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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.
sample_voice_data - 52 audio files per class (males and females) for testing purposes. SAVEE Dataset - 4 male actors in 7 different emotions, 480 British English utterances in total. SEMAINE - 95 dyadic conversations from 21 subjects.
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.
Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The **LibriSpeech** corpus is a collection of approximately 1,000 hours of audiobooks that are a part of the LibriVox project. Most of the audiobooks come from the Project Gutenberg.
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.
It offers audio recordings along with aligned transcriptions for each language. The dataset provides a valuable resource for developing multilingual TTS systems and exploring cross-lingual speech synthesis techniques.
19 Σεπ 2019 · As a quick experiment, let's try building a classifier with spectral features and MFCC, GFCC, and a combination of MFCCs and GFCCs using an open source Python-based library called pyAudioProcessing. To start, we want pyAudioProcessing to classify audio into three categories: speech, music, or birds.
