPu0jd8I28_000030	people screaming
+FIpCyWCy9Qc_000030	playing violin, fiddle
+FRxNI559-Xs_000280	railroad car, train wagon
+FUVXK29tUwQ_000000	owl hooting
+FWuYLFTe3_8_000000	playing trombone
+FXzP5bUz-Lo_000017	horse clip-clop
+FauD2eg73V8_000030	playing electronic organ
+Fd_SXrGw6ag_000030	playing marimba, xylophone
+Fe9YJozRi78_000148	child singing
+FfpD5XC8b5w_000137	playing bongo
+FglnuP1jpRY_000030	playing cello
+FhHBIlZ_5T8_000035	wood thrush calling
+Fj34VCzy_Og_000030	horse clip-clop
+Fpqf057G_SY_000000	chipmunk chirping
+FpwtNUX45qU_000047	snake hissing
+Frs4_Uf8Tq4_000127	ice cracking
+FtCT62fiyrU_000270	church bell ringing
+FtNV_Gq62l8_000019	cat meowing
+FudSk5EUbAY_000156	playing ukulele
+FvZqgCIbO2Q_000003	hail
+FyszP9lfbDk_000001	playing didgeridoo
+G-5AgMNzjv4_000017	vehicle horn, car horn, honking
+G-Eokh465wM_000030	printer printing
+G-IdABSxeHI_000097	dinosaurs bellowing
+G-jsAK9ITwM_000030	ocean burbling
+G6FhQuR3_88_000000	playing congas
+G6nSnVQCxBQ_000095	elephant trumpeting
+G7E7D2Z_Juo_000070	people burping
+G7F8HVNw1lI_000081	scuba diving
+G9AKWSzZtWI_000030	people eating
+G9F38sObAns_000025	playing harpsichord
+GAFJeF_AqZA_000086	hail
+GBf5DgubSuE_000030	wind noise
+GD8dVFZaWNU_000030	skateboarding
+GDQjuDpqnJI_000030	wind noise
+GL1TqKjpv1Q_000047	playing theremin
+GLA-upuVPSA_000057	police radio chatter
+GLtFkIbCZOY_000140	pigeon, dove cooing
+GMNJCJ0ykfc_000050	male singing
+GOFDdcvXq40_000030	goose honking
+GPl4twCSrLQ_000001	coyote howling
+GS_JqZCyqOc_000050	stream burbling
+GT2frI8BMMM_000013	vehicle horn, car horn, honking
+GTZkjw4aVn0_000030	engine accelerating, revving, vroom
+GUSlicDnqIA_000045	playing congas
+GX4kLN3hW4Y_000149	planing timber
+GXIPKWMIVhs_000072	playing oboe
+GXRHmy5Bqas_000008	vehicle horn, car horn, honking
+GYJCyn2piCc_000329	lip smacking
+GZoVDjx9ltQ_000235	playing erhu
+GZoypVKRpCo_000003	cuckoo bird calling
+G_hP5gvRfNw_000033	cat growling
+GaFqib8bCLM_000019	tapping guitar
+GbTzdC4mOtQ_000030	machine gun shooting
+GbUoljsX3lg_000672	people gargling
+GgUkhedV5e0_000190	female speech, woman speaking
+GhizOxu0ZpI_000060	people belly laughing
+GidBfE5JU3s_000005	vehicle horn, car horn, honking
+GjKjnplphn4_000200	playing acoustic guitar
+Gjide6V8U-E_000039	dog growling
+GoMH9AL7YRA_000050	ambulance siren
+GvPc1ncg0OY_000138	people booing
+Gwp62TNrER0_000014	barn swallow calling
+GxUovR3d2aM_000019	car engine knocking
+GzAdcTtwkM0_000011	missile launch
+H-ZKdWCEhbI_000140	fire crackling
+H-jnsSCa-c8_000090	playing lacrosse
+H-rd3O5haG8_000070	playing bass guitar
+H0zmJjMoV-4_000012	playing squash
+H1lx8lLLceQ_000120	machine gun shooting
+H2r4JHm00Vg_000260	sheep bleating
+H6onyc5r6os_000024	heart sounds, heartbeat
+H6z_gPH8m2A_000055	people crowd
+H7BcUVlPDsg_000026	parrot talking
+HCPKDz63_s4_000000	child speech, kid speaking
+HCuORBJf-Ho_000027	playing cornet
+HL36YvzbFYs_000210	goose honking
+HL_E1j069EI_000030	female speech, woman speaking
+HOD29VAXJD8_000030	car engine knocking
+HOI0ZaKLAMM_000030	fireworks banging
+HOI7KapLzz4_000030	playing violin, fiddle
+HOupeg-QhHk_000073	yodelling
+HQSafj2aCNI_000100	playing banjo
+HQlV2jYCz5k_000030	playing violin, fiddle
+HR35d67Dhts_000108	singing bowl
+HRaGv5q3P3E_000000	opening or closing drawers
+HTRRMT1NQOc_000060	playing saxophone
+HTqUtEGJ0As_000030	people whistling
+HUP72tlgzyE_000066	playing badminton
+HUWvhtKby-A_000033	car engine knocking
+HW2o3t3fE_k_000062	francolin calling
+HX2ccFGAuMU_000163	electric shaver, electric razor shaving
+HX5BeffFwV0_000008	smoke detector beeping
+HaABMNzUOvo_000030	wind rustling leaves
+Hakqd6g2jaY_000110	helicopter
+HcO60nHH4W0_000023	playing bass drum
+HckqMrtU3dg_000133	playing double bass
+HebxWsaO-LA_000115	train whistling
+HkCt4hh_x58_000030	rowboat, canoe, kayak rowing
+HlUvoEXQZYk_000007	playing tambourine
+Hlp5qKMfdYk_000180	playing bass guitar
+Honj-TQHx3U_000129	airplane
+Hqhi7LioGyM_000030	playing marimba, xylophone
+HsCj9l5Barg_000045	fire truck siren
+HsX5XlPFOWI_000380	lawn mowing
+Hum53_V1zw8_000001	wind noise
+Hwp_62TYhDk_000110	playing marimba, xylophone
+I-WMZh-ieC8_000280	playing harp
+I-qeWJGSXuQ_000083	playing bassoon
+I4ffG1Bh-d8_000156	playing oboe
+I5wV1AFabIA_000029	frog croaking
+I6_30m_TQ2o_000000	playing tuning fork
+IBy30oL3yxw_000399	playing harpsichord
+ICajcUYAan8_000410	people babbling
+IEiseWb8Tao_000080	playing acoustic guitar
+IF92YmTMtdk_000089	cattle, bovinae cowbell
+IFGbGcs3bQQ_000034	chinchilla barking
+IHaWOJuekYY_000109	tap dancing
+IINqN6L2NsY_000285	tapping guitar
+IJvYFkrfjBg_000049	tornado roaring
+IKj9E33H8e8_000012	pig oinking
+ILBWV9AFKDU_000115	playing ukulele
+IN-9DFoS3fM_000007	bird squawking
+IWVztd9QsXg_000005	owl hooting
+IWhgJgeUQuA_000090	playing bagpipes
+IYhq5aun18M_000181	police radio chatter
+IZAasx5KIKE_000010	fireworks banging
+IaAKobKeOtU_000271	people marching
+IeD5tKVhuI4_000030	playing synthesizer
+IeK6EDl8Z_k_000033	people clapping
+IeW36MTcnBs_000117	dog growling
+Ieca4fwxfyY_000049	tractor digging
+IicM8tOXAFg_000146	pheasant crowing
+Ik40yoz30vE_000068	woodpecker pecking tree
+Il82kphC6es_000172	dinosaurs bellowing
+IlLCyGNjG3M_000060	playing harp
+InxgcOFzxWY_000070	chicken clucking
+IpDU10kKguU_000311	vacuum cleaner cleaning floors
+IqCRbzhPkvU_000000	lawn mowing
+IrcX151sayY_000098	tapping guitar
+IrkyGrHjygY_000020	tapping guitar
+Irx-WWFsQYU_000667	people eating
+ItnOPd_CktY_000020	people coughing
+IuTgZQVcMBg_000007	sloshing water
+Ivho6H4q1zk_000017	typing on typewriter
+Iylzuk-0j64_000163	slot machine
+J0ZBjy_EEtg_000015	people clapping
+J18R3qBnJtA_000120	waterfall burbling
+J1kAKMeULF8_000500	subway, metro, underground
+J3K5HEX3gko_000030	playing banjo
+J4VeWujsLJg_000030	typing on computer keyboard
+J5ugw2GUbnY_000001	dog whimpering
+J7fVkoC-Ha8_000711	people eating crisps
+J82OaPeyioI_000030	horse clip-clop
+JC33o6YxH9c_000220	playing piano
+JHNBF0WJ-EM_000029	people belly laughing
+JIdUC1zZb9M_000060	rowboat, canoe, kayak rowing
+JK4YikH2myA_000161	playing vibraphone
+JKrghKg6UBU_000260	ocean burbling
+JKxdjXEI9Wc_000015	eagle screaming
+JLPpMZlBOEI_000038	playing accordion
+JQ3bFZbatGk_000030	people running
+JQr-BRXrjN4_000002	airplane
+JVevxopJjU8_000823	playing tabla
+JXi1ZtJecYo_000001	bowling impact
+J_k6z7_YVJU_000090	playing piano
+Jbiig_IQdIo_000282	cap gun shooting
+JcXhB_4B32o_000090	playing clarinet
+JeJGThFGm80_000001	lighting firecrackers
+JfAjUMKjoVI_000460	playing harp
+JfiFq8tn5Pk_000009	playing steel guitar, slide guitar
+Jk-SBbw7Afg_000140	driving buses
+K-B9CIVeQ_U_000030	horse clip-clop
+K-MCXLQmnFA_000004	playing banjo
+K3KsP-m_c5I_000353	basketball bounce
+K5HBK1c7noI_000010	cat meowing
+KBc_FdBzN2U_000017	wind chime
+KFFJI_TZmoY_000047	crow cawing
+KJwga4gMEzU_000239	people slurping
+KJxSJR3v6oE_000013	church bell ringing
+KKd2qSxww1o_000002	typing on typewriter
+KM_VudA7hgo_000030	people running
+KOzRB30gxpE_000362	planing timber
+KQbCjNzlYPs_000082	writing on blackboard with chalk
+KU5WQZsoKRE_000079	child singing
+K_8tBU1LYxU_000000	chicken crowing
+Kbc8ioemPlA_000081	tractor digging
+KdD8xho7ymw_000037	cat purring
+KfqdB93utIg_000000	waterfall burbling
+KfyYM6nq--A_000011	playing vibraphone
+KnwgxGWxp7Y_000025	people whistling
+Kp0W7S-oExs_000030	driving buses
+Kq0Dbp3C4d0_000017	dog howling
+KrUuPSM4LxM_000215	magpie calling
+KsuQWEN0COQ_000199	playing darts
+Kus5SmqOIrA_000024	mynah bird singing
+Kwha8UYndzI_000090	playing didgeridoo
+Kz4Jm9_iFeg_000038	hail
+KzK6d6Qpu_o_000010	dog barking
+KztFbSJPxg0_000197	planing timber
+L4u9LOjcXoE_000000	people sobbing
+LAaJfzvvlTI_000053	lions roaring
+LAx_fanEB_g_000168	arc welding
+LB2EbSmDSKw_000007	baby laughter
+LBH_D9h18bw_000042	rope skipping
+LCcPzeH_Cn4_000160	sailing
+LE49c8e5VMU_000049	mynah bird singing
+LEpzp8DnWyY_000026	sharpen knife
+LGMZ9c7q8tE_000168	cat purring
+LHYHo8wJF74_000342	playing oboe
+LJsSbG5A1y0_000000	lighting firecrackers
+LL618LsL2zY_000030	pig oinking
+LMbyOx04l9E_000036	vehicle horn, car horn, honking
+LNl3ANFth4Y_000021	mynah bird singing
+LOLFOiNiS1o_000067	sharpen knife
+LPTsZZVr06o_000030	people eating
+LSsYBN_RvPc_000122	rapping
+LWrztDg2BGI_000245	playing synthesizer
+LYUkVukRObA_000236	pigeon, dove cooing
+L_Da1Sv1iKU_000028	playing didgeridoo
+L_OvLmH_feU_000021	dog growling
+LaGhL-3ctOc_000048	playing double bass
+LbjkUR-ERQw_000049	opening or closing drawers
+LciaPQ1XV3c_000217	playing badminton
+Lfmcj5VW6VE_000050	playing acoustic guitar
+LgdtTzvKnT4_000030	rowboat, canoe, kayak rowing
+Lj4Ngu0ars8_000138	electric shaver, electric razor shaving
+LlRZR8xPOEw_000021	frog croaking
+Lmp51YN-7wc_000466	people marching
+LtqXpk2YGls_000010	chainsawing trees
+LuxrhiicesU_000000	donkey, ass braying
+LvzMerRGbCE_000099	bouncing on trampoline
