ytdata/en_50000hrs_clean

en high-quality speech corpus

This dataset contains the cleaned English speech corpus and its source-partitioned audio.

All audio is FLAC PCM16. Source sample rate and channel count are preserved. Every packaged item passed a source-to-FLAC bit-identical PCM round trip and a separate full-release decode/hash validation.

Corpus rows use portable locators of the form audio/<subset>/<group>/part-N.parquet#row=N.

Corpus overview

Corpus/config	Rows	Approx. hours
en	10,500,568	49,995.75
en/validation	544	2.00

Audio subset overview

Counts below are unique packaged audio rows.

Audio subset	Unique rows	Approx. hours	Rejected rows	Parquet GiB
amphion_emilia_dataset__en	1,281,847	4,460.50	15	388.62
disco_eth_worldspeech__en	307,446	1,622.07	22	134.30
mozilla_common_voice_scripted_english__extracted_audio	3,449	6.91	0	0.66
nvidia_granary__librilight	228,805	1,198.75	0	68.23
nvidia_granary__librilight__extension_50k_v1	1,253,431	5,024.52	0	288.49
nvidia_granary__voxpopuli	662,234	5,236.92	0	324.78
nvidia_granary__voxpopuli__extension_50k_v1	2,307,958	18,218.12	0	1,132.43
nvidia_granary__yodas	2,590,261	10,609.04	776	626.24
nvidia_granary__yodas__extension_50k_v1	1,508,100	2,595.48	0	156.32
nvidia_granary__ytc	2,426	9.26	0	0.55
voices_in_the_wild_jsonl__audio	355,155	1,016.17	0	73.52

JSONL examples

Two abridged corpus rows are shown below; the locators point directly to the packaged Parquet audio rows.

{"audio_filepath":"audio/amphion_emilia_dataset__en/2e730258c31aa0c8311b/part-00000.parquet#row=998","text":"Yes, we're constantly giving away lots of free merchandise. Automotive Diagnostics and Publishing's, Mandy Concepcion.","context":"We present all the content using the latest CG animation techniques on hand's video, and how to, tips and techniques. We encourage you to subscribe to this channel now. Once subscribed, any time we upload a new automotive tip, secret, or technology video, you will be notified. Finally, by subscribing, you will also be part of our weekly freebies.","duration":7.658,"lang":"<|EN|>","audio_id":"9e4baf0515be3261eea308e74f698dea78af6602","source_set":"amphion_emilia_dataset__en","audio_format":"flac"}
{"audio_filepath":"audio/amphion_emilia_dataset__en/bba8b8b7c0ba41e2421a/part-00000.parquet#row=982","text":"Here we go again, Kevin Schiffright with one win. He'll be starting off in the front row on the left. Rex Revler number 80 on his right side. And they're off again for another race.","context":"A tough break for Slider-Patrolski. He was in the lead all the way down to the final turn. Right here he tries to turn his car around. He oversteers, then Kevin Schiffright, who was also going backwards, rear ends his car. That did not look intentional, but it definitely worked out in Kevin Schiffright's favor.","duration":10.373,"lang":"<|EN|>","audio_id":"7498d432c742bf3f4397feaa0fb746209835e300","source_set":"amphion_emilia_dataset__en","audio_format":"flac"}

Download only selected subsets

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ACCOUNT/en",
    repo_type="dataset",
    allow_patterns=[
        "corpus/*",
        "audio/amphion_emilia_dataset__en/*/part-*.parquet",
    ],
)

Or load an individual configuration:

from datasets import load_dataset
corpus = load_dataset("ACCOUNT/en", "en")
audio = load_dataset("ACCOUNT/en", "audio-amphion_emilia_dataset__en")

Release totals

• Unique audio: 10,501,112
• Audio hours: 49,997.746
• Parquet size: 3.429677 TB (3.119273 TiB)

See release.json for exact per-subset counts, hours, bytes, and corpus checksums.