Download Audioset Dataset [CRACKED]

The ontology and dataset construction are described in more detail in our ICASSP 2017 paper. You can contribute to the ontology at our GitHub repository. The dataset and machine-extracted features are available at the download page.

The AudioSet dataset is a large-scale collection of human-labeled 10-second sound clips drawn from YouTube videos. To collect all our data we worked with human annotators who verified the presence of sounds they heard within YouTube segments. To nominate segments for annotation, we relied on YouTube metadata and content-based search.

Download Audioset Dataset

Download 🔥 https://urllie.com/2y3yCQ 🔥

The sound events in the dataset consist of a subset of the AudioSet ontology. You can learn more about the dataset construction in our ICASSP 2017 paper. Explore the dataset annotations by sound class below.

The dataset is made available by Google Inc. under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, while the ontology is available under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

The dataset is divided in three disjoint sets: a balanced evaluation set, a balanced training set, and an unbalanced training set. In the balanced evaluation and training sets, we strived for each class to have the same number of examples. The unbalanced training set contains the remainder of annotated segments.

Due to the size of the dataset, we have been rerating only up to 1,000 segments for each class (sampled independently per label). This means that for the majority of the classes all segments of eval and balanced_train are, or will, get rerated. At the same time, for classes with substantially more than 1,000 segments in total, the quality in unbalanced_train dataset can be substantially different from the balanced evaluation and train datasets.

A text file containing videos that have been labeled in the rerating task. This file consists of one YouTube video ID per line. Any segment in the dataset with these YouTube IDs will only contain rerated labels.

Audioset is an audio event dataset, which consists of over 2M human-annotated 10-second video clips. These clips are collected from YouTube, therefore many of which are in poor-quality and contain multiple sound-sources. A hierarchical ontology of 632 event classes is employed to annotate these data, which means that the same sound could be annotated as different labels. For example, the sound of barking is annotated as Animal, Pets, and Dog. All the videos are split into Evaluation/Balanced-Train/Unbalanced-Train set.

AudioSet Dataset is developed by the Google Sound and Video Understanding team. The core member of the AudioSet Dataset is Jort Florent Gemmeke, Daniel P.W.Ellis, Dylan, Aren, Manoj Plakal, Marwin Ritter, Shawn Hershey, and two more members of the team. There are other twelve contributors to AudioSet DataSet who help to build a pipeline for the data storage in the form Youtube_url Id, start_time, end_time, and other classes. AudioSet Dataset has more than 600 classes of annotated sound, 6000 hours of audio, and 2,084,320 million YouTube videos annotated videos and containing 527 labels. Each video has a 10 sec sounds clip extracted from Youtube Videos in different classes for the training and testing dataset.

After selecting the train horn, it comes to our required dataset, which is best suitable for us to build horn detection. Choose this and go furthermore one step to know about the Dataset. Select, as shown in the figure below.

After selecting the train horn now, you will get the overall details of the Dataset and their number of videos available, and the total number of each part of the dataset. Hour duration is sufficient to train the model.

We have learned about the AudioSet dataset, how we can download it from the source. In different file formats, AudioSet dataset creator and their researcher.AudioSet Ontology uses the case to choose the right dataset and Implementation of model in PyTorch.Much other real application is used in daily life using AudioSet Datasets.

Audioset is a large scale tag_hash_107_______________ [2] dataset for sound events, Audioset. It contains a total of 527 sound events for which labeled videos from Youtube are provided. The maximum duration of the recordings is 10 seconds and a large portion of the example recordings are of 10 seconds duration. However, there are a considerable number of recordings with smaller duration. In this paper, we worked with the balanced train for training the models and Eval set for evaluation. Balanced train set provides at least 59 examples for each sound event. It has a total of around 22, 000 recordings. Eval set is the full evaluation set of Audioset. It consist of a total of around 20, 000 example recordings, again with at least 59 examples per class Audioset is Multi-label dataset. On an average each recording example contains 2.7 classes [1]. Due to multi-label nature of recordings, the actual number of examples for several classes is higher. The class wise distribution of labels for both balanced train and eval set is shown in the figures below.

Fig 1. - Number of examples for different sound events in balanced ( Red ) and eval (Green) sets Fig 1. - Number of Events vs Number of Examples (Distribution of examples and events) ( Red ) and eval (Green) sets Audioset ResultsAs shown in paper, the proposed weak label CNN approach (\(\mathcal{N}_S\)) outperforms a network trained under strong label assumption (\(\mathcal{N}_S^{slat}\)). Moreover, \(\mathcal{N}_S\) works smoothly with recordings of variable lengths and is computationally more efficient by over 30 % during training as well as test (See paper for comparison).

The dataset is divided in three disjoint sets: a balanced evaluation set, a balanced training set, and an unbalanced training set. In the balanced evaluation and training sets, each class has the same number of examples. The unbalanced training set contains the remainder of annotated segments.