+LxdOWpwSzi0_000400	mouse pattering
+Lz8Ytz12MrU_000120	chopping wood
+M0EaEBlx5fk_000126	yodelling
+M9cNmb9HKPc_000110	turkey gobbling
+MApvC99wovc_000159	car engine starting
+MEtdxR3RdEA_000180	playing piano
+MFEhejrPVmw_000040	dog barking
+MMTvsiahcsc_000002	fire crackling
+MMjEIFDYQvc_000117	yodelling
+MQPNvRDVuUs_000100	playing french horn
+MQPggq37uX8_000003	scuba diving
+MQcS6DqCjKQ_000030	playing vibraphone
+MRnnE9MTm64_000052	driving snowmobile
+MTD6-1mrtP8_000072	owl hooting
+MVmJujaAocY_000030	baby crying
+MXmetP4F-EU_000019	door slamming
+MdUG2H5K5eg_000117	roller coaster running
+MenAsca8z6s_000137	slot machine
+Mf6bCl5HKgc_000000	wind chime
+MfSXrFJt6d4_000007	motorboat, speedboat acceleration
+Mhzz75z8mbY_000166	playing ukulele
+Mk8fhA3DAsA_000030	turkey gobbling
+MkrFhq3F_z4_000100	playing accordion
+MlX7I-OZIyk_000062	playing timpani
+Mmyr6Gpclbk_000070	bird chirping, tweeting
+Mnv4KVEt18I_000018	people giggling
+Msh94MTYC6A_000290	chainsawing trees
+MshXUve673A_000363	elk bugling
+Mvn2oFoKxwI_000128	people booing
+Mvue0y_EsDU_000000	orchestra
+MwVghEDjyQM_000030	people sobbing
+MwsoiJOqg_g_000030	duck quacking
+MwyzEfk2xbA_000054	playing double bass
+Mzc3DajWA0k_000030	ice cracking
+Mzgas545UXU_000090	playing snare drum
+N09QFSbvIC4_000150	playing electronic organ
+N2DQWIePoLs_000030	playing violin, fiddle
+N3_jZV1ejnA_000030	crow cawing
+N5CNEOKptjo_000000	splashing water
+N8cNWpCL0Rs_000183	owl hooting
+N9cM9BdATNs_000081	people booing
+NAETplWD64g_000030	playing harpsichord
+NAk-PU3X_DQ_000026	mynah bird singing
+NBeonGAqO84_000032	playing bugle
+NCdkXluu-D8_000350	playing harpsichord
+NFd5Zot-0_c_000006	heart sounds, heartbeat
+NJfJ4E9EVoM_000120	people whispering
+NN6mOUDBjEM_000042	dog howling
+NWSsGcjVRDw_000245	playing tabla
+NZs6RgHZOoI_000013	firing muskets
+NfcCnLiHlqU_000134	playing erhu
+NhO6B0zM9Pc_000030	playing electronic organ
+NjKRF79wl5Y_000110	wind noise
+Nkz9_eGsHKY_000057	people booing
+NmdqThtOVro_000160	beat boxing
+NnNm_oqkG0o_000050	people sobbing
+NniPHshHj9M_000068	playing didgeridoo
+NqxCX4G3N2g_000107	playing volleyball
+NrCNo4V7RVM_000030	lawn mowing
+NrWxMrh7cGw_000210	playing cornet
+NtJQ6W2o0EI_000075	canary calling
+NwIDavS0llk_000010	chicken crowing
+O-C9p_sK_eI_000030	horse clip-clop
+O0QV4_JRM0M_000002	car engine knocking
+O15FUv56iCc_000040	playing cymbal
+O3geFV-GoqM_000031	fire truck siren
+O5LFB39yCA4_000085	missile launch
+O5TMWyFd1DQ_000180	playing vibraphone
+O6_sGC3v96g_000006	wood thrush calling
+O7KCtFRaWck_000080	alarm clock ringing
+OEu8pZpN8ZA_000000	using sewing machines
+OIqUka8BOS8_021217	warbler chirping
+OLtTuBhG-og_000075	playing squash
+OTVFQoNRQTs_000060	playing bass guitar
+OWlCVuOznw0_000019	arc welding
+OZ14CiqpJL8_000010	child speech, kid speaking
+Ocdu7Lz0IuU_000003	francolin calling
+OdGHvGlSUcM_000157	playing timpani
+Of59qi5xxkM_000050	playing drum kit
+OiIaJb68Haw_000050	playing banjo
+OjOQ0K6lza8_000018	playing tuning fork
+Okd7ksWR-fc_000547	swimming
+Oljdv3iSTBc_000047	people eating noodle
+Om-Uc7ia1f0_000320	playing bass guitar
+OrueZOVOAD8_000010	motorboat, speedboat acceleration
+OtDVd-1zaqU_000030	motorboat, speedboat acceleration
+Ow1ZEhmP3qU_000116	typing on typewriter
+OyoJ99jDQdo_000147	playing didgeridoo
+P1eMMIK0cTs_000011	mynah bird singing
+P2taxpwuzcw_000053	wood thrush calling
+P2wbv4C6bBA_000210	barn swallow calling
+P35m_Rn7HbA_000030	motorboat, speedboat acceleration
+P5Y1D-fSVfg_000054	lip smacking
+P6sG1m6C4zI_000081	mouse pattering
+PavKY6YlSl4_000026	people whistling
+PawUc0pqf9M_000260	car engine starting
+PbomocKzqKU_000109	splashing water
+PeJxiP0CPn4_000025	playing didgeridoo
+PfwBOCxEst8_000243	cheetah chirrup
+PjbxRjKvzw4_000030	wind noise
+Pll-TpbHen4_000067	airplane
+Pp61sP7bols_000076	cap gun shooting
+PrIQbadXX74_000692	playing oboe
+PsmihTl5Cx8_000060	pig oinking
+Pu4BCOv6e5Q_000020	fireworks banging
+PvE48Ub_CgA_000034	bird chirping, tweeting
+PvpA8y7-ZC4_000101	people burping
+Pvt8VUQ_Bso_000030	playing vibraphone
+PxEpiEid_c8_000177	slot machine
+Py5s1uL46L0_000100	male singing
+Pz618GchhGI_000001	otter growling
+Pz9BhPMUzv8_000258	lathe spinning
+Q38lPvwj5Gw_000234	swimming
+Q57DFiTwcM4_000221	people eating noodle
+Q5jnMD1z86k_000287	people eating noodle
+Q7X3fyId2U0_000090	tornado roaring
+Q7ZPnRQraJk_000200	playing clarinet
+Q9AvyaxgRRo_000141	playing steel guitar, slide guitar
+QBFaKTDXCCQ_000120	playing acoustic guitar
+QBfcf-k5U28_000007	vehicle horn, car horn, honking
+QCX3H9wXgpo_000053	cap gun shooting
+QEcQtxP1fdg_000056	playing bongo
+QGkUBiVG8-Y_000034	owl hooting
+QH5ZtCI9Hts_000125	chopping wood
+QL6Ws4i07is_000040	goat bleating
+QO9sbXhMq08_000220	people hiccup
+QOFuXRetSLI_000064	arc welding
+QT1nE5lR7wA_000035	cat growling
+QTqN9c6661s_000000	forging swords
+QUMzyZRYpWs_000019	playing steel guitar, slide guitar
+QXEq7sE7dqg_000030	police car (siren)
+QXjaLCotbpY_000055	playing zither
+QZ-cG6VdBHM_000070	helicopter
+QaBmzAFivPQ_000096	people marching
+QcL-X7hJJYQ_000000	people whistling
+Qd9_UMNMhcA_000010	typing on computer keyboard
+QdEYMboSweA_000001	playing oboe
+Qdrcv-ZjC-g_000037	car engine starting
+QgEi6pAW36g_000150	male speech, man speaking
+QgHYiH6ES08_000120	basketball bounce
+QgQKqaMqRgs_000062	playing bongo
+QhRcayuLZ48_000390	playing piano
+Qlj2HEcX05Q_000136	playing saxophone
+QpA1_cezBwA_000123	mouse clicking
+Qr2PeUXBJu4_000197	playing erhu
+Qs8RjZlOcdU_000030	car passing by
+QsHeqaa4Ckc_000072	people whistling
+QsNHM92SIvo_000000	people whistling
+QvEMTs9_RQE_000010	lawn mowing
+Qwxa7ZCEBQs_000006	cricket chirping
+QyRrtn5AoSg_000280	playing saxophone
+QypTigdvLWU_000017	firing cannon
+R0YwusOkMx0_000008	bowling impact
+R1h5rRHM3oI_000000	donkey, ass braying
+R29qwv_mh4E_000018	playing bassoon
+R3VnztSX-k8_000200	ice cream truck, ice cream van
+R7KnzEqUGAc_000040	playing cymbal
+R7bSeIfRG-Y_000590	eating with cutlery
+R8cWq9GoEpE_000037	pheasant crowing
+RBHqcDacio0_000182	beat boxing
+RCIMcizSSZU_000044	francolin calling
+RDuDqEmKucQ_000030	motorboat, speedboat acceleration
+RJNjaPizyKg_000099	playing theremin
+RKZmAYXXWbg_000247	canary calling
+RM6uf-sdVQI_000043	playing bass drum
+RN96eLdMN_I_000005	bull bellowing
+ROsAOQe62gs_000050	playing electronic organ
+RVqCdL7_G2Y_000030	car engine knocking
+RWnvolYKQ2o_000414	lip smacking
+R_SNrPUIa1A_000140	playing bassoon
+R_yW6SKe_-M_000080	people booing
+RaawVrMvP7k_000048	pheasant crowing
+Rb0IEIeJTKY_000002	basketball bounce
+Rc_exQXrUG0_000100	skidding
+ReZUlDwGaLY_000080	playing marimba, xylophone
+Ria-XrpfgsA_000089	people marching
+Rifu8nB2cCs_000043	cat purring
+Riu9TpsQ_mk_000009	pig oinking
+RmGGiQMURcQ_000022	people sniggering
+RoLNzNAv-Ig_000030	motorboat, speedboat acceleration
+RrpMoJrp4AY_000180	people crowd
+RsYAulhucVI_000011	lions roaring
+RtHMCINXA0s_000052	cricket chirping
+Rur-IfwPZho_000051	dog howling
+RwE9JAktTvU_000580	people coughing
+RyV40yhlOeU_000419	people marching
+S29c6T__5HU_000003	playing timpani
+S3Ipyd9HHLk_000185	magpie calling
+S45cdr4x-mc_000080	chainsawing trees
+S9fw7NHd2eo_000380	playing electric guitar
+SBYzwBhUpYs_000166	playing badminton
+SBwOIJoGChM_000116	hammering nails
+SCjdlZSW8nY_000111	playing table tennis
+SGkzdDWFIHI_000085	playing bass drum
+SHebWHn0c2Y_000005	chopping wood
+SPnZIDCnKwM_000030	orchestra
+SS6iMabGB1Y_000020	chimpanzee pant-hooting
+ST33aEP5Hbc_000006	train horning
+SXC13GS87Co_000031	woodpecker pecking tree
+SXHYr-7nPaw_000030	playing drum kit
+SYDQX7Whjm4_000061	woodpecker pecking tree
+SYWqIfMOmGE_000051	hammering nails
+S_0v5j4S100_000039	cat purring
+S_qPgRNSkIw_000370	people clapping
+SbXyRN0DD-g_000080	dog bow-wow
+Sc-Ld96kbN0_000144	playing synthesizer
+SdCzaAUA6Xs_000005	playing djembe
+SeZm-iy9n8M_000150	playing electronic organ
+Sf0aZczIZVU_000040	playing cello
+SgYh5Lb7tlM_000130	playing flute
+SifYJFmSSRw_000123	playing marimba, xylophone
+Sl4weBj8xfc_000030	typing on computer keyboard
+SoVEYhxQabk_000103	canary calling
+Spm_zrjedzk_000392	cap gun shooting
+Sqq2dUA8t3A_000586	playing harmonica
+SvJ0kUY22C8_000055	turkey gobbling
+Sw6qDVMsR5M_000030	playing violin, fiddle
+SwQie7apk78_000198	playing darts
+SyEVBFw_9oE_000120	people screaming
+SyfyWK7dKXA_000021	playing squash
+SzmORuHD4g4_000059	wind chime
+T-AN31N4LD0_000050	people screaming
+T0NMgZC7CDU_000011	chipmunk chirping
+T19Xf5-OTHw_000130	playing piano
+T2zZbnu_NtM_000029	playing table tennis
+T4KEGH_8lY8_000119	playing timpani
+TAdH0kUJj9k_000050	helicopter
+TCUnK4k7QZ0_000000	telephone bell ringing
+TDh8_ixGzIo_000030	printer printing
+TGngN3n7EMw_000024	airplane flyby
+TLSmnnnyhEk_000030	people shuffling
+TMyd50KWyNo_000311	people slurping
+TNCcQfbselM_000120	francolin calling
+TQapWHNS5FE_000024	car engine knocking
