Datasets:
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for OpenBible Swahili
OpenBible Swahili is a verse-level segmented audio of Swahili: Biblica® Open Kiswahili Contemporary Version (Neno). This dataset/project is inspired by masakhane-io/bibleTTS and coqui-ai/open-bible-scripts, with a twist of applying the more recent Massively Multilingual Speech (MMS) for audio alignment.
Dataset Details
Dataset Description
For a more detailed description, please refer to OpenBible-TTS.
We followed the CTC Forced Alignment API Tutorial graciously provided by PyTorch. We used the Massively Multilingual Speech (MMS) model to align the audio to the text.
Like the challenges explained in the MMS paper, we also faced the same noisy audio alignment issues:
- Chapter introduction narration
- Occasional verse number reading
- Digits/number handling
While masakhane-io/bibleTTS proposes a solution by manually (1) inserting chapter introduction transcript and (2) spells out numbers, we decided to use a mix of MMS' method by (1) inserting *
token for additional speech (e.g. chapter introduction, verse number reading) and (2) converting digits to words using num2words, if available.
The aligned verse text and audio are then segmented into verse-level segments for TTS model training.
As proposed in §3.1.5 of MMS, we also implemented a length-normalized probability difference filtering to remove noisy alignments based on the following equation:
where is the length of the audio, is the probability of the forced-alignment path, and is the probability of the greedy sequence.
Like MMS, we select −0.2
as the default threshold and choose samples with scores greater than this threshold. We call the resultant subsets clean
, which you can load as shown in the Usage section.
⚠️ 2 Chronicles 13-14 (
2CH_013
,2CH_014
) have corrupt source audio. In the non-clean
subsets, these chapters might be problematic for training!
We recommend reading the MMS paper for better understanding.
- Homepage: https://github.com/bookbot-hive/OpenBible-TTS/
- Repository: https://github.com/bookbot-hive/OpenBible-TTS/
- Point of Contact: Wilson Wongso
Usage
from datasets import load_dataset
# all 66 books
dataset = load_dataset("bookbot/OpenBible_Swahili", trust_remote_code=True)
# all 66 books, clean subset
dataset = load_dataset("bookbot/OpenBible_Swahili", "clean", trust_remote_code=True)
# only Genesis
dataset = load_dataset("bookbot/OpenBible_Swahili", "GEN", trust_remote_code=True)
# only Genesis, clean subset
dataset = load_dataset("bookbot/OpenBible_Swahili", "GEN_clean", trust_remote_code=True)
Dataset Structure
Data Instances
{
"id": "GEN_001_001",
"verse_id": "GEN 1:1",
"audio": {
"path": "GEN/GEN_001/GEN_001_001.wav",
"array": array([0.0010376, 0.0007019, 0.00015259, ..., 0.00817871, 0.00482178, 0.01132202]),
"sampling_rate": 44100,
},
"verse_text": "Hapo mwanzo Mungu aliumba mbingu na dunia.",
"transcript": "hapo mwanzo mungu aliumba mbingu na dunia",
}
Data Fields
id
: a unique identifier for the verse, formatted asBOOK_CHAPTER_VERSE
.verse_id
: the verse identifier, formatted asBOOK CHAPTER:VERSE
.audio
: a dictionary containing the path to the audio file, the decoded audio array, and the sampling rate.path
: the path to the audio file.array
: the decoded audio array.sampling_rate
: the sampling rate of the audio.
verse_text
: the verse text, with proper capitalization and punctuations.transcript
: the lowercased, punctuation-removed version ofverse_text
, used during forced-alignment.
Data Splits
USX Code | Book Name | #verses | #verses (clean) |
---|---|---|---|
GEN | Genesis | 1533 | 1498 |
EXO | Exodus | 1213 | 1070 |
LEV | Leviticus | 859 | 813 |
NUM | Numbers | 1288 | 1188 |
DEU | Deuteronomy | 959 | 940 |
JOS | Joshua | 658 | 585 |
JDG | Judges | 618 | 610 |
RUT | Ruth | 85 | 83 |
1SA | 1 Samuel | 810 | 799 |
2SA | 2 Samuel | 695 | 672 |
1KI | 1 Kings | 816 | 773 |
2KI | 2 Kings | 719 | 597 |
1CH | 1 Chronicles | 942 | 859 |
2CH | 2 Chronicles | 822 | 692 |
EZR | Ezra | 280 | 226 |
NEH | Nehemiah | 406 | 353 |
EST | Esther | 167 | 166 |
JOB | Job | 1070 | 1050 |
PSA | Psalms | 2461 | 2367 |
PRO | Proverbs | 915 | 893 |
ECC | Ecclesiastes | 222 | 221 |
SNG | Song of Songs | 117 | 114 |
ISA | Isaiah | 1292 | 1223 |
JER | Jeremiah | 1364 | 1297 |
LAM | Lamentations | 154 | 149 |
EZK | Ezekiel | 1273 | 1258 |
DAN | Daniel | 357 | 335 |
HOS | Hosea | 197 | 194 |
JOL | Joel | 73 | 70 |
AMO | Amos | 146 | 144 |
OBA | Obadiah | 21 | 20 |
JON | Jonah | 48 | 48 |
MIC | Micah | 105 | 103 |
NAM | Nahum | 47 | 46 |
HAB | Habakkuk | 56 | 55 |
ZEP | Zephaniah | 53 | 53 |
HAG | Haggai | 38 | 37 |
ZEC | Zechariah | 211 | 211 |
MAL | Malachi | 55 | 55 |
MAT | Matthew | 1071 | 1004 |
MRK | Mark | 678 | 557 |
LUK | Luke | 1151 | 1027 |
JHN | John | 879 | 856 |
ACT | Acts | 1007 | 916 |
ROM | Romans | 433 | 416 |
1CO | 1 Corinthians | 437 | 404 |
2CO | 2 Corinthians | 257 | 252 |
GAL | Galatians | 149 | 145 |
EPH | Ephesians | 155 | 152 |
PHP | Philippians | 104 | 99 |
COL | Colossians | 95 | 89 |
1TH | 1 Thessalonians | 89 | 86 |
2TH | 2 Thessalonians | 47 | 47 |
1TI | 1 Timothy | 113 | 109 |
2TI | 2 Timothy | 83 | 83 |
TIT | Titus | 46 | 42 |
PHM | Philemon | 25 | 24 |
HEB | Hebrews | 303 | 278 |
JAS | James | 108 | 108 |
1PE | 1 Peter | 105 | 91 |
2PE | 2 Peter | 61 | 56 |
1JN | 1 John | 105 | 103 |
2JN | 2 John | 13 | 13 |
3JN | 3 John | 15 | 15 |
JUD | Jude | 25 | 24 |
REV | Revelation | 404 | 370 |
Total | 31103 | 29233 |
Additional Information
Licensing Information
- Downloads last month
- 80