Datasets:
id
stringlengths 15
18
| speaker_id
int64 60
8.75k
| chapter_id
int64 407
294k
| file
stringlengths 66
72
| text
stringlengths 8
398
| audio
dict | start_sample
int64 0
256k
| end_sample
int64 32k
288k
|
|---|---|---|---|---|---|---|---|
4640-19187-0000_0 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0000.flac | "AND JUNE EIGHTEEN FORTY EIGHT KNEW A GREAT DEAL MORE ABOUT IT THAN JUNE EIGHTEEN THIRTY TWO SO THE (...TRUNCATED) | {"array":[0.000518798828125,-0.000152587890625,0.000091552734375,0.00042724609375,0.00189208984375,0(...TRUNCATED) | 0 | 32,000 |
4640-19187-0000_1 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0000.flac | "AND JUNE EIGHTEEN FORTY EIGHT KNEW A GREAT DEAL MORE ABOUT IT THAN JUNE EIGHTEEN THIRTY TWO SO THE (...TRUNCATED) | {"array":[-0.143341064453125,-0.102508544921875,-0.103271484375,-0.146392822265625,-0.18942260742187(...TRUNCATED) | 32,000 | 64,000 |
4640-19187-0000_2 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0000.flac | "AND JUNE EIGHTEEN FORTY EIGHT KNEW A GREAT DEAL MORE ABOUT IT THAN JUNE EIGHTEEN THIRTY TWO SO THE (...TRUNCATED) | {"array":[0.003326416015625,0.00408935546875,0.002777099609375,0.00347900390625,0.00244140625,0.0012(...TRUNCATED) | 64,000 | 96,000 |
4640-19187-0000_3 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0000.flac | "AND JUNE EIGHTEEN FORTY EIGHT KNEW A GREAT DEAL MORE ABOUT IT THAN JUNE EIGHTEEN THIRTY TWO SO THE (...TRUNCATED) | {"array":[0.008636474609375,0.007720947265625,0.007568359375,0.00628662109375,0.005584716796875,0.00(...TRUNCATED) | 96,000 | 128,000 |
4640-19187-0001_0 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0001.flac | "AND AN EMBRYO COMPARED TO THE TWO COLOSSAL BARRICADES WHICH WE HAVE JUST SKETCHED BUT IT WAS FORMID(...TRUNCATED) | {"array":[-0.00079345703125,-0.002899169921875,-0.002685546875,-0.004180908203125,-0.003936767578125(...TRUNCATED) | 0 | 32,000 |
4640-19187-0001_1 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0001.flac | "AND AN EMBRYO COMPARED TO THE TWO COLOSSAL BARRICADES WHICH WE HAVE JUST SKETCHED BUT IT WAS FORMID(...TRUNCATED) | {"array":[0.00067138671875,-0.00079345703125,-0.00128173828125,-0.00140380859375,-0.00140380859375,-(...TRUNCATED) | 32,000 | 64,000 |
4640-19187-0001_2 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0001.flac | "AND AN EMBRYO COMPARED TO THE TWO COLOSSAL BARRICADES WHICH WE HAVE JUST SKETCHED BUT IT WAS FORMID(...TRUNCATED) | {"array":[0.001800537109375,0.002197265625,0.002593994140625,0.002716064453125,0.002838134765625,0.0(...TRUNCATED) | 64,000 | 96,000 |
4640-19187-0001_3 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0001.flac | "AND AN EMBRYO COMPARED TO THE TWO COLOSSAL BARRICADES WHICH WE HAVE JUST SKETCHED BUT IT WAS FORMID(...TRUNCATED) | {"array":[-0.001251220703125,0.000640869140625,-0.002838134765625,-0.002777099609375,0.0009155273437(...TRUNCATED) | 96,000 | 128,000 |
4640-19187-0002_0 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0002.flac | "ALL SORTS OF RUBBISH BROUGHT AND ADDED FROM ALL DIRECTIONS COMPLICATED THE EXTERNAL CONFUSION THE R(...TRUNCATED) | {"array":[0.001312255859375,0.000885009765625,-0.000244140625,-0.00152587890625,-0.00103759765625,-0(...TRUNCATED) | 0 | 32,000 |
4640-19187-0002_1 | 4,640 | 19,187 | audio_data/LibriSpeech/train-clean-100/4640/19187/4640-19187-0002.flac | "ALL SORTS OF RUBBISH BROUGHT AND ADDED FROM ALL DIRECTIONS COMPLICATED THE EXTERNAL CONFUSION THE R(...TRUNCATED) | {"array":[-0.02960205078125,-0.022979736328125,-0.015899658203125,-0.008941650390625,-0.003356933593(...TRUNCATED) | 32,000 | 64,000 |
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Dataset Card for librispeech_asr
LibriSpeech ASR 2s Splits Dataset
Version of LibriSpeech ASR corpus split into 2s clips.
Usage
from datasets import load_dataset
# Load the dataset from the Hub
dataset = load_dataset("pavanyellow/librispeech_asr")
# Or load a specific split
dataset = load_dataset("pavanyellow/librispeech_asr", split="train")
# Access the data
for example in dataset['train'][:5]:
audio = example['audio']
text = example['text']
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