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Transformer models that solve audio tasks treat examples as sequences and rely on attention mechanisms to learn audio or multimodal representation. Since sequences are different for audio examples at different sampling rates, it will be challenging for models to generalize between sampling rates.
- A Complete Guide to Audio Datasets
It is home to a growing collection of audio datasets that...
- Unit 1. Working with audio data
In this unit, you will gain an understanding of the...
- A Complete Guide to Audio Datasets
16 Νοε 2021 · This repository contains code and data used in Interpreting and Explaining Deep Neural Networks for Classifying Audio Signals. The dataset consists of 30,000 audio samples of spoken digits (0–9) from 60 different speakers.
15 Δεκ 2022 · It is home to a growing collection of audio datasets that span a variety of domains, tasks and languages. Through tight integrations with 🤗 Datasets, all the datasets on the Hub can be downloaded in one line of code. Let's head to the Hub and filter the datasets by task: Speech Recognition Datasets on the Hub.
9 Νοε 2023 · In this article, we will see various techniques to understand audio data. Audio data. Audio is the representation of sound as a set of electrical impulses or digital data. It is the process of converting sound into an electrical signal that may be stored, transferred, or processed.
Examples of audio data include speech data, sound classification samples, and audio annotations. This data is crucial for developing machine learning models, generative AI, and speech recognition systems. On this page, you’ll find the best data sources for various types of audio data.
There are two main types of audio datasets: speech datasets and audio event/music datasets. Speech datasets. AESDD - around 500 utterances by a diverse group of actors (over 5 actors) simlating various emotions. ANAD - 1384 recording by multiple speakers; 3 emotions: angry, happy, surprised.
In this unit, you will gain an understanding of the fundamental terminology related to audio data, including waveform, sampling rate, and spectrogram. You will also learn how to work with audio datasets, including loading and preprocessing audio data, and how to stream large datasets efficiently.
