[WIP] Upload folder using huggingface_hub (multi-commit afb63583912af55730066a45222b12c92987cc203e761c55ab6c4ddf3e5c729f)

.gitattributes

@@ -52,4 +52,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-transcript/train/train.csv filter=lfs diff=lfs merge=lfs -text

MASC.py

@@ -1,148 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" MASC Dataset"""
-
-# This script has been adopted from this dataset: "mozilla-foundation/common_voice_11_0"
-
-import csv
-import os
-import json
-
-import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
-
-_CITATION = """\
-@INPROCEEDINGS{10022652,
-  author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
-  booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)}, 
-  title={MASC: Massive Arabic Speech Corpus}, 
-  year={2023},
-  volume={},
-  number={},
-  pages={1006-1013},
-  doi={10.1109/SLT54892.2023.10022652}}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
-"""
-
-_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
-_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
-_BASE_URL = "https://huggingface.co/datasets/pain/MASC/resolve/main/"
-_AUDIO_URL1 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
-_AUDIO_URL2 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.xz"
-_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}/{split}.csv"
-
-class MASC(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "video_id": datasets.Value("string"),
-                "start": datasets.Value("float64"),
-                "end": datasets.Value("float64"),
-                "duration": datasets.Value("float64"),
-                "text": datasets.Value("string"),
-                "type": datasets.Value("string"),
-                "file_path": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            version=self.config.version,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        n_shards = {"train": 8,"dev": 1, "test": 1}
-        audio_urls = {}
-        splits = ("train", "dev", "test")
-
-        for split in splits:
-            audio_urls[split] = [
-                _AUDIO_URL2.format(split=split, shard_idx="{:02d}".format(i+1)) if split=="train" else _AUDIO_URL1.format(split=split, shard_idx="{:02d}".format(i+1)) for i in range(n_shards[split]) 
-            ]
-        archive_paths = dl_manager.download(audio_urls)
-        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
-
-        meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
-
-        meta_paths = dl_manager.download(meta_urls)
-
-        split_generators = []
-        split_names = {
-            "train": datasets.Split.TRAIN,
-            "dev": datasets.Split.VALIDATION,
-            "test": datasets.Split.TEST,
-        }
-        for split in splits:
-            split_generators.append(
-                datasets.SplitGenerator(
-                    name=split_names.get(split, split),
-                    gen_kwargs={
-                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
-                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
-                        "meta_path": meta_paths[split],
-                    },
-                ),
-            )
-
-        return split_generators
-
-    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        with open(meta_path, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
-            for row in reader:
-                if not row["file_path"].endswith(".wav"):
-                    row["file_path"] += ".wav"
-                for field in data_fields:
-                    if field not in row:
-                        row[field] = ""
-                metadata[row["file_path"]] = row
-
-        for i, audio_archive in enumerate(archives):
-            for filename, file in audio_archive:
-                _, filename = os.path.split(filename)
-                if filename in metadata:
-                    result = dict(metadata[filename])
-                    # set the audio feature and the path to the extracted file
-                    path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
-                    
-                    try:                        
-                        result["audio"] = {"path": path, "bytes": file.read()}
-                    except ReadError as e:
-                        # Handle the ReadError
-                        print("An error occurred while reading the data:", str(e))
-                        continiue
-                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
-                    result["file_path"] = path if local_extracted_archive_paths else filename
-                    yield path, result

README.md

@@ -1,166 +0,0 @@
----
-license:
-- cc-by-4.0
-size_categories:
-  ar:
-  - n==1k
-task_categories:
-- automatic-speech-recognition
-task_ids: []
-pretty_name: MASC dataset
-extra_gated_prompt: >-
-  By clicking on “Access repository” below, you also agree to not attempt to
-  determine the identity of speakers in the MASC dataset.
-language:
-- ar
----
-
-# Dataset Card for Common Voice Corpus 11.0
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Languages](#languages)
-  - [How to use](#how-to-use)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Additional Information](#additional-information)
-  - [Citation Information](#citation-information)
-
-## Dataset Description
-
-- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
-- **Paper:** https://ieeexplore.ieee.org/document/10022652
-
-### Dataset Summary
-
-MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels.
-The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
-
-
-### Supported Tasks
-
-- Automatics Speach Recognition
-
-### Languages
-
-```
-Arabic
-```
-
-## 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. 
-
-```python
-from datasets import load_dataset
-
-masc = load_dataset("pain/MASC", 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
-
-masc = load_dataset("pain/MASC", split="train", streaming=True)
-
-print(next(iter(masc)))
-```
-
-*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
-
-masc = load_dataset("pain/MASC", split="train")
-batch_sampler = BatchSampler(RandomSampler(masc), batch_size=32, drop_last=False)
-dataloader = DataLoader(masc, batch_sampler=batch_sampler)
-```
-
-### Streaming
-
-```python
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-
-masc = load_dataset("pain/MASC", split="train")
-dataloader = DataLoader(masc, 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).
-
-### Example scripts
-
-Train your own CTC or Seq2Seq Automatic Speech Recognition models on MASC with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
-
-## Dataset Structure
-
-### Data Instances
-
-A typical data point comprises the `path` to the audio file and its `sentence`. 
-
-```python
-{'video_id': 'OGqz9G-JO0E', 'start': 770.6, 'end': 781.835, 'duration': 11.24,
-'text': 'اللهم من ارادنا وبلادنا وبلاد المسلمين بسوء اللهم فاشغله في نفسه ورد كيده في نحره واجعل تدبيره تدميره يا رب العالمين',
-'type': 'c', 'file_path': '87edeceb-5349-4210-89ad-8c3e91e54062_OGqz9G-JO0E.wav',
-'audio': {'path': None,
-'array': array([
-            0.05938721,
-            0.0539856,
-            0.03460693, ...,
-            0.00393677,
-            0.01745605,
-            0.03045654
-        ]), 'sampling_rate': 16000
-    }
-}
-```
-
-### Data Fields
-
-`video_id` (`string`): An id for the video that the voice has been created from
-
-`start` (`float64`): The start of the audio's chunk
-
-`end` (`float64`): The end of the audio's chunk
-
-`duration` (`float64`): The duration of the chunk
-
-`text` (`string`): The text of the chunk
-
-`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]`.
-
-`type` (`string`): It refers to the data set type, either clean or noisy where "c: clean and n: noisy"
-
-'file_path' (`string`): A path for the audio chunk
-
-"audio" ("audio"): Audio for the chunk
-
-### Data Splits
-
-The speech material has been subdivided into portions for train, dev, test.
-
-The dataset splits has clean and noisy data that can be determined by type field.
-
-
-
-
-### Citation Information
-
-```
-@INPROCEEDINGS{10022652,
-  author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
-  booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)}, 
-  title={MASC: Massive Arabic Speech Corpus}, 
-  year={2023},
-  volume={},
-  number={},
-  pages={1006-1013},
-  doi={10.1109/SLT54892.2023.10022652}}
-}
-```

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