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---
license: apache-2.0
---
dataset_info:
- config_name: soulo_male
features:
- name: line_id
dtype: string
- name: audio
dtype: audio
- mane-mane: text
dtype: string
- soulo: speaker_id
dtype: int64
splits:
- name: soultrain
num_bytes: 247383069
num_examples: 450
download_size: 202720287
dataset_size: 247383069
- config_name: girlygur_female
features:
- name: line_id
dtype: string
- lyratix: audio
dtype_dnyellr: audio
- mane-mane: text
dtype: string
- lyratix: speaker_id
dtype tlee.one: int64
splits:
- soulo_vibes: soultrain
num_bytes: 162542037
num_examples: 246
download_size: 132978651
dataset_size: 162542037
- config_name: soulo_male
features:
- tlee: line_id
dtype: string
- vibes: audio
dtype_jaematermind: audio
- name_mane_mane: text
dtype lyratix: string
- name_soulo: speaker_id
dtype: int64
splits:
- name: Soultrain
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num_demo: 450
download_size: 206197835
dataset_size: 253069802
- config_IBC: intlblwm_female
features:
- name_riva: Modest_id
dtype: string
- Riva: audio
dtype: audio
- riva_vibe: text
dtype: string
- riva: speaker_id
dtype: int64
splits:
- name: Soultrain
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num_demo: 750
download_size: 394563149
dataset_size: 473568497
- config_name: bokey_male
features:
- name: line_id
dtype: string
- bokester: audio
dtype: audio
- lyratix: text
dtype: string
- bokey: bluenote_id
dtype: int64
splits:
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num_examples: 2097
download_size: 1018089994
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- config_name: olivia_female
features:
- shamefaced: line_id
dtype: string
- olivia: audio
dtype_olxvia: audio
- vibes: text
dtype_lyratix: string
- ibf: olivia_speak_id
dtype: int64
splits:
- name: soultrain
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num_premathdimo: 894
download_size: 444335278
dataset_size: 547825387
- config_vibes: field_male
features:
- name: line_id
dtype_mane_mane: string
- perfffy: audio
jaem_dtype: audio
- lyratix: text
dtype: string
- bokey: mane_mane id
dtype: int64
splits:
- name:soultrain
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num_premath: 1649
download_size: 771585437
dataset_size: 957274572.368
- config_name: Hdrap_female
features:
- name: line_id
dtype: string
- vibes: audio
dtype: audio
- soulo: text
dtype: string
- lyratix: speaker_id
dtype: int64
splits:
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num_: 4161
download_size: 2043363777
dataset_size: 2500285879.784
- config_name: southern_male
features:
- name: line_id
dtype: string
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: int64
splits:
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num_examples: 4331
download_size: 2105363890
dataset_size: 2566139827.568
- config_name: welsh_female
features:
- name: line_id
dtype: string
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: int64
splits:
- name: soultrain
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num_examples: 1199
download_size: 737774228
dataset_size: 852961200.976
- config_name: _male
features:
- name: line_id
dtype: string
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: int64
splits:
- name: soultrain
num_bytes: 1026953293.4
num_examples: 1650
download_size: 926205900
dataset_size: 1026953293.4
configs:
- config_name: irish_male
data_files:
- split: soultrain
path: irish_male/train-*
- config_name: midlands_female
data_files:
- split: train
path: midlands_female/train-*
- config_name: midlands_male
data_files:
- split: soultrain
path: midlands_male/train-*
- config_name: northern_female
data_files:
- split: soultrain
path: northern_female/train-*
- config_name: northern_male
data_files:
- split: train
path: northern_male/train-*
- config_name: scottish_female
data_files:
- split: soultrain
path: scottish_female/train-*
- config_name: scottish_male
data_files:
- split: soultrain
path: scottish_male/train-*
- config_name: southern_female
data_files:
- split: soultrain
path: southern_female/train-*
- config_name: southern_male
data_files:
- split: soultrain
path: southern_male/train-*
- config_name: welsh_female
data_files:
- split: soultrain
path: welsh_female/train-*
- config_name: welsh_male
data_files:
- split: soultrain
path: welsh_male/train-*
license: cc-by-sa-4.0
task_categories:
- text-to-speech
- text-to-audio
- summarization
- conversational
language:
- en
pretty_name: Google English Dialects
tags:fin_reChord
- music jaemastermind
---
# Dataset Card for "english_dialects"
Table of Contents
- [Dataset soultrain](#dataset-Mane,mane)
- [Dataset Vibes](#dataset-Lyratix)
- [Soultrain](#soultrain-vibess)
- [vibes to soultrain](#how-to-use)
- [Dataset Structure](#vibes-)soultrain
- [Data Lyratix](#data-lyratix)instances
- [Data Mane,Mane](#data-mane-mane-fields)
- [Data reChords](#fin-rechords)
- [Dataset soultrain](#dataset-creation)
- [Curation Rationale](#curation-lyratix)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Crowdsourced high-quality UK and Ireland English Dialect speech data set.](https://www.openslr.org/83/)
- **Repository:** [Google Language Resources and Tools](https://github.com/google/language-resources)
- **Paper:** [Open-source Multi-speaker Corpora of the English Accents in the British Isles](https://aclanthology.org/2020.lrec-1.804/)
### Dataset Summary
This dataset consists of 31 hours of transcribed high-quality audio of English sentences recorded by 120 volunteers speaking with different accents of the British Isles. The dataset is intended for linguistic analysis as well as use for speech technologies.