+TRW01xXMMqg_000210	playing accordion
+TRt_14JcRWQ_000080	playing bass drum
+TTElms_ZWqI_000428	hair dryer drying
+TTstWFDMmqc_000030	people whistling
+TUPEF6PQxow_000132	rapping
+T_iuImHtqUI_000010	people sobbing
+Ta__Ev0mkBk_000030	chainsawing trees
+TakDv24Tiq0_000032	plastic bottle crushing
+TcN0QofoTvg_000221	playing erhu
+TdkhMZZvdgc_000006	owl hooting
+Tdyh5ziqH-U_000007	lions roaring
+TiaGOZ-ibxw_000411	people booing
+TriRWR9YiNk_000016	frog croaking
+Tse5rzNV5dk_000084	pheasant crowing
+Tze9ybKops4_000020	playing synthesizer
+U3-h9ZARqD4_000264	police radio chatter
+U34oQw93afs_000219	playing tambourine
+U3zsgbf9WHQ_000194	horse neighing
+U4RRMpX2wCU_000010	toilet flushing
+U55bYLMVKiw_000193	pheasant crowing
+U6vVDGaKL3Q_000354	bouncing on trampoline
+U9qUXBqIoZ0_000106	dog howling
+UA62hwIBgGY_000020	chicken clucking
+UFIi1OuMx0o_000302	rope skipping
+UGwl5VOHuaw_000200	playing accordion
+UIFxlzHYPBM_000060	gibbon howling
+UJ1lZOY9LSY_000035	playing didgeridoo
+UM1j8kFaxi8_000020	motorboat, speedboat acceleration
+UOL-hbkzUN4_000010	barn swallow calling
+UOlwg402_r4_000070	people clapping
+UPUwaW8jfhA_000030	ice cream truck, ice cream van
+UQonGRRRpv4_000024	goose honking
+UUKyUUjv8qg_000030	church bell ringing
+UZAB21OSorM_000007	electric shaver, electric razor shaving
+UZYfRXafn9I_000005	ferret dooking
+UZp0AcdimvA_000021	cattle, bovinae cowbell
+UeCkRYU_SuM_000100	playing accordion
+Uf2j1VbOk8c_000055	pheasant crowing
+UfG4dP0szuY_000040	fireworks banging
+UjTYiJ0dm8s_000002	vehicle horn, car horn, honking
+UkdS0cwAGYE_000010	car engine starting
+UnGLtJX29Hc_000043	planing timber
+UoFgJXGWJXA_000111	playing congas
+UpWivODbpIY_000059	owl hooting
+UsJAb6aftq8_000580	playing bagpipes
+UuuQH-TFxMo_000034	missile launch
+UzKZijSs4-A_000004	fox barking
+UzPSMiqeH3Y_000118	singing choir
+V-ZbY0SL2XI_000040	people sniggering
+V1ALglq7_x8_000018	dog growling
+V6lQVpw888U_000590	machine gun shooting
+V6y-jCli4I4_000000	cuckoo bird calling
+V7SGeTSJz9w_000090	skateboarding
+V82SmRI0GHY_000030	playing clarinet
+V83lIhKVraY_000125	playing darts
+VCEicqV_2Xw_000030	ambulance siren
+VDXN0xwWgRA_000083	playing bass guitar
+VDzkPfnI1g4_000093	playing djembe
+VEER910vqMk_000002	duck quacking
+VEhmvrgrZb0_000000	chicken clucking
+VFj1vFMV3dQ_000025	playing darts
+VGrI3TMjWog_000120	playing vibraphone
+VHQjG81NcXE_000030	crow cawing
+VS9R3iOc4Vk_000027	pheasant crowing
+VU9W8Y1E5u4_000030	bouncing on trampoline
+VdxslFvStdo_000370	female speech, woman speaking
+VfXlyIjtfo4_000117	baby babbling
+Vgs_XjEqKl0_000020	people sobbing
+Vh4E5JPTMBM_000146	typing on typewriter
+VhLn9pUFwXw_000039	chopping wood
+VhUG4vTpPUo_000324	ripping paper
+VhsFniEZO-k_000026	mynah bird singing
+Vkbp8VmL3pM_000040	people sobbing
+VkgLWYydiPE_000125	tractor digging
+VlGuwiKwJAM_000027	playing sitar
+VlkgwzKAamE_000051	ripping paper
+Vnnw7lK63rg_000041	playing snare drum
+Vt3qBXzyS5k_000280	eating with cutlery
+VwZ8gzI3qNE_000106	people slapping
+VwqcV76E6Nk_000000	people booing
+VwqqmiiznQU_000028	woodpecker pecking tree
+Vxs0xCJI92Y_000080	driving motorcycle
+Vzb427ZmWvw_000220	fireworks banging
+W0PwVllBxkI_000114	playing steel guitar, slide guitar
+W1o_XgU8lec_000050	skateboarding
+W2_8zRHaEPk_000150	playing vibraphone
+W2gkFTFR8mw_000047	rope skipping
+W4eT7fj-aIA_000201	driving snowmobile
+W5oXrz8dqBk_000030	playing piano
+W5wBkCwEEmY_000140	playing banjo
+W7OJevEgq7w_000000	dog bow-wow
+W7u5kEt-q-8_000000	playing tennis
+W9L5rTbcMFA_000004	people eating noodle
+WABbXpAT_UA_000049	playing bagpipes
+WAhoodHHm2w_000001	playing squash
+WBOqGIqUwGg_000090	people sniggering
+WD0aVtBqoxo_000120	goose honking
+WDmJ4ZtLuNU_000102	playing timbales
+WGHTlOM4-3w_000050	sheep bleating
+WH7LBLKyEkA_000241	playing mandolin
+WIWRYG4vJC4_000020	people burping
+WIZTFH-LGpo_000001	planing timber
+WJQ27fShKvk_000000	playing tennis
+WQFZLDitkkM_000067	eletric blender running
+WQuoH_HyUAk_000030	playing cello
+WRvPzjj5uoE_000134	ice cream truck, ice cream van
+WWzD6E9Wp_k_000260	playing cornet
+WXMt58sLsf8_000028	zebra braying
+WZ568vdA7bU_000070	plastic bottle crushing
+We-E7-Sx3Zo_000260	barn swallow calling
+Wg86ercBjY0_000002	playing clarinet
+Wh8A7CAuLe0_000028	barn swallow calling
+Whjk5Fvue1o_000030	singing choir
+Wj0qIPUjTfE_000008	lions roaring
+WqKP-0cSKgs_000030	dog bow-wow
+WvRkqVmRH0g_000088	playing harp
+WvcM0ueEjfo_000050	people burping
+Ww3CMatNd84_000721	cat purring
+WxQHtaD0Yqg_000028	tractor digging
+X-o1Twh5SFY_000032	playing steelpan
+X0gT3reH8A8_000120	people sniggering
+X17lq90OIO8_000020	dog barking
+X5C9NY9MjA4_000105	train whistling
+X7EGSxA-aCI_000132	child singing
+XBAwcPvVSoA_000068	lathe spinning
+XDMTylVtYx4_000190	race car, auto racing
+XEOUYLlaef4_000003	rope skipping
+XJnKU_SXYlM_000049	playing tabla
+XK4Ws-xvt10_000267	vacuum cleaner cleaning floors
+XKp4HCxVmaI_000017	vehicle horn, car horn, honking
+XLTqSk1Z3D0_000000	police radio chatter
+XM6eeVHjmLk_000001	dog growling
+XNgq-cDV7FI_000101	dinosaurs bellowing
+XOTSovKwxLk_000030	child speech, kid speaking
+XSJzshsMz30_000030	chainsawing trees
+XTDo4OaFapg_000100	hammering nails
+XU8dCEdiGWc_000010	crow cawing
+XUyBxCbiv7A_000073	playing bassoon
+XVveRibUh18_000023	frog croaking
+XWp8qMpnD00_000026	electric shaver, electric razor shaving
+XYZ4Nd4qV-I_000101	people humming
+XdSCT_cQDbE_000010	splashing water
+Xgm17YbPztk_000022	playing didgeridoo
+XiExpKM1Hpo_000160	playing trombone
+XlJ-tAbzzSg_000234	alligators, crocodiles hissing
+XtExs7nIzts_000034	people booing
+Xv4AVT2QYhA_000100	rowboat, canoe, kayak rowing
+Xxq7CElxJLc_000063	singing choir
+Y798EuJZaPU_000017	playing squash
+Y9Oee-VRfVA_000339	airplane
+YC_k4W1YaDw_000030	race car, auto racing
+YD41QET24SM_000125	playing badminton
+YD7jTek7yVU_000206	arc welding
+YEatlg_b0BY_000054	people burping
+YISopDKuQ0k_000050	playing accordion
+YJ5xLJ85AwM_000106	tractor digging
+YOTnbp40tf4_000030	male singing
+YOrImbuhsQ8_000027	lions roaring
+YS_zTwf-FRo_000092	playing ukulele
+YU78jPcU6FI_000070	playing trumpet
+YUXZVAQ1iJ4_000007	volcano explosion
+YUcdJy-rpD8_000590	raining
+YVOmkmjoT40_000030	ocean burbling
+YYgYiO9DjEY_000161	tap dancing
+YbALYr-5WpM_000000	playing harmonica
+YbOztklOkF0_000023	goose honking
+YcvHv44MYiU_000027	barn swallow calling
+YdjsatpizhE_000023	airplane flyby
+Ye72yJyWxs8_000021	airplane flyby
+YeEySSrxwpg_000078	barn swallow calling
+YfZp5C7xrKs_000181	playing bassoon
+YgySYOAi8JQ_000396	skiing
+YhJwTBFij48_000015	motorboat, speedboat acceleration
+YjCLRifFCj0_000010	skateboarding
+YjJioclqdQ8_000150	wind noise
+Ys1P04EjGH4_000196	playing bassoon
+Ys9j6IBcFBo_000024	opening or closing car doors
+YvBCKb1LbCk_000095	fire truck siren
+Yvq8WrFpXhE_000057	people crowd
+YwNdDHEhm2g_000005	duck quacking
+YwTFxcWCac8_000381	electric grinder grinding
+YyqqXEmYPIA_000020	ambulance siren
+YzBaTwjmikc_000018	hammering nails
+Z-V-1iUbMWI_000520	lions growling
+Z1BhAXfiZtU_000037	vacuum cleaner cleaning floors
+Z4QR8uvx_Wk_000169	reversing beeps
+Z5SyUJSDCOA_000562	ripping paper
+Z7Hzc1Yw2aY_000060	sloshing water
+Z93pTtHnDXo_000110	playing vibraphone
+Z9nG2fIh214_000075	chinchilla barking
+ZALP7Di4HaM_000180	playing saxophone
+ZAZZ1wImM9M_000010	singing choir
+ZCA_NapBTlg_000060	dog barking
+ZDDnEdzjyrE_000597	playing tambourine
+ZFGcmmpt1bs_000094	playing bagpipes
+ZL_MxixlnHE_000079	reversing beeps
+ZNboftBNdyY_000406	cap gun shooting
+ZPODO-Ehl_M_000030	male singing
+ZQO_uhrJPNA_000110	playing violin, fiddle
+ZUjum5gZMKM_000140	playing accordion
+Z_Bk_CnpWsY_000198	people sneezing
+Z_sW4UxpbbY_000050	using sewing machines
+ZbtuNDtoyOI_000030	sliding door
+ZcskQV2A2cQ_000030	playing flute
+ZdtaSkUkrIE_000256	police radio chatter
+ZeDa5hT2ffk_000071	police radio chatter
+Zgbuj3y2iuY_000210	cattle mooing
+Zh2whhvFWsM_000016	pigeon, dove cooing
+ZhLwVzOZziA_000368	blowtorch igniting
+Zi3FOnx4nuk_000001	playing table tennis
+Zj73Wh6LEiU_000120	skateboarding
+ZjN9CL7B-9I_000239	playing timbales
+ZkfUo4l9ruc_000090	chainsawing trees
+Zl_ZWSLB8Ic_000024	sheep bleating
+Zs8liAFeuuQ_000058	smoke detector beeping
+ZtPoTqVxVvU_000050	helicopter
+Zu0BpngzT_Q_000007	bowling impact
+ZuwSkX0RQQY_000343	playing tennis
+ZxmKMSUpbvc_000065	car engine idling
+ZxpiZiSAm9I_000060	turkey gobbling
+Zy70U6w0yXw_000088	mynah bird singing
+ZyUqhIDVuNc_000541	scuba diving
+Zz0fhQuHZEE_000012	penguins braying
+_0iRtZRG6UA_000047	woodpecker pecking tree
+_4RRKzDUd60_000079	lathe spinning
+_7GnnuKVVCM_000023	engine accelerating, revving, vroom
+_8FhgH9k7Rw_000120	vacuum cleaner cleaning floors
+_9wN5d1Z1ak_000024	lions roaring
+_CF34A0RrPs_000018	horse neighing
+_Cks36T64zE_000061	striking pool
+_DdVu5sPsjk_000490	people whispering
+_GaEZe-Z73k_000233	fire crackling
+_HRn4aOhjhU_000016	canary calling
