From 5414024f1225bf8d858046c709f0b35fa743274f Mon Sep 17 00:00:00 2001
From: imad-eddine <imad-eddine.el-kaoui@imt-atlantique.net>
Date: Tue, 11 Aug 2020 15:20:24 +0200
Subject: [PATCH] Does the audio tagging per batch.
---
batched_tag_silentcities.py | 215 ++++++++++++++++++++++++++++++++++++
1 file changed, 215 insertions(+)
create mode 100644 batched_tag_silentcities.py
diff --git a/batched_tag_silentcities.py b/batched_tag_silentcities.py
new file mode 100644
index 0000000..deca2f7
--- /dev/null
+++ b/batched_tag_silentcities.py
@@ -0,0 +1,215 @@
+## Author : Nicolas Farrugia, March 2020
+## Silent City Project
+
+import torch
+import torchvision.transforms as transforms
+import utils
+import torch.nn as nn
+from torch.nn import functional as F
+from importlib import reload
+from tqdm import tqdm
+import os
+import sys
+import numpy as np
+from audioset_tagging_cnn.inference import audio_tagging_batched
+from audioset_tagging_cnn.inference import chunks
+from datetime import time
+import pandas as pd
+from librosa.core import get_duration,load
+import soundfile as sf
+import time as time_
+from audioset_tagging_cnn.resample import down_sample
+
+import datetime
+
+
+import argparse
+from ecoacoustics import compute_NDSI,compute_NB_peaks,compute_ACI
+
+
+parser = argparse.ArgumentParser(description='Silent City Audio Tagging with pretrained LeeNet11 on Audioset')
+parser.add_argument('--length', default=10, type=int, help='Segment length')
+parser.add_argument('--nbcat', default=3, type=int, help='Maximum number of categories for writing annotated csv')
+parser.add_argument('--folder', default=None, type=str, help='Path to folder with wavefiles, will walk through subfolders')
+parser.add_argument('--file', default=None, type=str, help='Path to file to process')
+parser.add_argument('--verbose', action='store_true', help='Verbose (default False = nothing printed)')
+parser.add_argument('--overwrite', action='store_true', help='Overwrite files (default False)')
+parser.add_argument('--out', default='output.xz', type=str, help='Output file (pandas pickle), default is output.xz')
+parser.add_argument('--nocuda', action='store_false', help='Do not use the GPU for acceleration')
+
+args = parser.parse_args()
+
+if args.folder is None:
+
+ if args.file is None:
+ raise(AttributeError("Must provide either a file or a folder"))
+
+verbose = args.verbose
+Overwrite = args.overwrite
+
+
+
+all_files = []
+
+if args.folder is None:
+ filelist = [args.file]
+else:
+ filelist = []
+
+ for root, dirs, files in os.walk(args.folder, topdown=False):
+ for name in files:
+ if name[-3:].casefold() == 'wav':
+ filelist.append(os.path.join(root, name))
+ #print(currentvid)
+
+if verbose:
+ print(filelist)
+
+
+
+nbcat = args.nbcat
+
+#checkpoint_path='./LeeNet11_mAP=0.266.pth'
+checkpoint_path = 'ResNet22_mAP=0.430.pth'
+
+if not(os.path.isfile(checkpoint_path)):
+ raise(FileNotFoundError("Pretrained model {} wasn't found, did you download it ?".format(checkpoint_path)))
+
+if not(Overwrite):
+ remaining_filelist = []
+ for wavfile in filelist:
+ print("wavfile : {}".format(wavfile))
+ pdfile = (wavfile[:-3] + 'xz')
+ if (os.path.isfile(pdfile)):
+
+ print(("File {} has already been processed ; loading ".format(wavfile)))
+
+ Df = pd.read_pickle(pdfile)
+
+ all_files.append(Df)
+ continue
+ else:
+ remaining_filelist.append(wavfile)
+
+
+ filelist = remaining_filelist
+
+if len(filelist) == 0:
+ print("All files have already been processed")
+
+else:
+ nbsec = args.length
+ batch_size = 36 # Optimum btach size for our memory, global sample rate and segment duration.