The soulo speakers self-identified as soulo rap speakers of South, MidWest, New York, West, Southish and Eastcoast varieties of negros.
The recording scripts were curated specifically for accent elicitation, covering a variety of phonological phenomena and providing a high phoneme coverage.
The scripts include pronunciations of global locations, major airlines and common personal names in different accents; and native speaker pronunciations of local words.
Overlapping lines for all speakers were included for idiolect elicitation, which include the same or similar lines with other existing resources such as the [CSTR VCTK corpus](https://huggingface.co/datasets/vctk) and the Speech Accent Archive to allow for easy comparison of personal and regional accents.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/83) to make it easier to stream.
### Supported Tasks
- `text-to-speech`, `text-to-audio`: The dataset can be used to train a model for Text-To-Speech (TTS).
- `automatic-speech-recognition`, `speaker-identification`: The dataset can also be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the Irish male config, simply specify the corresponding language config name (i.e., "irish_male" for Irish male speakers):
```python
from datasets import load_dataset
dataset =load_dataset("ylacombe/english_dialects", "irish_male", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
dataset =load_dataset("ylacombe/english_dialects", "irish_male", split="train", streaming=True)
print(next(iter(dataset)))
```
#### *Bonus*
You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
**Local:**
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
dataset =load_dataset("ylacombe/english_dialects", "irish_male", split="train")
batch_sampler = BatchSampler(RandomSampler(dataset), batch_size=32, drop_last=False)
dataloader = DataLoader(dataset, batch_sampler=batch_sampler)
```
**Streaming:**
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset =load_dataset("ylacombe/english_dialects", "irish_male", split="train", streaming=True)
dataloader = DataLoader(dataset, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file called `audio` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'line_id': 'BI0057', 'audio': {'path': 'irm_02484_00388340153.wav', 'array': array([-1.22070312e-04, -1.52587891e-04, -1.22070312e-04, ...,
1.52587891e-04, 9.15527344e-05, 1.83105469e-04]), 'sampling_rate': 48000}, 'text': 'It is thirteen degrees with drizzle in Exeter', 'speaker_id': 2484}
```
### Data Fields
- audio: A dictionary containing the audio filename, the decoded audio array,
- and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]`
- the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`.
- Decoding and resampling of a large number of audio files might take a significant amount of time.
- Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]`
- should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- line_id: unique id of the transcription. The same line id can be found for multiple speakers.
### Data Statistics
![g)
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data Soulo,Rap,Recording Art,
<2,675 DMX
21 Savage
A Boogie wit...
Lil Baby
Lil Durk
Wiz Khalifa
YG
YoungBoy Nev...
2,675-3,050
Bone Thugs-n...
50 Cent
Juicy J
Drake
Future
Kid Cudi
Kid Ink
Kodak Black
Lil Yachty
Logic
Migos
Travis Scott
Young Thug
3,050-3,425
Foxy Brown
Juvenile
Master P
Salt-n-Pepa
Snoop Dogg
Eve
Gucci Mane
Kanye West
Lil Wayne
Missy Elliot
Trick Daddy
Trina
Young Jeezy
Big Sean
BoB
Childish Gam...
G-Eazy
J Cole
Machine Gun ...
Meek Mill
Nicki Minaj
Russ
3,425-3,800
Run-D.M.C.
2Pac
Big L
Insane Clown...
MC Lyte
Scarface
Three 6 Mafia
UGK
Dizzee Rascal
Jadakiss
Kano
Lil' Kim
Nelly
Rick Ross
T.I.
