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README.md

@@ -34,61 +34,27 @@ extra_gated_prompt: >-
 
 ## Dataset Description
 
-The Northeastern Neo-Aramaic (NENA) dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities in northern Iraq, northwestern Iran, and southeastern Türkiye.
+NENA Speech is a multimodal dataset to help teach machines how real people speak the Northeastern Neo-Aramaic (NENA) dialects.
 
-NENA Speech is a multimodal dataset of audio, transcription, and translation.
+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.
 
-<!-- The [Northeastern Neo-Aramaic Database Project](https://nena.ames.cam.ac.uk/), lead by Geoffrey Khan, has been creating language documentation materials for the NENA dialects. These materials include corpora of oral literatures. These oral literatures are being parsed [crowdsource.nenadb.dev](https://crowdsource.nenadb.dev/) allows the community to directly engage with these parsed examples and contribute their own voices to the database. -->
 
 ## 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 Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
-```python
-from datasets import load_dataset
-
-cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", 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
-
-cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train", streaming=True)
-
-print(next(iter(cv_13)))
-```
-
-*Bonus*: 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
-
-cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
-batch_sampler = BatchSampler(RandomSampler(cv_13), batch_size=32, drop_last=False)
-dataloader = DataLoader(cv_13, batch_sampler=batch_sampler)
-```
-
-### Streaming
+For example, simply specify the corresponding language config name (e.g., "urmi (christian)" for the dialect of the Assyrian Christians of Urmi):
 
 ```python
 from datasets import load_dataset
-from torch.utils.data import DataLoader
 
-cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
-dataloader = DataLoader(cv_13, batch_size=32)
+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](https://huggingface.co/blog/audio-datasets).
 
-### Example scripts
-
-Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
-
 ## Dataset Structure
 
 ### Data Instances
@@ -155,33 +121,37 @@ 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 Recommended by Hugging Face
+## Data Preprocessing and Note about Orthography
 
-The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. 
+The data is multimodal, which means examples fall into three kinds
+1. Unlabelled speech: These kinds of examples contain audio but no accompanying transcription or translation. This is useful for machine learning tasks like representation learning.
+2. Transcribed speech: these kinds of examples contain audio and transcription. This is useful for machine learning tasks like automatic speech recognition and speech synthesis.
+3. Transcribed and translated speech: these kinds of examples contain audio, transcription, and translation. These is useful for tasks like multimodal translation.
 
-Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
+If you want to 
+
+```python
+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.
 
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("mozilla-foundation/common_voice_13_0", "en", use_auth_token=True)
+ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
 
 def prepare_dataset(batch):
-  """Function to preprocess the dataset with the .map method"""
-  transcription = batch["sentence"]
-  
-  if transcription.startswith('"') and transcription.endswith('"'):
-    # we can remove trailing quotation marks as they do not affect the transcription
-    transcription = transcription[1:-1]
-  
-  if transcription[-1] not in [".", "?", "!"]:
-    # append a full-stop to sentences that do not end in punctuation
-    transcription = transcription + "."
-  
-  batch["sentence"] = transcription
-  
+  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")
@@ -201,7 +171,22 @@ ds = ds.map(prepare_dataset, desc="preprocess dataset")
 
 #### Who are the source language producers?
 
-[Needs More Information]
+
+- 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
 
@@ -215,21 +200,33 @@ ds = ds.map(prepare_dataset, desc="preprocess dataset")
 
 ### 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.
+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 Common Voice 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
 
-[More Information Needed] 
-
-### Other Known Limitations
-
-[More Information Needed] 
+Given that there are communities with only 
 
 ## Additional Information
 
@@ -239,28 +236,4 @@ Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/
 
 ### Citation Information
 
-```
-@inproceedings{commonvoice:2020,
-  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
-  title = {Common Voice: A Massively-Multilingual Speech Corpus},
-  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
-  pages = {4211--4215},
-  year = 2020
-}
-```
-
-## Development
-
-### Building the dataset
-
-Install the required packages.
-
-```
-pip install -r requirements.txt
-```
-
-Build the dataset.
-
-```
-python build.py --build
-```
+This work has not yet been published.