+_H_W34UobYU_000459	bouncing on trampoline
+_HcIHVLRzpM_000450	female singing
+_NShiXyBmsY_000270	train wheels squealing
+_Ow1h1eTNk0_000178	playing trombone
+_SfaPFwwJHs_000026	train wheels squealing
+_T0iCBHWKt0_000101	pig oinking
+_T5ZUrmRiQI_000108	playing ukulele
+_Uyw_Legahg_000045	tap dancing
+_VOx5BWJsyQ_000030	raining
+_WQQ3QvGrYw_000340	child speech, kid speaking
+_WUAz2RAZZc_000201	planing timber
+_YF3aFSsgUk_000093	playing steel guitar, slide guitar
+_YhSeML8rQo_000109	alligators, crocodiles hissing
+_aX_UzkXRd0_000140	helicopter
+_cvucKdFb5I_000043	people booing
+_dIzu78Ld2w_000166	lathe spinning
+_gQFB_Utuf0_000077	cat caterwauling
+_j8zzvBts98_000000	splashing water
+_m6lwfMU8Eo_000272	electric shaver, electric razor shaving
+_pSMw5FKHX0_000040	people sobbing
+_pfccpy7Cqc_000180	typing on typewriter
+_ru-n--PRNA_000030	police car (siren)
+_t-Abwz6JG4_000031	baby babbling
+_t259gootxc_000190	female speech, woman speaking
+_u9zUuBdo1k_000000	cat growling
+_vkXDgupDN8_000250	sailing
+_wvB2HlVn1I_000050	engine accelerating, revving, vroom
+_xGLwynjhSs_000010	playing french horn
+_xq-9GZBfrg_000014	pheasant crowing
+_yVgX3hi1OQ_000195	driving snowmobile
+_zTmqhuLwAM_000001	donkey, ass braying
+a0LIemH5Cw0_000010	people clapping
+a3ZAFViNYyk_000000	swimming
+a57DUeBMeHY_000320	rowboat, canoe, kayak rowing
+a6CPpulnJ2A_000420	stream burbling
+a8fa79w2aIQ_000023	lighting firecrackers
+aC3nlLHFOfk_000030	playing violin, fiddle
+aCnLa_H0-P0_000000	magpie calling
+aDXQSTbKlIc_000010	playing cornet
+aE32elV-Jtk_000210	people crowd
+aG1wGSIqGR4_000013	frog croaking
+aHzkCSXsrqg_000038	vacuum cleaner cleaning floors
+aJ41sea1s0U_000080	people farting
+aNArqTW4cbc_000025	vehicle horn, car horn, honking
+aNOELrfjAYY_000000	vehicle horn, car horn, honking
+aRI4l67ZlYQ_000063	planing timber
+aSYCwv_hda8_000030	subway, metro, underground
+aSleAKgkDDk_000000	playing accordion
+aVs2QBhLIhY_000162	playing didgeridoo
+acYp_SYmHs8_000164	running electric fan
+aclGsdr83pM_000400	playing saxophone
+aezIOAga5V8_000070	child speech, kid speaking
+agMolFR_pFc_000075	train whistling
+agrdgrC2cdI_001076	dinosaurs bellowing
+ah5cSy0yXs0_000178	slot machine
+aiTXGmkpfnk_000030	playing trumpet
+ainzK7QuseU_000001	dog whimpering
+aj6kdMafoek_000693	hair dryer drying
+aju2z1N0aOo_000030	wind rustling leaves
+ap3PdrjChdo_000040	playing bassoon
+apTvGua1-FY_000271	playing guiro
+asXWEB_SBEI_000060	playing cello
+atT7DPwTkds_000130	people clapping
+auHL-4XCFAk_000030	driving buses
+b-8lh_tfhLQ_000124	hair dryer drying
+b-gza98ikBo_000020	playing snare drum
+b2bpNgK0Cnc_000250	orchestra
+b4Bu0AHwBWs_000084	woodpecker pecking tree
+b4WK1A7DK18_000018	crow cawing
+b8q6Z7dtRvg_000030	playing flute
+bBMcsO6IeDE_000021	lions roaring
+bF89h31EEzg_000000	golf driving
+bFmIV3pNJPY_000001	basketball bounce
+bI_4_x735PA_000020	typing on computer keyboard
+bJtu55jpzNc_000140	playing violin, fiddle
+bJzkn2kRh8g_000070	helicopter
+bLAz_kbihLE_000147	elk bugling
+bMNcdb3Eeds_000064	civil defense siren
+bN9fXjHalIY_000065	playing timpani
+bPNt6iVmemQ_000504	playing bongo
+bPfP2rjJfDY_000609	playing ukulele
+bQV7q5VRaH0_000174	car engine knocking
+bT8QfAM9NRA_000197	cutting hair with electric trimmers
+bVdI6laTOXI_000480	people screaming
+bVskpqAJF8E_000116	people eating crisps
+bYT-N-_u448_000217	civil defense siren
+bZUN1tQnuDQ_000001	child singing
+b_C-fNIS8aI_000000	cat purring
+baVILr18Y9A_000015	civil defense siren
+bd-swxc3o4w_000260	playing hammond organ
+bo9sSwEqnzs_000030	orchestra
+bokQgOSQ2OA_000001	playing squash
+bpF6KhK8El0_000030	police car (siren)
+bsM-z2joYss_000030	child speech, kid speaking
+bsUBSFHXY0g_000040	helicopter
+bukJZ1FxymQ_000390	male speech, man speaking
+bw3GIZLj6kM_000000	playing piano
+bx5BUbiIXFw_000107	child singing
+bzxjT3h2ir8_000105	lip smacking
+c3UPyEZ1yQY_000070	typing on computer keyboard
+c4M3JIyAPcM_000020	playing bass drum
+c5dPZoWwmC0_000020	driving motorcycle
+c6e4pxgoCls_000105	magpie calling
+c84w0ECD-Lc_000010	ocean burbling
+cAI0pcOwk2g_000346	elk bugling
+cEddS8Y-qZc_000510	people clapping
+cFNcpddGRno_000340	fireworks banging
+cIHKR2E1uiQ_000303	smoke detector beeping
+cJSWXGTJMcc_000018	rowboat, canoe, kayak rowing
+cL_nCiBnlbk_000001	playing bugle
+cMdnie91zp4_000000	playing trombone
+cNUIc68WpD4_000075	people marching
+cRiW0u0QY18_000030	playing trumpet
+cSym5f2jySA_000005	chicken crowing
+cUBHfozbsao_000044	playing harp
+cV4QlanVa9w_000070	basketball bounce
+cVhWB3IniBo_000014	playing tuning fork
+cZfuBCVV6n8_000390	eating with cutlery
+ces9pc_r6Wo_000036	child singing
+ckwEyopmfKs_000024	crow cawing
+cmkEW0KJDYI_000165	arc welding
+cp-ZI_fQ1l0_000154	airplane flyby
+cwQY1bck2G8_000070	playing bagpipes
+cx4QSvep_wE_000009	train horning
+cxFdK2G6wq0_000030	playing bagpipes
+d-UQr-8UEUY_000069	playing saxophone
+d05lXeFKDn0_000275	pheasant crowing
+d4yBeEbVp1Y_000030	typing on computer keyboard
+d5HmVBPY1Qc_000230	playing saxophone
+d66pNyYB6WY_000013	people burping
+d8gWsmBdBhE_000097	playing sitar
+dBivnkxNOOc_000175	playing vibraphone
+dECLS-JHWYA_000000	vacuum cleaner cleaning floors
+dK46EdcZFzg_000030	playing trumpet
+dNMCURn41wU_000179	playing djembe
+dN_EzmXbsu8_000016	playing bass drum
+dSeWq0Qd9Hs_000318	playing tambourine
+dVg4IEbk-l8_000010	cat meowing
+d_OIBYBwexQ_000160	playing accordion
+daHwPM2azrc_000036	wood thrush calling
+dfr1OFz20sI_000000	goat bleating
+dgSOnxqNtFE_000246	people coughing
+dgS_Fy1FiNA_000110	people burping
+dhG_GSGW_RI_000004	volcano explosion
+dlJm9R5t_qg_000030	playing hammond organ
+dlWrMn_RDg0_000120	playing bassoon
+dqymshfwGEE_000030	playing saxophone
+duca08sjlbQ_000001	playing bongo
+dugd_OSzghs_000203	ice cracking
+e0LMGLr-T-I_000029	air conditioning noise
+e3ZJnO3s53o_000016	child singing
+eANsaSAzHm8_000010	driving motorcycle
+eBFPD8YrqiA_000140	driving buses
+eCpA_7B-k94_000030	dog bow-wow
+eDqfHtuB8Hk_000015	snake rattling
+eEUsoUKPxy8_000187	basketball bounce
+eFaLkcfCzos_000140	playing cello
+eK97_rb6BsY_000072	playing gong
+eLyQDSo2NAM_000129	opening or closing drawers
+eOJQsk_kdWI_000032	ice cracking
+eS8Tf1hfwxk_000205	sea waves
+eSEIPV-qSj0_000020	squishing water
+e_3GUZmPFBI_000020	playing erhu
+ebhtW1tIXRY_000002	donkey, ass braying
+ecTDu-EX3WE_000019	car engine knocking
+ecq96FWbCF0_000037	gibbon howling
+ed4wVB_RhHw_000011	baby crying
+ehw6y3_g-8A_000757	ripping paper
+ej6jlkTeobU_000002	car engine knocking
+el3i-oj08Q4_000173	playing oboe
+f-XD-BgLWk0_000000	skidding
+f5c5KuWylig_000343	vacuum cleaner cleaning floors
+f6Wl-9pzib0_000032	cattle mooing
+f8bMURZiPiU_000019	people whistling
+f9U7g3g4voA_000026	golf driving
+f9c9YZ8WgjM_000037	bull bellowing
+fD362l9P3u8_000041	tornado roaring
+fFn2P7ZRIeM_000480	playing clarinet
+fNlGlh1GaeA_000013	heart sounds, heartbeat
+fS17RfJYjS4_000001	pig oinking
+fTT_D_d_5FA_000080	people clapping
+f_55S5G8M2s_000000	playing harmonica
+faFCcN6y-C8_000020	ferret dooking
+fcyUlEGvMdc_000037	playing volleyball
+fj0qlDdWt1M_000158	playing squash
+fknz5hZg_3I_000295	playing darts
+fmc6hwse-IA_000085	skiing
+g-CydtX7btM_000086	eagle screaming
+g-u5YOJu_gY_000230	lawn mowing
+g1n-ZaW0QHQ_000095	reversing beeps
+g5OBeqvOmRU_000001	bathroom ventilation fan running
+g8E9gBfe8B4_000180	female speech, woman speaking
+gH25X_mj6mc_000210	ocean burbling
+gJbMwvsUyA8_000000	driving motorcycle
+gL2i_DTGUEY_000028	cattle, bovinae cowbell
+gLTvwzBktxE_000015	airplane
+gLj93C9rRsg_000055	playing tambourine
+gLokxx-ruH8_000230	playing piano
+gM9WSjAPDVc_000030	people babbling
+gPwtTVH44OY_000030	people shuffling
+gW-1oOsNGJs_000010	playing harp
+g_axbxP7Amc_000071	playing harp
+gaFtxq1hBU4_000118	spraying water
+gcBUpboDmjc_000033	hammering nails
+gfgv17hOPIM_000040	playing marimba, xylophone
+gjJ4nqwlgnE_000010	playing hammond organ
+gm0HkvshnPk_000340	cattle mooing
+goS6rwhPth4_000026	mynah bird singing
+goz-IQ8s6uk_000050	skidding
+gpaX15tTUoc_000017	cat growling
+guvnNwCkhcs_000030	people sniggering
+gwzqjVCFNqA_000040	playing clarinet
+gyioxO7fWzI_000046	lions roaring
+gyjZ7tnnZeA_000132	playing theremin
+gyt54t3R_BU_000032	blowtorch igniting
+gzqq0knK2FA_000003	cat meowing
+h-Z5cTyu4LE_000150	wind rustling leaves
+h0025UfxME0_000308	sharpen knife
+h0V51dolEjA_000194	airplane flyby
+h5Gq0y3qkX0_000063	volcano explosion
+h7EWw2n5D5I_000050	train horning
+h7pz6niHZuw_000006	donkey, ass braying
+hEePXITb26o_000042	playing harp
+hFVd2Em9-cc_000100	toilet flushing
+hQvIg0t546Q_000146	playing vibraphone
+hUlqIdQFuxE_000030	basketball bounce
+hV_CjOK-mME_000030	people sniggering
+hW-RxgLN2l0_000007	owl hooting
+h_tdr4t6unw_000300	typing on typewriter
+ha-5LhgpVmQ_000946	playing tympani
+hc9aQ8VL9o0_000083	playing steel guitar, slide guitar
+hcR4BiG8sZs_000150	playing clarinet