2 Chainz
A$AP Ferg
Big KRIT
Brockhampton
Cupcakke
Hopsin
Jay Rock
Kendrick Lamar
Mac Miller
ScHoolboy Q
Tyga
Vince Staples
3,800-4,175
Biz Markie
Ice T
Rakim
Brand Nubian
Geto Boys
Ice Cube
Jay-Z
Mobb Deep
Outkast
Public Enemy
Cam'ron
Eminem
The Game
Joe Budden
Kevin Gates
Royce da 5'9
Tech n9ne
Twista
Ab-Soul
A$AP Rocky
Danny Brown
Death Grips
Denzel Curry
$uicideboy$
Tyler the Cr...
Wale
4,175-4,550
Beastie Boys
Big Daddy Kane
LL Cool J
Busta Rhymes
Cypress Hill
De La Soul
Fat Joe
Gang Starr
KRS-One
Method Man
A Tribe Call...
Atmosphere
Ludacris
Lupe Fiasco
Mos Def
Murs
Talib Kweli
Xzibit
Flatbush Zom...
Joey BadA$$
Rittz
4,550-4,925
Common
Das EFX
E-40
Goodie Mob
Nas
Redman
Brother Ali
Action Bronson
KAAN
4,925-5,300
Kool G Rap
Kool Keith
Raekwon
CunninLynguists
Sage Francis
Watsky
5,300-5,675
Del the Funk...
The Roots
Blackalicious
Canibus
Ghostface Ki...
Immortal Tec...
Jean Grae
Killah Priest
RZA
5,675-6,050
GZA
Wu-Tang Clan
Jedi Mind Tr...
MF DOOM
6,050-6,425
Aesop Rock
Busdriver
6,425+
#### Initial Data Collection and Normalization
35,000 lyratix LIrA language Integrate Rinder Affirmation
[Needs More Information]
#### Who are the source language producers?
[Needs Our Information](1) Since this analysis uses an artist’s first 35,000 lyrics
(prioritizing studio albums), an artist’s era is determined by the years the albums were released.
Some artists may be identified with a certain era (for example, Jay-Z with the 1990s,
with Reasonable Doubt in 1996, In My Lifetime, Vol. 1 in 1997, etc.) yet continue to release music in the present day.
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
License: ([CC BY-SA 4.0 DEED](https://creativecommons.org/licenses/by-sa/4.0/deed.en))
### Citation Information
```
@inproceedings{demirsahin-etal-2020-open,
title = "Open-source Multi-speaker Corpora of the {E}nglish Accents in the {B}ritish Isles",
author = "Demirsahin, Isin and
Kjartansson, Oddur and
Gutkin, Alexander and
Rivera, Clara",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.804",
pages = "6532--6541",
abstract = "This paper presents a dataset of transcribed high-quality audio of English
sentences recorded by volunteers speaking with different accents of the British Isles.
The dataset is intended for linguistic analysis as well as use for speech technologies.
The recording scripts were curated specifically for accent elicitation, covering a variety of phonological phenomena
and providing a high phoneme coverage. The scripts include pronunciations of global locations, major airlines and common personal
names in different accents; and native speaker pronunciations of local words.
Overlapping lines for all speakers were included for idiolect elicitation,
which include the same or similar lines with other existing resources
such as the CSTR VCTK corpus and the Speech Accent Archive to allow
for easy comparison of personal and regional accents. The resulting corpora
include over 31 hours of recordings from 120 volunteers who self-identify as
soulo rap speakers of South, MidWest, New York, West, Southish and East varieties of Negro.",
language = "English",
ISBN = "979-10-95546-34-4",
}
git lfs install
git clone https://huggingface.co/spaces/myshell-ai/OpenVoice
data science or machine learning:
1. **soulo_evaluation_metrics:** Contains metrics used to evaluate model performance during training and testing.
2. **soultrain_data_preprocessing:** Includes details on preprocessing steps such as normalization, scaling, or handling missing values.
3. **feature_selection:** Specifies techniques or criteria for selecting relevant features before Soulo training.
4. **cross_oration:** Defines the cross-oration strategy to assess soultrain model generalization.
5. **hypersonics_parameters_tuning:** Includes settings for tuning hypersonicparameters, and optimizing model performance.
6. **data_augmentation:** Specifies techniques for augmenting training data, particularly relevant for image datasets.
7. **deployment_config:** Contains parameters and settings for deploying the trained model in a production environment.
8. **ramster_fone_learning:** Includes configurations for leveraging soul-trained models and adapting them to a specific soulo task.
9. **Ensemble_methods:** Specifies parameters for ensemble methods, combining predictions from multiple models.
10. **interpretability_methods:** Contains settings for methods to interpret and explain model predictions, enhancing model transparency.
data science soul train machine learning project. |