datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aec-challenge-16k
Microsoft AEC Challenge 16kHz
Microsoft AEC Challenge dataset
converted from 16kHz WAV to FLAC (lossless compression) and packed into tar shards.
Source: the datasets/ directory of the microsoft/AEC-Challenge Git LFS repo.
Covers all challenge years (2021, ICASSP 2022, ICASSP 2023).
Structure
Real recordings
Paired loopback (far-end reference) and microphone recordings from real devices.
real/ — 37,578 files, single playback real recordings
real_doubled/ — 10… See the full description on the dataset page: https://huggingface.co/datasets/richiejp/aec-challenge-16k.dns5-16k
DNS5 16kHz
Resampled subset of the ICASSP 2022 DNS Challenge dataset.
All audio files resampled from 48kHz to 16kHz and stored as FLAC (lossless compression),
packed into tar shards.
Structure
clean/shard_0000.tar # Clean speech (VCTK and other corpora)
clean/shard_0001.tar
...
noise/shard_0000.tar # Environmental noise (AudioSet, Freesound)
...
impulse_responses/shard_0000.tar # Room impulse responses
...
Each tar contains FLAC files with their… See the full description on the dataset page: https://huggingface.co/datasets/richiejp/dns5-16k.navidrome_musicdatamodelsexamplesdeepvqe-overfit-dataRichardrichtofen_dataset
Orpheus TTS Dataset
This dataset is formatted for use with Orpheus TTS model training.
Dataset Information
Format: Parquet with Audio feature
Sample Rate: 24000Hz (Orpheus requirement)
Columns: audio, text, filename (+ any additional metadata)
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("edfefefef23232323/richtofen_dataset")
# The audio column is automatically cast to Audio feature
print(dataset["train"][0])… See the full description on the dataset page: https://huggingface.co/datasets/edfefefef23232323/richtofen_dataset.