+hdphUn6ihrA_000450	lawn mowing
+hdqCHBTwnuQ_000253	playing badminton
+hgcLJFz2WKQ_000512	police radio chatter
+hlKkLqHpJ_s_000400	chicken crowing
+ht3jNf66nbo_000286	missile launch
+htRB8f0r2rg_000027	playing bassoon
+hvoOSCZo2-E_000030	cattle, bovinae cowbell
+hy87-XUmhkE_000004	playing timbales
+i-SmzP7T_E8_000295	skiing
+i3nEgFq4yfo_000578	heart sounds, heartbeat
+i4IsKRvCLi0_000036	lions roaring
+i5dz5NV4Vpc_000007	crow cawing
+i6BBre7xV-c_000937	dinosaurs bellowing
+i9PvGS9Xr9k_000332	lions roaring
+iAqJ9lPCU4w_000024	chicken crowing
+iD8gRmmiiqU_000130	driving motorcycle
+iDODqIflQ1Q_000061	parrot talking
+iFU48OcnO7k_000000	woodpecker pecking tree
+iLaLf95DcQk_000040	duck quacking
+iQMIGLrKlTI_000260	playing accordion
+iSjvZiygjCQ_000510	playing cello
+iTd7hOI27BE_000048	playing harp
+iXslVMHwkTU_000212	people eating noodle
+iZR9dpO64NA_000011	cap gun shooting
+i_-LCRDriig_000030	ocean burbling
+i_hhSKWxzeU_000038	frog croaking
+ibd7CKcSiTI_000122	playing bass drum
+icK4IQb2KsE_000000	hail
+ieRU5f5P4B8_000350	cap gun shooting
+ieXdQlIBgLk_000030	playing marimba, xylophone
+iiUvfvkeo0c_000237	disc scratching
+ijirbb9m05k_000285	swimming
+ipo5U5Grsno_000020	people cheering
+irUkV1DP7Cs_000030	playing cello
+irhsdhRIUwI_000010	fireworks banging
+itH-fbb9Ook_000250	ambulance siren
+ixv1jovJe3c_000151	playing timpani
+j-GF_0RxUlg_000176	playing bass guitar
+j-hyPaKjCAU_000030	playing accordion
+j0NNSluEaS0_000150	heart sounds, heartbeat
+j15Ldqb_XVw_000020	fireworks banging
+j2OhKQ6sm0o_000077	people eating noodle
+j3A_ekLNu1Y_000008	car passing by
+j4GHwj1Yqz8_000076	ice cream truck, ice cream van
+j5oZYOBOppQ_000003	mouse squeaking
+j6f4pheXNDE_000108	tractor digging
+jB-OcexH1n0_000033	cat caterwauling
+jBCKFPXuFOw_000086	strike lighter
+jBZ1C1ihCIY_000005	playing bongo
+jL4h1-_LECU_000022	church bell ringing
+jQRurvUk2xs_000051	writing on blackboard with chalk
+jVG2LQ2kA1Q_000067	playing glockenspiel
+j_WKRbDVZhs_000071	barn swallow calling
+j_vtU1U9rg0_000042	playing volleyball
+jb92NmGYNbU_000279	police radio chatter
+jd2ENRtbxRQ_000010	people coughing
+ji-27X81tIs_000133	playing bassoon
+ji4T1ArqCz0_000017	fire truck siren
+ji8HeUiTfoU_000030	orchestra
+jld-wHLRUWM_000020	playing accordion
+jmLX2yQ4eKk_000007	hail
+jsw5soBYfsc_000020	people farting
+jt7w_UY4yUI_000040	lions growling
+jxnPU7Okb5U_000043	playing snare drum
+jzw_Wa_TXVo_000018	playing squash
+k-AKVEheu4g_000096	alligators, crocodiles hissing
+k-jDS1jp_AA_000014	firing cannon
+k4h2VtrPwus_000161	rapping
+kAWAs_7SaKw_000000	bird chirping, tweeting
+kBmcp8nL6Kg_000195	playing didgeridoo
+kCsmvK06SCA_000254	playing sitar
+kDwFyUvAi4w_000077	playing bongo
+kEQJJyYkYTY_000200	child speech, kid speaking
+kL6xemyurI8_000140	people eating apple
+kPp7CwFBl1c_000030	playing violin, fiddle
+kPpaeW3DObU_000481	playing castanets
+kPus6xz6fN8_000030	car engine knocking
+kSdqIpAMz_M_000175	playing snare drum
+kSwrdM7UD98_000057	owl hooting
+kTyaqJIhX6Q_000020	playing accordion
+kVMXMaTyEbE_000116	playing theremin
+kVtj0bAYAF8_000000	people sobbing
+kW23iJgtyfk_000002	raining
+k_NIUqHoNz4_000037	playing bassoon
+khZPuH00RNc_000332	yodelling
+kjtZNsHp_a0_000330	lawn mowing
+kkgjiCKHvoY_000449	firing cannon
+kmPmQ6aylRc_000012	reversing beeps
+koTbsmbqyxo_000103	people booing
+kp_7Sd6s0h8_000306	people eating apple
+kqvpyaIls0c_000090	playing cello
+ksaiDSSJeOg_000030	playing cymbal
+ktBzLsiL6l0_000157	playing steelpan
+kxl_ZU3j99A_000415	missile launch
+ky92PHpUpEA_000050	playing accordion
+kyEDPVvDQt4_000040	bird wings flapping
+kz849EPouys_000318	magpie calling
+kzntbWmyWBg_000074	playing squash
+l0DQpxoSr2Q_000040	playing banjo
+l3i-cKkVL-o_000007	car engine knocking
+l3rzkrm98J0_000001	alligators, crocodiles hissing
+l3uGoel_Ats_000000	people crowd
+l4XYVX79H58_000400	people babbling
+l5LnwNRK7Bw_000030	playing cello
+l6uZDuUsdpc_000010	people burping
+l7ELBtiVtQ8_000190	striking pool
+l8bdmlXL-Lk_000197	playing didgeridoo
+l9ple4xWo3w_000193	chopping food
+lAF2dHM7Tyc_000170	playing electric guitar
+lEzMz9odWXM_000058	lathe spinning
+lGsxnfOPaUw_000022	baby crying
+lGtRJjnC4PI_000210	airplane
+lKhe8BxkRnU_000025	wind rustling leaves
+lLme6yedI6w_000040	cricket chirping
+lN2kwc34bo0_000050	train horning
+lP5znTMLevo_000030	playing bagpipes
+lQG8CRumj3g_000560	playing cello
+lUWrhn9z9FI_000096	pumping water
+lXIaZksDY38_000030	people shuffling
+lXwEV2S1rt4_000150	using sewing machines
+lc1QTC0R_CQ_000018	people shuffling
+ld9b7tfnqTE_000109	playing erhu
+ldF2EJCVY3g_000147	playing theremin
+ldvcH7bOy_o_000184	playing french horn
+levuF973w8s_000250	playing french horn
+lg6X9iqcqXI_000233	playing table tennis
+lg7DqdnmkmE_000130	skateboarding
+lj-PczKzEaw_000040	using sewing machines
+ljXTXoBG9rg_000077	pheasant crowing
+lnatlhCU5kI_000420	singing choir
+loMPOYNM66g_000123	playing timpani
+lqVp4OJ4hbY_000044	lions roaring
+lr1RLADQXNg_000110	helicopter
+lrFFGvB03Fw_000071	golf driving
+lsBttXzhPHw_000144	playing sitar
+lt5H2iH9Ln8_000120	chicken crowing
+lwgKXn21ymc_000774	people whispering
+lxFVAc2dHVM_000152	fire crackling
+lzLgjt8VRmU_000000	skateboarding
+m-4-BAv8cCQ_000380	lawn mowing
+m-NpPmAkncw_000030	male singing
+m0g-zWJJClA_000150	playing banjo
+m1lFSuSixy8_000350	people marching
+m1lFSuSixy8_000613	people marching
+m2E4i-EzHIE_000085	people finger snapping
+m4j5XY09HlE_000021	car engine idling
+mCyvq9TF5Ms_000052	typing on typewriter
+mInTDyk6c2A_000012	writing on blackboard with chalk
+mPnRdL1sC48_000240	people eating crisps
+mQ60N4HdDyI_000102	machine gun shooting
+mRCzIaqRG_c_000000	using sewing machines
+mWGLXbNhuB4_000096	hammering nails
+m_7BjYa44lo_000030	child speech, kid speaking
+ma0P7XOsBgE_000030	people running
+ma2RuCUufcI_000036	fox barking
+maUlA8WWTEQ_000004	hail
+maVHGHl01Yc_000034	lathe spinning
+mcVY3xsxgcU_000060	playing bagpipes
+mi9AokZ8m5s_000849	shot football
+mjK1vNF3lKE_000023	playing theremin
+mlihNhHFGTM_000030	playing harpsichord
+mt13n4XleGY_000030	orchestra
+mwu46g-jnac_000170	bird chirping, tweeting
+n-PjT4mDn9Y_000173	playing bagpipes
+n0PnM0u47m4_000042	mynah bird singing
+n0gO6pPICi4_000065	playing mandolin
+n21m6N5UmNk_000002	firing cannon
+n2CgftHGLJ0_000030	driving buses
+n3bX64Z_Yds_000000	playing clarinet
+n4wpVSIu7c0_000087	beat boxing
+n6PQq584nWA_000010	playing trumpet
+n8vhraccEnc_000009	dog howling
+nAtvzIyRwnU_000100	playing saxophone
+nEBUuVsMtGE_000000	church bell ringing
+nGIVQLeZ76E_000103	bowling impact
+nHDsu69zzSA_000000	skidding
+nIHYEEVzuzE_000095	canary calling
+nJ7TBigS5bY_000018	people booing
+nLOOmtvC9Hc_000066	playing steel guitar, slide guitar
+nLVmclZYZMY_000200	people screaming
+nP0vO3Xv10M_000010	dog barking
+nPCYkMhaLYs_000024	roller coaster running
+nTo6W-50CDg_000018	whale calling
+nXc-dHK2A2A_000016	playing theremin
+n_F_tRGGoEA_000107	frog croaking
+ngJ_Us2C19g_000040	police car (siren)
+niYH8Dpt4uE_000140	cattle mooing
+nnyll58-lrA_000009	wind chime
+nowY2-6reIk_000030	pigeon, dove cooing
+nz0qYNbFGD4_000030	people coughing
+o2-6TSqWPCY_000170	people clapping
+o2qd4hsquvE_000056	bird squawking
+o4F5dtUXivA_000034	playing steelpan
+o6kY64rTk2k_000291	singing bowl
+o7mBR043UCs_000014	pig oinking
+o8iHgGRzcTE_000020	people clapping
+o8oMY-WgW9Y_000030	wind rustling leaves
+o9uGfNn4JyU_000062	lions roaring
+oBrRQ5SiJTQ_000210	driving motorcycle
+oCZ3WCK5BZU_000000	driving motorcycle
+oDAI33ybJlo_000029	playing theremin
+oDuiwpaep1k_000035	sliding door
+oEEOscuru6s_000280	playing flute
+oEXqWoSZ9Ww_000024	playing erhu
+oG6EUnQjeF8_000077	swimming
+oIUi8gFI_XY_000178	cat purring
+oIXRSpjo7vk_000170	wood thrush calling
+oJ4m2OvhA8Q_000100	playing cello
+oSsLQCIJjyE_000030	singing bowl
+oVxKyGnz-IA_000230	chainsawing trees
+oXXHkjFLN3E_000237	electric shaver, electric razor shaving
+oX_XdxqTE9Y_000110	bird chirping, tweeting
+oYe46obCJhc_000039	alarm clock ringing
+oZ6l0EStee4_000011	police car (siren)
+oZKVPzRyn50_000432	playing electronic organ
+oad_agP1oJU_000287	playing harpsichord
+od2HXuT_NuI_000100	playing cello
+oePtbOc8Hqs_000000	foghorn
+oeSxlmkPj78_000030	ocean burbling
+ofFtXFnfebQ_000684	cat purring
+ohh7mWALd_k_000473	pheasant crowing
+olZa2vOpbD4_000110	male speech, man speaking
+omiGYobPra4_000100	toilet flushing
+onqGNrWQ7us_000587	machine gun shooting
+or7ikBeUhBg_000020	driving buses
+osA1JXFL2Gk_000021	parrot talking
+otp3r8SfygA_000102	people shuffling
+p-DcPCo7Swo_000086	playing double bass
+p4RWTSRg6Bg_000290	people crowd
+p5LsBog-XRk_000130	playing saxophone
+p5j91ecL43Y_000030	people whispering
+p8HTTAhm5ic_000100	waterfall burbling
+pAe8kcpjZII_000010	playing theremin
+pKUzj3ckXvI_000010	toilet flushing
+pNiB5w3JBVI_000003	spraying water
+pQrnDC-kPHk_000106	sharpen knife
+pRdi3oChUR4_000020	baltimore oriole calling
