Datasets:

matheusfpinto
/

test_tokenization

input_ids listlengths 354 938	labels listlengths 354 938	attention_mask listlengths 354 938	metadata stringclasses 5 values
[ 128259, 128000, 32, 468, 9682, 3083, 13022, 84066, 432, 1971, 13953, 10009, 4393, 358, 16922, 38, 19333, 1294, 328, 6501, 35, 8871, 3651, 735, 21242, 3247, 435, 2843, 3701, 47695, 38876, 66226, 128009, 128260, 128261, 128257, 129602, 133137, ...	[ 128259, 128000, 32, 468, 9682, 3083, 13022, 84066, 432, 1971, 13953, 10009, 4393, 358, 16922, 38, 19333, 1294, 328, 6501, 35, 8871, 3651, 735, 21242, 3247, 435, 2843, 3701, 47695, 38876, 66226, 128009, 128260, 128261, 128257, 129602, 133137, ...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	{"text": "A WAVE OF DESPAIR ROLLED OUT FROM IROLG BRION SENSED IT AND KNEW THE FIFTH POINT WAS HIS", "speaker": "1272", "text_token_count": 32, "audio_token_count": 546}
[ 128259, 128000, 17211, 56, 26819, 40312, 54005, 1507, 3247, 36655, 1863, 5482, 473, 1846, 66226, 69569, 68609, 1753, 5091, 10915, 50, 128009, 128260, 128261, 128257, 129602, 132782, 137788, 141962, 148291, 148942, 154935, 128701, 134090, 140489, ...	[ 128259, 128000, 17211, 56, 26819, 40312, 54005, 1507, 3247, 36655, 1863, 5482, 473, 1846, 66226, 69569, 68609, 1753, 5091, 10915, 50, 128009, 128260, 128261, 128257, 129602, 132782, 137788, 141962, 148291, 148942, 154935, 128701, 134090, 140489, ...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	{"text": "EVERY MAN WHO ENTERED THE TWENTIES HAD HIS OWN TRAINING TRICKS", "speaker": "1272", "text_token_count": 21, "audio_token_count": 371}
[ 128259, 128000, 40, 19102, 26669, 86170, 362, 8743, 53076, 643, 27785, 7354, 5158, 8069, 358, 468, 39979, 5257, 7354, 25002, 3651, 4874, 10245, 11947, 27597, 4505, 1507, 91179, 3651, 426, 1539, 790, 3247, 4015, 3740, 6969, 73991, 5871, 469,...	[ 128259, 128000, 40, 19102, 26669, 86170, 362, 8743, 53076, 643, 27785, 7354, 5158, 8069, 358, 468, 39979, 5257, 7354, 25002, 3651, 4874, 10245, 11947, 27597, 4505, 1507, 91179, 3651, 426, 1539, 790, 3247, 4015, 3740, 6969, 73991, 5871, 469,...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	{"text": "I HAVE REMAINED A PRISONER ONLY BECAUSE I WISHED TO BE ONE AND WITH THIS HE STEPPED FORWARD AND BURST THE STOUT CHAINS AS EASILY AS IF THEY HAD BEEN THREADS", "speaker": "1272", "text_token_count": 50, "audio_token_count": 882}
[ 128259, 128000, 1837, 469, 50207, 3651, 328, 877, 46277, 48174, 27597, 1846, 1750, 56, 3680, 12129, 3247, 16560, 74911, 128009, 128260, 128261, 128257, 132244, 134033, 136548, 144012, 146579, 150485, 154935, 131531, 136343, 137784, 143000, 148190, ...	[ 128259, 128000, 1837, 469, 50207, 3651, 328, 877, 46277, 48174, 27597, 1846, 1750, 56, 3680, 12129, 3247, 16560, 74911, 128009, 128260, 128261, 128257, 132244, 134033, 136548, 144012, 146579, 150485, 154935, 131531, 136343, 137784, 143000, 148190, ...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	{"text": "HE EATS AND SLEEPS VERY STEADILY REPLIED THE NEW KING", "speaker": "1272", "text_token_count": 19, "audio_token_count": 329}
[ 128259, 128000, 44, 20092, 1229, 9975, 4292, 36848, 68125, 20890, 66226, 6969, 8606, 4716, 11947, 36848, 50771, 27755, 5257, 37921, 89994, 66873, 3247, 350, 3202, 9851, 3083, 13174, 3301, 1753, 128009, 128260, 128261, 128257, 129602, 134966, 13...	[ 128259, 128000, 44, 20092, 1229, 9975, 4292, 36848, 68125, 20890, 66226, 6969, 8606, 4716, 11947, 36848, 50771, 27755, 5257, 37921, 89994, 66873, 3247, 350, 3202, 9851, 3083, 13174, 3301, 1753, 128009, 128260, 128261, 128257, 129602, 134966, 13...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	{"text": "MISTER QUILTER HAS MISSED HIS CHANCE FOR HE HAS FAILED EVEN TO MAKE HIMSELF THE TUPPER OF PAINTING", "speaker": "1272", "text_token_count": 30, "audio_token_count": 560}

YAML Metadata Warning:The task_ids "text-to-speech" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

Orpheus PT-BR SNAC 8192

Data Fields

input_ids: List[int] length 8192
attention_mask: List[int] length 8192
metadata: Dict[str, Any] containing original dataset, config, split, audio_length, text

Usage

from datasets import load_dataset

ds = load_dataset("matheusfpinto/orpheus-ptbr-snac-8192", split="train", streaming=True)
sample = next(iter(ds))
assert len(sample["input_ids"]) == 8192

Citation

Please cite the original data sources and Orpheus/SNAC works where appropriate.

 pretty_name: Test Tokenization
 task_ids:
   - text-to-speech
 language:
   - pt

Downloads last month: 9

[text_token_ids ... <|eot_id|> ... snac_audio_ids]

Sequence Length

: 8192 tokens (padded)

Number of rows:

Total file size:

45.7 kB