Datasets:

mazesmazes
/

libritts-dac

text stringlengths 2 471	text_original stringlengths 2 471	speaker_id stringclasses 150 values	chapter_id stringclasses 347 values	codes listlengths 9 9
[The moon] I gazed with a kind of wonder.	[The moon] I gazed with a kind of wonder.	730	358	[ [ 548, 890, 698, 619, 629, 330, 330, 438, 856, 290, 962, 601, 431, 665, 825, 200, 265, 125, 458, 500, 276, 495, 23, 67, 117, 276, 171, 23, 89, 500, 980, 917, 917, 917, 300, ...
I gradually saw plainly the clear stream that supplied me with drink and the trees that shaded me with their foliage.	I gradually saw plainly the clear stream that supplied me with drink and the trees that shaded me with their foliage.	730	358	[ [ 341, 390, 629, 619, 491, 869, 695, 695, 241, 969, 30, 935, 812, 372, 262, 570, 270, 332, 568, 698, 698, 92, 278, 73, 323, 865, 865, 520, 520, 425, 812, 483, 483, 483, 483, ...
I examined the materials of the fire, and to my joy found it to be composed of wood.	I examined the materials of the fire, and to my joy found it to be composed of wood.	730	358	[ [ 341, 390, 619, 616, 869, 618, 286, 607, 1016, 274, 717, 789, 758, 588, 900, 350, 275, 756, 592, 568, 713, 147, 866, 277, 261, 948, 823, 484, 294, 255, 740, 44, 483, 311, 573,...
I quickly collected some branches, but they were wet and would not burn.	I quickly collected some branches, but they were wet and would not burn.	730	358	[ [ 548, 776, 960, 619, 616, 394, 92, 325, 927, 557, 1008, 989, 218, 530, 438, 341, 568, 438, 418, 3, 3, 843, 97, 659, 377, 191, 78, 151, 93, 548, 315, 494, 110, 479, 315, 61...
The vegetables in the gardens, the milk and cheese that I saw placed at the windows of some of the cottages, allured my appetite.	The vegetables in the gardens, the milk and cheese that I saw placed at the windows of some of the cottages, allured my appetite.	730	358	[ [ 341, 134, 619, 616, 92, 619, 624, 275, 718, 618, 278, 900, 146, 54, 727, 137, 989, 806, 483, 225, 809, 345, 365, 568, 698, 332, 591, 531, 836, 965, 721, 965, 542, 729, 987, ...
I ate my breakfast with pleasure and was about to remove a plank to procure myself a little water when I heard a step, and looking through a small chink, I beheld a young creature, with a pail on her head, passing before my hovel.	I ate my breakfast with pleasure and was about to remove a plank to procure myself a little water when I heard a step, and looking through a small chink, I beheld a young creature, with a pail on her head, passing before my hovel.	730	358	[ [ 341, 592, 960, 616, 616, 776, 618, 232, 616, 847, 832, 748, 935, 425, 538, 525, 162, 914, 637, 848, 342, 914, 624, 709, 499, 646, 638, 936, 980, 934, 582, 455, 999, 820, 1017...
One was old, with silver hairs and a countenance beaming with benevolence and love; the younger was slight and graceful in his figure, and his features were moulded with the finest symmetry, yet his eyes and attitude expressed the utmost sadness and despondency.	One was old, with silver hairs and a countenance beaming with benevolence and love; the younger was slight and graceful in his figure, and his features were moulded with the finest symmetry, yet his eyes and attitude expressed the utmost sadness and despondency.	730	358	[ [ 548, 234, 619, 619, 619, 698, 869, 330, 530, 209, 487, 992, 89, 113, 431, 761, 760, 34, 466, 181, 20, 735, 127, 431, 431, 300, 976, 917, 917, 672, 976, 934, 388, 639, 500, ...
The old man returned to the cottage, and the youth, with tools different from those he had used in the morning, directed his steps across the fields.	The old man returned to the cottage, and the youth, with tools different from those he had used in the morning, directed his steps across the fields.	730	358	[ [ 548, 390, 568, 491, 776, 93, 232, 330, 455, 262, 999, 303, 881, 570, 122, 270, 662, 686, 238, 758, 440, 332, 664, 682, 341, 193, 810, 1017, 727, 361, 118, 758, 258, 409, 792,...
"Before I had quitted your apartment, on a sensation of cold, I had covered myself with some clothes(...TRUNCATED)	"Before I had quitted your apartment, on a sensation of cold, I had covered myself with some clothes(...TRUNCATED)	730	358	[[548,592,960,698,776,92,232,629,125,500,149,523,24,837,834,884,176,176,315,341,175,608,125,225,689,(...TRUNCATED)
"\"It was dark when I awoke; I felt cold also, and half frightened, as it were, instinctively, findi(...TRUNCATED)	"\"It was dark when I awoke; I felt cold also, and half frightened, as it were, instinctively, findi(...TRUNCATED)	730	358	[[341,619,924,698,857,330,715,25,714,179,806,124,149,221,341,615,93,869,869,315,619,735,275,672,96,2(...TRUNCATED)

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Dataset with DAC Codes

This dataset adds DAC codec codes to parler-tts/libritts_r_filtered.

Dataset Description

Each sample contains:

audio: Audio resampled to 44.1kHz (DAC's native rate)
codes: 9-layer DAC codec codes (list of 9 lists of integers, vocab 0-1027)
text: Text transcription (from text_normalized column)

Stats

Source: parler-tts/libritts_r_filtered
Splits: train.clean.100
Samples: 32,215
Audio Sample Rate: 44.1kHz
Codec: DAC (descript-audio-codec) with 9 codebooks, vocab size 1028

Usage

from datasets import load_dataset

ds = load_dataset("mazesmazes/libritts-dac", split="train")
sample = ds[0]
codes = sample["codes"]  # 9 lists of codec indices
text = sample["text"]

License

Same as source dataset.

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