+pUMZEzdKmPM_000136	owl hooting
+pVJY1Q137cw_000681	cat purring
+pX_Sg3xDAUg_000000	people burping
+p_KsZsJwH0w_000555	sharpen knife
+pdzAs6Be2sY_000139	people gargling
+piYKrS14dxA_000113	mynah bird singing
+pnFtPlslgGw_000019	plastic bottle crushing
+ppDvhlGr5nI_000003	golf driving
+ppLjxFk8C4M_000023	heart sounds, heartbeat
+pqDHX5R4sdg_000220	female singing
+pqElMm80SX8_000025	airplane flyby
+prq7EqBGWaY_000035	playing harpsichord
+psz3LAhSi9U_000001	yodelling
+pu9pO-rCzy4_000153	people farting
+pugRM2Nsnyo_000283	church bell ringing
+pukny4fvbOQ_000040	playing clarinet
+pxpIsajKD-Y_000042	reversing beeps
+pxpIsajKD-Y_000065	reversing beeps
+pyHJrlNMYwo_000350	sheep bleating
+pzixqhh0xG4_000175	golf driving
+q0Hz09My-_E_000018	lions roaring
+q0R8KXxZOZM_000070	people farting
+q0lahEg486Y_000295	tractor digging
+q1oBXqEFXy4_000070	sloshing water
+q5fUdJoUrAE_000257	beat boxing
+q7cvNFoT9nQ_000027	lighting firecrackers
+qA-yeGwsVn4_000018	pheasant crowing
+qBDrrE6LnUo_000103	bird chirping, tweeting
+qBmsSZQ7HNg_000360	railroad car, train wagon
+qCcC7n2mOC0_000074	playing harpsichord
+qIcEYC46zmI_000087	playing cornet
+qJJEBEajF1M_000017	air conditioning noise
+qL-4fJyDGXc_000893	people eating noodle
+qNi5Xlf2ZVY_000510	people clapping
+qORUGCczq74_000042	swimming
+qRm5Yh3JPSg_000016	playing tambourine
+qRwun6pFuNA_000010	playing banjo
+qTRrHj-DNYc_000137	dinosaurs bellowing
+qW9b8qu_KrU_000180	lions growling
+qXFgtkhWLgM_000134	child singing
+q_ZMlkVS740_000222	playing congas
+qbmNcYH52eo_000516	striking pool
+qdl6t1bDb-8_000400	eating with cutlery
+qgv0riPveBQ_000030	bird chirping, tweeting
+qiw2I1oQIVQ_000057	playing snare drum
+qjBkiP7mBNI_000597	ripping paper
+qmjK_Wi0IK8_000080	people cheering
+qoPAdSFZ4f0_000370	chopping wood
+qpjOCvQEHdo_000080	people cheering
+qrNCI310T9Y_000018	chicken clucking
+qsj_OgZZDvQ_000080	tap dancing
+qsrNWdcjwwY_000320	female speech, woman speaking
+quF2HA3u2JY_000101	cupboard opening or closing
+quZSWDeSywg_000040	toilet flushing
+qv51EqZA8eE_000291	train horning
+qxeCxC_zpvU_000202	playing french horn
+r24KMnV5Rrk_000030	people running
+r42dJt0hxro_000010	gibbon howling
+r47N9mdOeXc_000030	playing violin, fiddle
+r4Zm5lEsI-M_000110	vehicle horn, car horn, honking
+r7e4wJy4NP8_000090	motorboat, speedboat acceleration
+r96LZqBtlwg_000050	dog whimpering
+r9uN-AltjDQ_000130	lawn mowing
+rAXnOxWHaLs_000030	playing french horn
+rAth9ueRqM4_000040	whale calling
+rD4zq3CvJSo_000130	people slapping
+rEdr-j9oAN0_000074	playing french horn
+rFA1GBcIGN4_000067	playing ukulele
+rFgrOflwKPg_000290	playing trombone
+rLuNw3Cm7rs_000024	lighting firecrackers
+rMDnGZU7jzE_000001	dog baying
+rQthEYYXM-k_000030	people sniggering
+rRP810El--s_000958	fire truck siren
+rSHvW5dGanw_000150	fireworks banging
+rSWPVWkAbec_000000	bee, wasp, etc. buzzing
+rTNSzUXd3wk_000180	playing double bass
+rVnkDOvLWm8_000180	cap gun shooting
+raz3OUu768k_000068	playing clarinet
+rfqqBv3eriU_000160	stream burbling
+rgdMDo5TBic_000355	playing squash
+rn381TUMxyE_000298	arc welding
+rs2FL8HJfGE_000030	people sniggering
+rwVhTlLcBO0_000099	playing erhu
+rx2lqMvj2Wo_000052	squishing water
+rz9PZZA04z8_000183	playing badminton
+s2QrQdxzLwQ_000074	playing glockenspiel
+s8zSSYQM0Tc_000127	footsteps on snow
+s9gzcUg_nlM_000030	playing drum kit
+sFTyeq295xU_000041	people humming
+sIHApNhq2Ik_000002	bird squawking
+sLEEurjCsAY_000051	typing on typewriter
+sLOjC8EWrHA_000070	driving buses
+sOg4MNTWx_0_000000	skateboarding
+sUHlRRyS2YM_000009	pigeon, dove cooing
+sUs8O9toO4M_000311	dinosaurs bellowing
+sXDJvBEzqjs_000000	dog bow-wow
+sYy0lPjLEXQ_000100	playing cymbal
+s_FLZ-ekB2A_000088	telephone bell ringing
+sa6B5XyFYIg_000040	playing bagpipes
+scm7r0uBepU_000467	mouse clicking
+smBHJiEPCRI_000030	duck quacking
+snbtH1P3MVA_000119	playing timbales
+snyzyJlTBbg_000003	dog baying
+surXSGAnpM0_000000	playing harmonica
+sxiVIGK5AEc_000010	people crowd
+syysO74ja30_000007	playing gong
+szQ-4VQQQsI_000020	railroad car, train wagon
+t0XoS_8YVP4_000728	magpie calling
+t2xJjZp1D1E_000030	dog growling
+t3YfjKEmei4_000080	race car, auto racing
+t3u3ykowlvs_000030	raining
+tD9rMw8YPBI_000030	child speech, kid speaking
+tDayTL0ivzU_000014	playing timbales
+tJChPvDD-hI_000035	parrot talking
+tLFNgY5NBMk_000001	playing bassoon
+tRw0KL6PMFU_000060	skateboarding
+tTePTFQV52M_000030	pig oinking
+tV0sIqEryIY_000037	wind chime
+tWDG6UsiG3s_000090	people babbling
+tYBxgXg8yxw_000046	woodpecker pecking tree
+tYzH5rkbuBQ_000000	frog croaking
+tm9rnG0455k_000010	skidding
+tuqcWxh_mdc_000012	baby crying
+twWBQjLyuxw_000014	bull bellowing
+u1nAQ6GgJ7Y_000154	playing volleyball
+u6AV24u4OMQ_000052	rope skipping
+u6c5tvrkqVA_000187	playing timbales
+u88CrTGAqbo_000000	lawn mowing
+uEPueBOV06U_000109	yodelling
+uGQ0TW02gBo_000004	frog croaking
+uI5eona1hc4_000000	elk bugling
+uIHnphQWVRA_000169	opening or closing drawers
+uIg0I7pAjvM_000030	race car, auto racing
+uJSDmIF4dhE_000260	driving buses
+uK0jcVxT-Pg_000030	driving buses
+uLm5oUt3XG4_000031	playing tabla
+uSmduC6gJxg_000050	rowboat, canoe, kayak rowing
+uWdgdlJqI2Y_000019	basketball bounce
+uWq8Q_cIEwE_000086	playing ukulele
+uZghS49MC1k_000180	skidding
+u_85N9h_cGs_000050	car passing by
+udVSYrFacsc_000072	playing cornet
+ugUyp_keJO4_000022	mouse clicking
+uiPC88KDlW4_000022	engine accelerating, revving, vroom
+unF6DdqG4l8_000050	people whistling
+upZ0sKmaZrI_000167	playing lacrosse
+uvUEfRqpEQU_000145	singing choir
+uyNyWLJIci8_000000	fire truck siren
+v5OdaMw5hhk_000030	playing snare drum
+vADdI9YTMRs_000243	playing timbales
+vJk_Jzr2YIs_000080	playing hammond organ
+vLLiaCDHSPY_000010	dog barking
+vUORRJqXp7A_000036	playing table tennis
+vXupVqDfK34_000116	cricket chirping
+v_cxwPhwaBQ_000000	people farting
+varD0b9CTgs_000020	people belly laughing
+vcwXIa-QB8A_000025	sailing
+vdXavSaj8-M_000070	playing accordion
+vgIgTWqXtms_000023	child singing
+vhqkCDgsuh4_000255	people booing
+vkA-v4DSriM_000229	playing tabla
+vktUwc0Cs7w_000170	playing clarinet
+vpAGr_NrM_w_000050	fireworks banging
+vzoQdjPITKw_000030	pigeon, dove cooing
+w-9xoB74oF0_000004	opening or closing car electric windows
+w-JaJ11OqQY_000345	people slurping
+w3kMt-zQ9t4_000215	playing table tennis
+w5T582MCzlY_000011	running electric fan
+w5vaBVSxgKg_000030	lawn mowing
+w8puug1pEUA_000170	stream burbling
+w9K_AmeWhlo_000071	fire crackling
+wAnqT37UgYY_000034	dog growling
+wEbJ-9cmSaE_000003	playing cornet
+wHdgExbL6dA_000034	playing badminton
+wOYLWY6UCu8_000262	playing ukulele
+wP-96GP6bsU_000000	vehicle horn, car horn, honking
+wT6-Isia2PQ_000149	child singing
+wTQ-1cd8owI_000181	dog bow-wow
+wUNpHu61l7Q_000190	male singing
+wVJ-S2zYxug_000040	playing drum kit
+wX4Ya3D20H8_000039	scuba diving
+wXsrff4No40_000237	playing hockey
+wYZc2-3ViXs_000155	civil defense siren
+wZj294W4RVU_000094	fire crackling
+w_yGhgrow38_000091	eletric blender running
+wdk-RmsGdyw_000310	driving buses
+wdlfOAR03iY_000000	playing glockenspiel
+we-ONoZIkWE_000018	dog howling
+wegIxELjtz4_000334	people eating noodle
+whIS2UodgLI_000002	gibbon howling
+wkwjx0oMAjw_000021	beat boxing
+wnW4qgQQg3g_000050	playing cello
+wrFyu2T1XOo_000000	hail
+wsHPe19Y9Nc_000081	electric shaver, electric razor shaving
+wtyuiWygNTc_000000	zebra braying
+wuAcPWyHMXo_000008	lions roaring
+wwQPX3zjV4s_000028	elk bugling
+x0_AiAhfeV0_000068	eagle screaming
+x0bbH2Tao_0_000000	dog howling
+x1Rt2zN-oXo_000000	dog growling
+x1bXQS9dUAc_000140	playing violin, fiddle
+x2uCcPNM6Nw_000030	pigeon, dove cooing
+x3cLaiaaF0M_000032	skiing
+x68R1rmvKgc_000060	female singing
+x6d8ytnWNDI_000045	barn swallow calling
+x8yymm3DtVA_000022	playing cello
+xK1vy_6H2VM_000010	scuba diving
+xMa1vAUhTfM_000429	ice cream truck, ice cream van
+xN_CePbfjVg_000004	playing bass drum
+xPIhTw0fbzI_000010	train horning
+xQaYumd1O48_000004	lions growling
+xS4brO1qu0g_000591	playing hockey
+xUCKcoE3K6Q_000313	lip smacking
+xVDGIF1pFvQ_000030	driving buses
+xVEXWvj0iWo_000060	rowboat, canoe, kayak rowing
+xWBMt4fI95M_000063	scuba diving
+xWgd4OMcKbs_000263	people nose blowing
+xY9mlbn2IhY_000000	people burping
+xYAHwbhWEgM_000030	playing violin, fiddle
+xbNNxwGRG20_000062	cattle, bovinae cowbell
+xdUbCcEbipM_000290	people crowd
+xeS25F6uHic_000162	airplane flyby
+xetF74UUCGk_000001	ice cream truck, ice cream van
+xf0cheS5wFM_000090	playing piano
+xfT0HF1Pbxk_000003	playing sitar
+xg_3Uas3z40_000240	skateboarding
+xibFeibkfWM_000036	alligators, crocodiles hissing
+xj0Xi47RC88_000200	lawn mowing
+xkUzsvSImy4_000306	people eating crisps
+xm0N3HXnSWc_000361	rope skipping
+xoViga6dJa4_000141	playing steelpan
+xocKilOzrb4_000065	reversing beeps
+xq5kMmAFYx8_000030	playing double bass
+xqv96EPg7so_000200	railroad car, train wagon
+xtvQjd6cwC4_000040	playing bagpipes