_audioset/youtube_corpus/v1/csv/eval_segments.csv contains 20,383 segments from distinct videos, providing at least 59 examples for each of the 527 sound classes that are used. Because of label co-occurrence, many classes have more examples.

_audioset/youtube_corpus/v1/csv/balanced_train_segments.csv contains 22,176 segments from distinct videos chosen with the same criteria: providing at least 59 examples per class with the fewest number of total segments.

Manually download the tar.gz file from one of (depending on region):storage.googleapis.com/us_audioset/youtube_corpus/v1/features/features.tar.gzstorage.googleapis.com/eu_audioset/youtube_corpus/v1/features/features.tar.gzstorage.googleapis.com/asia_audioset/youtube_corpus/v1/features/features.tar.gz

The initial AudioSet release included 128-dimensional embeddings of each AudioSet segment produced from a VGG-like audio classification model that was trained on a large YouTube dataset (a preliminary version of what later became YouTube-8M).

You have to specify which labels you want as the target of the data set bythe names of the corresponding columns in the CSV file. If you want toselect one of those columns the target is returned directly in itscorresponding data type or you can specify a list of columns and the dataset will return a dictionary containing the targets.

Extracting everything else from the tfrecord files works fine (I could extract the keys: video_id, start_time_seconds, end_time_seconds, labels), but the actual embeddings needed for training do not seem to be there at all. When I iterate over the contents of any tfrecord file from the dataset, only the four keys video_id, start_time_seconds, end_time_seconds, and labels, are printed.

Google's AudioSet consistently reformattedDuring my work with Google's AudioSet( )I encountered some problems due to the fact that Weak ( ) and Strong ( _strong.html) versions of the dataset used different csv formatting for the data, and that also labels used in the two datasets are different ( ) and also presented in files with different formatting.This dataset reformatting aims to unify the formats of the datasets so that it is possibleto analyse them in the same pipelines, and also make the dataset files compatiblewith psds_eval, dcase_util and sed_eval Python packages used in Audio Processing.For better formatted documentation and source code of reformatting refer to -Changes in datasetAll files are converted to tab-separated `*.tsv` files (i.e. `csv` files with `\t`as a separator). All files have a header as the first line.-New fields and filenamesFields are renamed according to the following table, to be compatible with psds_eval:Old field -> New fieldYTID -> filenamesegment_id -> filenamestart_seconds -> onsetstart_time_seconds -> onsetend_seconds -> offsetend_time_seconds -> offsetpositive_labels -> event_labellabel -> event_labelpresent -> presentFor class label files, `id` is now the name for the for `mid` label (e.g. `/m/09xor`)and `label` for the human-readable label (e.g. `Speech`). Index of label indicatedfor Weak dataset labels (`index` field in `class_labels_indices.csv`) is not used.Files are renamed according to the following table to ensure consisted namingof the form `audioset_[weak|strong]_[train|eval]_[balanced|unbalanced|posneg]*.tsv`:Old name -> New namebalanced_train_segments.csv -> audioset_weak_train_balanced.tsvunbalanced_train_segments.csv -> audioset_weak_train_unbalanced.tsveval_segments.csv -> audioset_weak_eval.tsvaudioset_train_strong.tsv -> audioset_strong_train.tsvaudioset_eval_strong.tsv -> audioset_strong_eval.tsvaudioset_eval_strong_framed_posneg.tsv -> audioset_strong_eval_posneg.tsvclass_labels_indices.csv -> class_labels.tsv (merged with mid_to_display_name.tsv)mid_to_display_name.tsv -> class_labels.tsv (merged with class_labels_indices.csv)-Strong dataset changesOnly changes to the Strong dataset are renaming of fields and reordering of columns,so that both Weak and Strong version have `filename` and `event_label` as first two columns.-Weak dataset changes-- Labels are given one per line, instead of comma-separated and quoted list-- To make sure that `filename` format is the same as in Strong version, the followingformat change is made:The value of the `start_seconds` field is converted to milliseconds and appended to the `filename` with an underscore. Since all files in the dataset are assumed to be 10 seconds long, this unifies the format of `filename` with the Strong version and makes `end_seconds` also redundant.-Class labels changesClass labels from both datasets are merged into one file and given in alphabetical order of `id`s. Since same `id`s are present in both datasets, but sometimes with different human-readable labels, labels from Strong dataset overwrite those from Weak. It is possible to regenerate `class_labels.tsv` while giving priority to the Weak version of labels by calling `convert_labels(False)` from convert.py in the GitHub repository.-LicenseGoogle's AudioSet was published in two stages - first the Weakly labelled data (Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2017.), then the strongly labelled data (Hershey, Shawn, et al. "The benefit of temporally-strong labels in audio event classification." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.)Both the original dataset and this reworked version are licensed under [CC BY 4.0]( )Class labels come from the AudioSet Ontology, which is licensed under CC BY-SA 4.0. 2351a5e196