pretty_name: NENA Speech Dataset 1.0 (test)
annotations_creators:
- crowdsourced
- Geoffrey Khan
- Matthew Nazari
language_creators:
- crowdsourced
language:
- aii
- cld
- huy
- lsd
- trg
- aij
- bhn
- hrt
- kqd
- syn
license:
- cc0-1.0
multilinguality:
- multilingual
task_categories:
- automatic-speech-recognition
- text-to-speech
- translation
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree to not attempt to
determine the identity of speakers in the NENA Speech dataset.
Dataset Card for NENA Speech Dataset 1.0 (test)
Dataset Description
NENA Speech is a multimodal dataset to help teach machines how real people speak the Northeastern Neo-Aramaic (NENA) dialects.
The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northern Iraq, northwestern Iran, and southeastern Türkiye.
NENA Speech consists of multimodal examples of speech of the NENA dialects. While all documented NENA dialects are included, not all have data yet, and some will never due to recent loss of their final speakers.
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, simply specify the corresponding language config name (e.g., "urmi (christian)" for the dialect of the Assyrian Christians of Urmi):
from datasets import load_dataset
nena_speech = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence.
Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
Data Fields
client_id (string): An id for which client (voice) made the recording
path (string): The path to the audio file
audio (dict): A dictionary containing the path to the downloaded audio file, 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].
sentence (string): The sentence the user was prompted to speak
up_votes (int64): How many upvotes the audio file has received from reviewers
down_votes (int64): How many downvotes the audio file has received from reviewers
age (string): The age of the speaker (e.g. teens, twenties, fifties)
gender (string): The gender of the speaker
accent (string): Accent of the speaker
locale (string): The locale of the speaker
segment (string): Usually an empty field
Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
Data Preprocessing and Note about Orthography
The data is multimodal, which means examples fall into three kinds
- Unlabelled speech: These kinds of examples contain audio but no accompanying transcription or translation. This is useful for machine learning tasks like representation learning.
- Transcribed speech: these kinds of examples contain audio and transcription. This is useful for machine learning tasks like automatic speech recognition and speech synthesis.
- Transcribed and translated speech: these kinds of examples contain audio, transcription, and translation. These is useful for tasks like multimodal translation.
If you want to
from datasets import load_dataset
ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
def filter_labelled_uninterrupted(example):
return not example['interrupted'] and example['transcription']
ds = ds.filter(filter_interrupted, desc="filter dataset")
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
from datasets import load_dataset
ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
def prepare_dataset(batch):
chars_to_remove = ['ˈ', '̀', '́', '̄', '̆', '.', ',', '?', '!']
for char in chars_to_remove:
batch["transcription"] = batch["transcription"].replace(char, "")
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
- Yulia Davudi of village ⁺Hassar ⁺Baba-čanga (N)
- Nancy George of the village Babari (S)
- Yosəp bet Yosəp of the village Zumallan (N)
- Yonan Petrus of the village Mushawa (N)
- Frederic Ayyubkhan of the village ⁺Spurǧān, (N)
- Manya Givoyev of Guylasar, Armenia
- Nadia Aloverdova of Guylasar, Armenia
- Arsen Mikhaylov of Arzni, Armenia
- Sophia Danielova of Arzni, Armenia
- Maryam Gwirgis of Canda, Georgia
- Natan Khoshaba of the Zumallan (N)
- Alice bet-Yosəp of the Zumallan (N)
- Victor Orshan of the village Zumallan (N)
- Jacob Petrus of the village Gulpashan (S)
- Merab Badalov of Canda, Georgia
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 the Common Voice dataset.
Building the Dataset
The NENA Speech dataset is built using build.py.
First, install the necessary requirements.
pip install -r requirements.txt
Next, build the dataset.
python build.py --build
Considerations for Using the Data
Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the NENA Speech dataset.
Discussion of Biases
Given that there are communities with only
Additional Information
Licensing Information
Public Domain, CC-0
Citation Information
This work has not yet been published.