+y3TRiYwDbHo_000287	playing oboe
+y6wsRU2aNx4_000040	railroad car, train wagon
+y95ml0IYGr4_000440	chainsawing trees
+yA_63YfQ034_000022	dog growling
+yBwMu2NueR0_000284	rapping
+yE_SP127xy8_000010	people crowd
+yEfhYsMd1yc_000006	playing double bass
+yH3PJfYi_gs_000109	car engine starting
+yJGtoH8INnA_000084	tapping guitar
+yJN5_1tfqXo_000075	magpie calling
+yMMmjb3BRi0_000030	dog bow-wow
+yOhdod2Kg40_000210	playing bassoon
+yPJiPWkeT3U_000254	playing gong
+yPUYU6t3rwo_000370	bee, wasp, etc. buzzing
+yQzzdP-4iBU_000002	planing timber
+yUL9UefoANU_000128	tractor digging
+yVzIaZzLH38_000130	bee, wasp, etc. buzzing
+yYPNrg-s-NI_000060	child singing
+ybnXdQfSNZs_000001	police radio chatter
+ycN30BUfzeo_000070	playing clarinet
+ygOHZ_55jME_000174	electric shaver, electric razor shaving
+yjyZgzYuuSQ_000089	cat purring
+yo5I2MTqv9E_000030	playing marimba, xylophone
+ywD_am3uZh8_000020	splashing water
+ywYLMe6y-S0_000040	playing piano
+z9CCSNKepA8_000537	striking pool
+z9crgUIWcmA_000000	dog barking
+zBgR_gj8NGg_000083	striking pool
+zGbJAz-3Ao8_000070	playing banjo
+zGn9k6j8kVo_000049	rope skipping
+zILE3kr9nIU_000030	mouse pattering
+zJPgE79wkE4_000000	playing tennis
+zMKJFnBr1Gw_000013	reversing beeps
+zPlyG_ryFpg_000006	sliding door
+zRU8A0m9Op8_000145	driving snowmobile
+zVCqTRlc7NU_000020	fire truck siren
+zYPY3Fh1Xjo_000000	skidding
+zcZ0WVQ8t8s_000210	splashing water
+zhPLdAMVAuo_000257	church bell ringing
+zl6hP51zURM_000075	playing oboe
+zlt2EGxum58_000174	bouncing on trampoline
+zmSPCArJHB0_000190	bird squawking
+zpqGedo-jm4_000043	cell phone buzzing
+zrKMC4fAKp0_000202	playing cello
+zsnU7rt_Qq0_000005	baby laughter
+zw7dTh-Lx3o_000074	canary calling
+zzP5qr-ZxHY_000199	people marching
+zzftU8z4aOI_000230	skateboarding

training/combine_latents.py

@@ -0,0 +1,58 @@
+import os 
+import torch 
+from tqdm import tqdm
+import pandas as pd
+import tensordict as td
+
+this_latent_dir = './training/vgg_output_44k/video-latents/train'
+output_dir = './training/vgg_output_44k/memmap'
+split = 'train'
+
+os.makedirs(output_dir, exist_ok=True)
+
+print('Extraction done. Combining the results.')
+used_id = set()
+list_of_ids_and_labels = []
+output_data = {
+    'mean': [],
+    'std': [],
+    'clip_features': [],
+    'sync_features': [],
+    'text_features': [],
+}
+
+for t in tqdm(sorted(os.listdir(this_latent_dir))):
+    #print(t)
+    
+    if t.split('.')[-1] != 'pth':
+                    continue
+            
+    data = torch.load(os.path.join(this_latent_dir, t), weights_only=True)
+    bs = len(data['id'])
+
+    for bi in range(bs):
+        this_id = data['id'][bi]
+        this_caption = data['caption'][bi]
+        if this_id in used_id:
+            print('Duplicate id:', this_id)
+            continue
+
+        list_of_ids_and_labels.append({'id': this_id, 'label': this_caption})
+        used_id.add(this_id)
+        output_data['mean'].append(data['mean'][bi])
+        output_data['std'].append(data['std'][bi])
+        output_data['clip_features'].append(data['clip_features'][bi])
+        output_data['sync_features'].append(data['sync_features'][bi])
+        output_data['text_features'].append(data['text_features'][bi])
+
+
+#output_dir.mkdir(parents=True, exist_ok=True)
+output_df = pd.DataFrame(list_of_ids_and_labels)
+output_df.to_csv(os.path.join(output_dir, f'vgg-{split}.tsv'), sep='\t', index=False)
+
+print(f'Output: {len(output_df)}')
+
+output_data = {k: torch.stack(v) for k, v in output_data.items()}
+td.TensorDict(output_data).memmap_(os.path.join(output_dir, f'vgg-{split}'))
+
+print(f'Finished combining {split} results')

training/extract_audio_training_latents.py

@@ -0,0 +1,179 @@
+import logging
+import os
+from argparse import ArgumentParser
+from pathlib import Path
+
+import pandas as pd
+import tensordict as td
+import torch
+import torch.distributed as distributed
+import torch.nn.functional as F
+from open_clip import create_model_from_pretrained
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from mmaudio.data.data_setup import error_avoidance_collate
+from mmaudio.data.extraction.wav_dataset import WavTextClipsDataset
+from mmaudio.ext.autoencoder import AutoEncoderModule
+from mmaudio.ext.mel_converter import get_mel_converter
+
+log = logging.getLogger()
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+local_rank = int(os.environ['LOCAL_RANK'])
+world_size = int(os.environ['WORLD_SIZE'])
+
+# 16k
+SAMPLE_RATE = 16_000
+NUM_SAMPLES = 16_000 * 8
+tod_vae_ckpt = './ext_weights/v1-16.pth' # vae for audio
+bigvgan_vocoder_ckpt = './ext_weights/best_netG.pt' # vocoder 
+mode = '16k'
+
+# 44k
+# SAMPLE_RATE = 44100
+# NUM_SAMPLES = 353280
+# tod_vae_ckpt = './ext_weights/v1-44.pth'
+# bigvgan_vocoder_ckpt = None
+# mode = '44k'
+
+
+def distributed_setup():
+    distributed.init_process_group(backend="nccl")
+    local_rank = distributed.get_rank()
+    world_size = distributed.get_world_size()
+    print(f'Initialized: local_rank={local_rank}, world_size={world_size}')
+    return local_rank, world_size
+
+
+@torch.inference_mode()
+def main():
+    distributed_setup()
+
+    parser = ArgumentParser()
+    parser.add_argument('--data_dir', type=Path, default='./training/example_audios/')
+    parser.add_argument('--captions_tsv', type=Path, default='./training/example_audio.tsv')
+    parser.add_argument('--clips_tsv', type=Path, default='./training/example_output/clips.tsv')
+    parser.add_argument('--latent_dir',
+                        type=Path,
+                        default='./training/example_output/audio-latents')
+    parser.add_argument('--output_dir',
+                        type=Path,
+                        default='./training/example_output/memmap/audio-example')
+    parser.add_argument('--batch_size', type=int, default=32)
+    parser.add_argument('--num_workers', type=int, default=8)
+    args = parser.parse_args()
+
+    data_dir = args.data_dir
+    captions_tsv = args.captions_tsv
+    clips_tsv = args.clips_tsv
+    latent_dir = args.latent_dir
+    output_dir = args.output_dir
+    batch_size = args.batch_size
+    num_workers = args.num_workers
+
+    clip_model = create_model_from_pretrained('hf-hub:apple/DFN5B-CLIP-ViT-H-14-384',
+                                              return_transform=False).eval().cuda()
+
+    # a hack to make it output last hidden states
+    def new_encode_text(self, text, normalize: bool = False):
+        cast_dtype = self.transformer.get_cast_dtype()
+
+        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.to(cast_dtype)
+        x = self.transformer(x, attn_mask=self.attn_mask)
+        x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
+        return F.normalize(x, dim=-1) if normalize else x
+
+    clip_model.encode_text = new_encode_text.__get__(clip_model)
+
+    tod = AutoEncoderModule(vae_ckpt_path=tod_vae_ckpt,
+                            vocoder_ckpt_path=bigvgan_vocoder_ckpt,
+                            mode=mode).eval().cuda()
+    mel_converter = get_mel_converter(mode).eval().cuda()
+
+    dataset = WavTextClipsDataset(data_dir,
+                                  captions_tsv=captions_tsv,
+                                  clips_tsv=clips_tsv,
+                                  sample_rate=SAMPLE_RATE,
+                                  num_samples=NUM_SAMPLES,
+                                  normalize_audio=True,
+                                  reject_silent=True)
+    dataloader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            shuffle=False,
+                            num_workers=num_workers,
+                            collate_fn=error_avoidance_collate)
+    latent_dir.mkdir(exist_ok=True, parents=True)
+
+    # extraction
+    for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
+        ids = batch['id']
+        waveforms = batch['waveform'].cuda()
+        tokens = batch['tokens'].cuda()
+
+        text_features = clip_model.encode_text(tokens, normalize=True)
+        mel = mel_converter(waveforms)
+        dist = tod.encode(mel)
+
+        a_mean = dist.mean.detach().cpu().transpose(1, 2)
+        a_std = dist.std.detach().cpu().transpose(1, 2)
+        text_features = text_features.detach().cpu()
+
+        ids = [id for id in ids]
+        captions = [caption for caption in batch['caption']]
+
+        data = {
+            'id': ids,
+            'caption': captions,
+            'mean': a_mean,
+            'std': a_std,
+            'text_features': text_features,
+        }
+
+        torch.save(data, latent_dir / f'r{local_rank}_{i:05d}.pth')
+
+    distributed.barrier()
+    # combine the results
+    if local_rank == 0:
+        print('Extraction done. Combining the results.')
