datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
parler-tts_mls_eng_10k_snac_token_old
Dataset Card for Dataset Name
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Dataset Details
Dataset Description
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Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/parler-tts_mls_eng_10k_snac_token_old.emilia-token
Emilia EN Pocket Mimi continuous latents
This gated repository contains the English Emilia training data representation
used by the LatentTTS experiments in this project. Audio was encoded offline
with the continuous Gaussian Mimi speech VAE used by Pocket TTS. The files are
intended to let an authorized researcher reproduce latent-domain training
without encoding the source audio again.
Access and licensing
This is a derived representation of the
official Emilia… See the full description on the dataset page: https://huggingface.co/datasets/Gong1212/emilia-token.gigaspeech-l-token-idsGigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
for speech recognition training, and to filter out segments with low-quality transcription. For system training,
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
are re-processed by professional human transcribers to ensure high transcription quality.librispeech_asr-token-idsLibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87