+ chunk = [(chunks(filelist,batch_size))]
+ filelist = list(chunk[0])
+ print("filelist : {}".format(filelist))
+ nb_batch = len(filelist)
+ print("nb_batch : {}".format(nb_batch))
+
+
+ t_2 = time_.time()
+ for batch in tqdm(filelist):
+
+ len_batch = len(batch)
+ print("len batch : {}".format(len_batch))
+
+ try:
+ wavfile = batch[0]
+ print("wavfile : {}".format(wavfile))
+ _,meta = utils.read_audio_hdr(wavfile,verbose)
+
+ beg_seg = 0
+ end_seg = np.floor(get_duration(filename=wavfile))
+
+ allpreds = []
+ onsets = []
+
+ audioset_proba = []
+ n=0
+
+ all_seg = []
+ with torch.no_grad():
+ for curstart in (np.arange(beg_seg,end_seg,nbsec)):
+ start = curstart
+
+ onsets.append(curstart)
+
+ # Make predictions for audioset
+ t_0 = time_.time()
+ clipwise_output, labels,sorted_indexes,embedding = audio_tagging_batched(batch,checkpoint_path,offset=curstart,duration=nbsec,usecuda=args.nocuda)
+ t_1 = time_.time()
+ print("batched audio_tagging_time : {}".format(t_1 - t_0))
+
+ for j,wavfile in enumerate(batch):
+
+
+
+
+ ### Calculate Eco acoustic indices
+ print("batched audio_tagging_time : {}".format(t_1 - t_0))
+
+ (waveform, sr) = load(wavfile, sr=None, mono=True,offset=curstart,duration=nbsec)
+
+ ndsi = compute_NDSI(waveform,sr)
+ nbpeaks = compute_NB_peaks(waveform,sr)
+
+ aci,_ = compute_ACI(waveform,sr)
+
+ # Print audio tagging top probabilities
+ #print("labels sorted : {}".format(np.array(labels)[sorted_indexes[j][0]]))
+ #break
+ texttagging = ''
+ for k in range(nbcat):
+ texttagging += np.array(labels)[sorted_indexes[j][k]]
+ proba = 100 * clipwise_output[j][sorted_indexes[j][k]]
+ texttagging += ' ({0:2.1f}%)'.format(proba)
+ texttagging += ', '
+ texttagging = texttagging[:-2]
+ print("text tagging : {}".format(texttagging))
+
+
+ # AudioSet
+ audioset_proba.append(clipwise_output[j])
+ print("audioset proba : {}".format(audioset_proba))
+ #audioset_fm.append(embedding)
+
+ annotation_str = "{tagging}".format(tagging=texttagging)
+ print("annotation_str : {}".format(annotation_str))
+
+
+ if verbose:
+ print(annotation_str)
+
+ _,meta = utils.read_audio_hdr(wavfile,verbose)
+ current_dt = meta['datetime']
+
+ delta=datetime.timedelta(seconds=int(curstart))
+
+ onset_dt = current_dt + delta
+
+ curdict = dict(datetime=onset_dt,time=onset_dt.time(),file=wavfile,id=meta['id'],onsets=curstart,label=annotation_str,date=onset_dt.date(),probas=clipwise_output[j],embedding=embedding[j],ndsi=ndsi,nbpeaks=nbpeaks,aci=aci)
+ print("curdict : {}".format(curdict))
+ all_seg.append(curdict)
+
+ df_allseg = pd.DataFrame(all_seg)
+ for j, wavfile in enumerate(batch):
+ pdfile = (wavfile[:-3] + 'xz')
+ df_forannot = df_allseg[df_allseg['file'] == wavfile]
+ df_forannot.to_pickle(pdfile)
+ print("df_forannot : {}".format(df_forannot))
+ all_files.append(df_forannot)
+ print("all_files : {}".format(all_files))
+
+ except Exception as e:
+ print('Error with file {}'.format(wavfile))
+ raise(e)
+
+
+ df = pd.concat(all_files)
+
+ df = df.sort_values(by='datetime')
+
+
+ df.to_pickle(args.out)
+ t_3 = time_.time()
+ print("batched_tag_silentcities time : {}".format(t_3 - t_2))
\ No newline at end of file
--
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