+
+        list_of_ids_and_labels = []
+        output_data = {
+            'mean': [],
+            'std': [],
+            'text_features': [],
+        }
+
+        latents = sorted(os.listdir(latent_dir))
+        latents = [l for l in latents if l.endswith('.pth')]
+        for t in tqdm(latents):
+            data = torch.load(latent_dir / t, weights_only=True)
+            bs = len(data['id'])
+
+            for bi in range(bs):
+                this_id = data['id'][bi]
+                this_caption = data['caption'][bi]
+
+                list_of_ids_and_labels.append({'id': this_id, 'caption': this_caption})
+                output_data['mean'].append(data['mean'][bi])
+                output_data['std'].append(data['std'][bi])
+                output_data['text_features'].append(data['text_features'][bi])
+
+        output_df = pd.DataFrame(list_of_ids_and_labels)
+        output_dir.mkdir(exist_ok=True, parents=True)
+        output_name = output_dir.stem
+        output_df.to_csv(output_dir.parent / f'{output_name}.tsv', sep='\t', index=False)
+
+        print(f'Output: {len(output_df)}')
+
+        output_data = {k: torch.stack(v) for k, v in output_data.items()}
+        td.TensorDict(output_data).memmap_(output_dir)
+
+
+if __name__ == '__main__':
+    main()
+    distributed.destroy_process_group()

training/extract_video_training_latents.py

@@ -0,0 +1,204 @@
+import logging
+import os
+from argparse import ArgumentParser
+from datetime import timedelta
+from pathlib import Path
+
+import pandas as pd
+import tensordict as td
+import torch
+import torch.distributed as distributed
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+
+from mmaudio.data.data_setup import error_avoidance_collate
+from mmaudio.data.extraction.vgg_sound import VGGSound
+from mmaudio.model.utils.features_utils import FeaturesUtils
+from mmaudio.utils.dist_utils import local_rank, world_size
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+# for the 16kHz model
+#SAMPLING_RATE = 16000
+#DURATION_SEC = 8.0 # todo #4.0  
+#NUM_SAMPLES = 128000 #64000 #128000 # todo
+#vae_path = './ext_weights/v1-16.pth' # VAE for visual input
+#bigvgan_path = './ext_weights/best_netG.pt' # vocoder 
+#mode = '16k' # {16k, 44.1k}
+
+# for the 44.1kHz model
+SAMPLING_RATE = 44100
+DURATION_SEC = 8.0
+NUM_SAMPLES = 353280 # 352800? 
+vae_path = './ext_weights/v1-44.pth'
+bigvgan_path = None
+mode = '44k'
+
+synchformer_ckpt = './ext_weights/synchformer_state_dict.pth'
+
+# per-GPU
+BATCH_SIZE = 8 #16
+NUM_WORKERS = 8 #16
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+data_cfg = {
+     #'train': {
+     #    'root': '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/datasets/vggsound/videos',
+     #    'subset_name': './sets/vgg-train-filtered-caption.tsv', #'./filter_dataset/filtered_vggsound/train_filter_av_align.tsv', #'./sets/vgg-train-filtered.tsv',
+     #    'normalize_audio': True,
+     #},
+     #'test': {
+     #    'root': '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/datasets/vggsound/videos',
+     #    'subset_name': './sets/vgg-test-filtered-caption.tsv', #'./filter_dataset/filtered_vggsound/test_filter_av_align.tsv', #'./sets/vgg-test-filtered.tsv',
+     #    'normalize_audio': False,
+     #},
+     'val': {
+        'root': '/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/gaopeng/public/kwang/datasets/vggsound/videos',
+         'subset_name': './sets/vgg-val-filtered-caption.tsv', #'./filter_dataset/filtered_vggsound/val_filter_av_align.tsv', # './sets/vgg-val-filtered.tsv',
+         'normalize_audio': False,
+     },
+}
+
+
+def distributed_setup():
+    distributed.init_process_group(backend="nccl", timeout=timedelta(hours=1))
+    log.info(f'Initialized: local_rank={local_rank}, world_size={world_size}')
+    return local_rank, world_size
+
+
+def setup_dataset(split: str):
+    dataset = VGGSound(
+        data_cfg[split]['root'],
+        tsv_path=data_cfg[split]['subset_name'],
+        sample_rate=SAMPLING_RATE,
+        duration_sec=DURATION_SEC,
+        audio_samples=NUM_SAMPLES,
+        normalize_audio=data_cfg[split]['normalize_audio'],
+    )
+    sampler = DistributedSampler(dataset, rank=local_rank, shuffle=False)
+    loader = DataLoader(dataset,
+                        batch_size=BATCH_SIZE,
+                        num_workers=NUM_WORKERS,
+                        sampler=sampler,
+                        drop_last=False,
+                        collate_fn=error_avoidance_collate)
+
+    return dataset, loader
+
+
+@torch.inference_mode()
+def extract():
+    # initial setup
+    distributed_setup()
+
+    parser = ArgumentParser()
+    parser.add_argument('--latent_dir',
+                        type=Path,
+                        default='./training/vgg_caption_filtered_output_44k/video-latents')
+    parser.add_argument('--output_dir', type=Path, default='./training/vgg_caption_filtered_output_44k/memmap')
+    args = parser.parse_args()
+
+    latent_dir = args.latent_dir
+    output_dir = args.output_dir
+
+    # cuda setup
+    torch.cuda.set_device(local_rank)
+    feature_extractor = FeaturesUtils(tod_vae_ckpt=vae_path,
+                                      enable_conditions=True,
+                                      bigvgan_vocoder_ckpt=bigvgan_path,
+                                      synchformer_ckpt=synchformer_ckpt,
+                                      mode=mode).eval().cuda()
+
+    for split in data_cfg.keys():
+        print(f'Extracting latents for the {split} split')
+        this_latent_dir = latent_dir / split
+        this_latent_dir.mkdir(parents=True, exist_ok=True)
+
+        # setup datasets
+        dataset, loader = setup_dataset(split)
+        log.info(f'Number of samples: {len(dataset)}')
+        log.info(f'Number of batches: {len(loader)}')
+
+        for curr_iter, data in enumerate(tqdm(loader)):
+            output = {
+                'id': data['id'],
+                'caption': data['caption'],
+            }
+
+            audio = data['audio'].cuda()
+            dist = feature_extractor.encode_audio(audio)
+            output['mean'] = dist.mean.detach().cpu().transpose(1, 2)
+            output['std'] = dist.std.detach().cpu().transpose(1, 2)
+
+            clip_video = data['clip_video'].cuda()
+            clip_features = feature_extractor.encode_video_with_clip(clip_video)
+            output['clip_features'] = clip_features.detach().cpu()
+
+            sync_video = data['sync_video'].cuda()
+            sync_features = feature_extractor.encode_video_with_sync(sync_video)
+            output['sync_features'] = sync_features.detach().cpu()
+
+            caption = data['caption']
+            text_features = feature_extractor.encode_text(caption)
+            output['text_features'] = text_features.detach().cpu()
+
+            torch.save(output, this_latent_dir / f'r{local_rank}_{curr_iter}.pth')
+
+        distributed.barrier()
+
+        # combine the results
+        if local_rank == 0:
+            print('Extraction done. Combining the results.')
+
+            used_id = set()
+            list_of_ids_and_labels = []
+            output_data = {
+                'mean': [],
+                'std': [],
+                'clip_features': [],
+                'sync_features': [],
+                'text_features': [],
+            }
+
+            for t in tqdm(sorted(os.listdir(this_latent_dir))):
+                
+                if t.split('.')[-1] != 'pth':
+                    continue
+                
+                data = torch.load(this_latent_dir / t, weights_only=True)
+                bs = len(data['id'])
+                
+                for bi in range(bs):
+                    this_id = data['id'][bi]
+                    this_caption = data['caption'][bi]
+                    if this_id in used_id:
+                        print('Duplicate id:', this_id)
+                        continue
+
+                    list_of_ids_and_labels.append({'id': this_id, 'label': this_caption})
+                    used_id.add(this_id)
+                    output_data['mean'].append(data['mean'][bi])
+                    output_data['std'].append(data['std'][bi])
+                    output_data['clip_features'].append(data['clip_features'][bi])
+                    output_data['sync_features'].append(data['sync_features'][bi])
+                    output_data['text_features'].append(data['text_features'][bi])
+
+            output_dir.mkdir(parents=True, exist_ok=True)
+            output_df = pd.DataFrame(list_of_ids_and_labels)
+            output_df.to_csv(output_dir / f'vgg-{split}.tsv', sep='\t', index=False)
+
+            print(f'Output: {len(output_df)}')
+
+            output_data = {k: torch.stack(v) for k, v in output_data.items()}
+            td.TensorDict(output_data).memmap_(output_dir / f'vgg-{split}')
+            
+            print(f'Finished combining {split} results')
+
+
+if __name__ == '__main__':
+    extract()
+    distributed.destroy_process_group()

training/partition_clips.py

@@ -0,0 +1,65 @@
+import argparse
+import os
+from pathlib import Path
+
+import pandas as pd
+import torchaudio
+from tqdm import tqdm
+
+min_length_sec = 8.1
+max_segments_per_clip = 5
+
+parser = argparse.ArgumentParser(description='Process audio clips.')
+parser.add_argument('--data_path',
+                    type=Path,
+                    help='Path to the directory containing audio files',
+                    default='./training/example_audios')
+parser.add_argument('--output_path',
+                    type=Path,
+                    help='Path to the output tsv file',
+                    default='./training/example_output/clips.tsv')
+parser.add_argument('--start', type=int, help='Start index for processing files', default=0)
+parser.add_argument('--end', type=int, help='End index for processing files', default=-1)
+args = parser.parse_args()
+
+data_path = args.data_path
+output_path = args.output_path
+start = args.start
+end = args.end
+
+output_data = []
+
+blacklisted = 0
+if end == -1:
+    end = len(os.listdir(data_path))
+audio_files = sorted(os.listdir(data_path))[start:end]
+print(f'Processing {len(audio_files)} files from {start} to {end}')
+
+for audio_file in tqdm(audio_files):
+    audio_file_path = data_path / audio_file
+    audio_name = audio_file_path.stem
+
+    waveform, sample_rate = torchaudio.load(audio_file_path)
+
+    # waveform: (1/2) * length
+    if waveform.shape[1] < sample_rate * min_length_sec:
+        continue
+
+    # try to partition the audio into segments, each with length of min_length_sec
+    segment_length = int(sample_rate * min_length_sec)
+    total_length = waveform.shape[1]
+    num_segments = min(max_segments_per_clip, total_length // segment_length)
+    if num_segments > 1:
+        segment_interval = (total_length - segment_length) // (num_segments - 1)
+    else:
+        segment_interval = 0
+
+    for i in range(num_segments):
+        start_sample = i * segment_interval
+        end_sample = start_sample + segment_length
+        audio_id = f'{audio_name}_{i}'
+        output_data.append((audio_id, audio_name, start_sample, end_sample))
+
+output_path.parent.mkdir(parents=True, exist_ok=True)
+output_df = pd.DataFrame(output_data, columns=['id', 'name', 'start_sample', 'end_sample'])
+output_df.to_csv(output_path, index=False, sep='\t')