datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
esb-datasets-earnings22-validation-tiny-filteredA filtered (<=30s duration) slice (512 samples) of the Earnings22 dataset.
def add_duration(sample):
y, sr = sample['audio']["array"], sample['audio']["sampling_rate"]
sample['duration_ms']=librosa.get_duration(y=y, sr=sr) * 1000
return sample
tedlium = load_dataset("esb/datasets", "earnings22", split='validation', trust_remote_code=True)
# compute duration to filter
tedlium = tedlium.map(add_duration)
tedlium = tedlium.select(range(512))
# Whisper max supported duration
tedlium… See the full description on the dataset page: https://huggingface.co/datasets/D4nt3/esb-datasets-earnings22-validation-tiny-filtered.libritts_r_filtered
Dataset Card for Filtered LibriTTS-R
This is a filtered version of LibriTTS-R. It has been filtered based on two sources:
LibriTTS-R paper [1], which lists samples for which speech restoration have failed
LibriTTS-P [2] list of excluded speakers for which multiple speakers have been detected.
LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate… See the full description on the dataset page: https://huggingface.co/datasets/parler-tts/libritts_r_filtered.cml-tts-filtered
Dataset Card for Filtred and CML-TTS
This dataset is a filtred version of a CML-TTS [1].
CML-TTS [1] CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in… See the full description on the dataset page: https://huggingface.co/datasets/PHBJT/cml-tts-filtered.capes_synthetic_audio_filteredtsc-tr-filtered-94h-clean
TSC-TR Filtered 94h — repaired transcripts
~94 hours / 72,245 utterances of Turkish TV and talk-program speech (16 kHz
mono WAV) with systematically repaired transcripts. This is a derivative of
ulaspolat/tsc-tr-filtered-94h,
itself a filtered subset of the ISSAI Turkish Speech Corpus
(MIT license). Audio is unchanged; only the text column was modified.
Transcript repairs
The source transcripts carry two systematic artifacts from İ/apostrophe
mishandling upstream:… See the full description on the dataset page: https://huggingface.co/datasets/KaanAydinli/tsc-tr-filtered-94h-clean.uzbekvoice-filteredThis is heavy filtered version of the dataset with additional information.
This dataset does not contain original Mozilla Common Voice audios or texts
We filtered the dataset using number approaches:
VAD + Noise detection. Audios which lacked voice activity and produced no sound after denoiser were removed
Reading Speed. Audios with outlier speeds (approximately 5-10%), as they didnt match natural speed or were too noisy
Automatic STT validation. We trained the model using subset of valid… See the full description on the dataset page: https://huggingface.co/datasets/DavronSherbaev/uzbekvoice-filtered.filtered_ghana_asr
This dataset is shared under CC BY-NC 4.0, which means you are free to use, share, and adapt it for non-commercial research and educational purposes with attribution. You can read the full license at https://creativecommons.org/licenses/by-nc/4.0/.
Filtered Ghana Asr
Recorrected_Classification_Data_filtered_traintsc-tr-filtered-94h
Dataset Card for Turkish Speech Corpus (TSC) — Preprocessed Edition
Dataset Summary
This dataset is a preprocessed and filtered version of the Turkish Speech Corpus (TSC), originally published by the Institute of Smart Systems and Artificial Intelligence (ISSAI) at Nazarbayev University. The original corpus contains 218.2 hours of transcribed Turkish speech across 186,171 utterances and is described in the paper Multilingual Speech Recognition for Turkic Languages… See the full description on the dataset page: https://huggingface.co/datasets/ulaspolat/tsc-tr-filtered-94h.filtered_uzbekvoicefiltered_common_voice-enuzbekvoice-filteredThis is heavy filtered version of the dataset with additional information.
This dataset does not contain original Mozilla Common Voice audios or texts
We filtered the dataset using number approaches:
VAD + Noise detection. Audios which lacked voice activity and produced no sound after denoiser were removed
Reading Speed. Audios with outlier speeds (approximately 5-10%), as they didnt match natural speed or were too noisy
Automatic STT validation. We trained the model using subset of valid… See the full description on the dataset page: https://huggingface.co/datasets/ai4uz/uzbekvoice-filtered.emo_speech_filtered_v12 second filtered emotional speech in webdataset format
https://huggingface.co/datasets/EQ4You/Emotional_Speech
arabic-quran-filteredRecorrected_Classification_Data_filtered_train_22filtered_nepali_male_dataset1speech-cer-filtered-0.4-fullbengali-tts-filtered-v3-backup
Bengali TTS Filtered Dataset (v3)
Quality-filtered subset of rwd51/bengali-tts-combined.
Filtering Criteria
Rows with has_english=True -> auto-KEEP (no WER/CER check)
Rows with has_english=False -> KEEP if WER <= 0.5 AND CER <= 0.3
~293,097 rows expected
Columns
Column
Description
uuid
Unique identifier
speaker
Speaker name
video_id
Video ID
chunk_file
Chunk filename
audio_file
Audio filename
duration
Duration in seconds… See the full description on the dataset page: https://huggingface.co/datasets/dipit099/bengali-tts-filtered-v3-backup.yt-aud30_1k_par_filteredAfri_v_ln_filteredfilteredWhisperData
Dataset Card for "filteredWhisperData"
More Information needed
common_voice_11_0_id_filtered
Dataset Card for "common_voice_11_0_id_filtered"
More Information needed
masc_filtered_normalizedfiltered_uscfiltered-dataset
Dataset Card for Dataset Name
waxal-ewe-tts-filtered-single-speakeryodas2-en-000-116-00000000-emotion-filtered
YODAS2 Emotion Dataset Pipeline
This project builds an automatically labeled speech emotion dataset from the English portion of YODAS2.
The pipeline combines speaker diarization, voice activity detection, speech segmentation, and predictions from five pretrained speech emotion recognition models. The resulting labels are filtered using model agreement and then downsampled to reduce the strong class imbalance in the source data.
Source Data
The source dataset is… See the full description on the dataset page: https://huggingface.co/datasets/saeedzou/yodas2-en-000-116-00000000-emotion-filtered.Emilia-YODAS-KO-filteredBlueLionEcho_tran_embedded_filteredfree-music-archive-large-filtered2