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.gitattributes

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README.md

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 ---
-license: mit
+license: cc-by-sa-4.0
+dataset_info:
+- config_name: original
+  features:
+  - name: utterance_id
+    dtype: string
+  - name: speaker_id
+    dtype: string
+  - name: utterance
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: transcription
+    dtype: string
+  - name: num_frames
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 40925646
+    num_examples: 157905
+  download_size: 9340083067
+  dataset_size: 40925646
+- config_name: cleaned
+  features:
+  - name: utterance_id
+    dtype: string
+  - name: speaker_id
+    dtype: string
+  - name: utterance
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: transcription
+    dtype: string
+  - name: num_frames
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 40925646
+    num_examples: 157905
+  download_size: 5978669282
+  dataset_size: 40925646
 ---
+
+# Dataset Card for OpenSLR Nepali Large ASR Cleaned
+
+## Table of Contents
+- [Dataset Card for OpenSLR Nepali Large ASR Cleaned](#dataset-card-for-openslr-nepali-large-asr-cleaned)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [How to use?](#how-to-use)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  
+
+## Dataset Description
+
+- **Homepage:** [Original OpenSLR Large Nepali ASR Dataset link](https://www.openslr.org/54/)
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Sagar Sapkota](mailto:spkt.sagar@gmail.com)
+
+### Dataset Summary
+
+This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
+The data set has been manually quality-checked, but there might still be errors.
+
+The audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.
+
+For your reference, following was the function applied on each of the original openslr utterances.
+```python
+import torchaudio
+
+SAMPLING_RATE = 16000
+
+def process_audio_file(orig_path, new_path):
+    """Read and process file in `orig_path` and save it to `new_path`"""
+    waveform, sampling_rate = torchaudio.load(orig_path)
+    if sampling_rate != SAMPLING_RATE:
+        waveform = torchaudio.functional.resample(waveform, sampling_rate, SAMPLING_RATE)
+    #  trim end silences with Voice Activity Detection
+    waveform = torchaudio.functional.vad(waveform, sample_rate=SAMPLING_RATE)
+    torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE)
+```
+
+### How to use?
+
+There are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.
+1. First, to download the original dataset with HuggingFace's [Dataset](https://huggingface.co/docs/datasets/) API:
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="original", split='train')
+```
+
+2. To download the preprocessed dataset:
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="cleaned", split='train')
+```
+
+### Supported Tasks and Leaderboards
+
+- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition. 
+
+### Languages
+
+Nepali
+
+## Dataset Structure
+
+### Data Instances
+
+```js
+{
+    'utterance_id': 'e1c4d414df',
+    'speaker_id': '09da0',
+    'utterance': {
+        'path': '/root/.cache/huggingface/datasets/downloads/extracted/e3cf9a618900289ecfd4a65356633d7438317f71c500cbed122960ab908e1e8a/cleaned/asr_nepali/data/e1/e1c4d414df.flac',
+        'array': array([-0.00192261, -0.00204468, -0.00158691, ...,  0.00323486, 0.00256348,  0.00262451], dtype=float32),
+        'sampling_rate': 16000
+    },
+    'transcription': '२००५ मा बिते',
+    'num_frames': 42300
+}
+```
+
+### Data Fields
+
+- utterance_id: a string identifying the utterances
+- speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance
+- utterance:
+    - path: path to the utterance .flac file
+    - array: numpy array of the utterance
+    - sampling_rate: sample rate of the utterance
+- transcription: Nepali text which spoken in the utterance
+- num_frames: length of waveform array
+
+### Data Splits
+
+The dataset is not split. The consumer should split it as per their requirements.

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openslr-nepali-asr-cleaned.py

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+# Copyright 2020 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.
+"""Leading and Trailing Silences Removed Large Nepali ASR Dataset"""
+
+import os
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{kjartansson-etal-sltu2018,
+title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
+author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+year  = {2018},
+address = {Gurugram, India},
+month = aug,
+pages = {52--55},
+URL   = {http://dx.doi.org/10.21437/SLTU.2018-11}
+}
+"""
+
+_DESCRIPTION = """\
+This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
+The data set has been manually quality checked, but there might still be errors.
+
+The audio files are sampled at rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.
+"""
+
+# Official homepage for the dataset
+_HOMEPAGE = "https://www.openslr.org/54/"
+
+# The licence for the dataset
+_LICENSE = "license:cc-by-sa-4.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    'cleaned': {
+        "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_clean.tsv",
+        "zipfiles": [
+            f"https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/asr_nepali_{k}.zip"
+            for k in [*range(10), *'abcdef']
+        ],
+    },
+    'original': {
+        "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_orig.tsv",
+        "zipfiles": [
+            f"https://www.openslr.org/resources/54/asr_nepali_{k}.zip"
+            for k in [*range(10), *'abcdef']
+        ],
+    },
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class OpenslrNepaliAsrCleaned(datasets.GeneratorBasedBuilder):
+    """End Silences Removed Large Nepali ASR Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="original", version=VERSION,
+                               description="All original utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
+        datasets.BuilderConfig(name="cleaned", version=VERSION,
+                               description="All cleaned utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
+    ]
+
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "original"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "utterance_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "utterance": datasets.Audio(sampling_rate=16000),
+                "transcription": datasets.Value("string"),
+                "num_frames": datasets.Value("int32"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            # Here we define them above because they are different between the two configurations
+            features=features,
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        index_file = dl_manager.download(_URLS[self.config.name]['index_file'])
+        zip_paths = [item for sublist in [
+            dl_manager.download(
+                _URLS[self.config.name]['zipfiles'][i:i+4]
+            ) for i in range(0, len(_URLS[self.config.name]['zipfiles']), 4)
+        ] for item in sublist]
+        audio_paths = dict(zip([url[-5] for url in _URLS[self.config.name]["zipfiles"]],
+                           dl_manager.extract(zip_paths)))
+        for path in zip_paths:
+            if os.path.exists(path):
+                os.remove(path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "index_file": index_file,
+                    "audio_paths": audio_paths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, index_file, audio_paths):
+        with open(index_file, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter='\t')
+            for key, row in enumerate(reader):
+                if self.config.name == 'cleaned':
+                    path = os.path.join(
+                        audio_paths[row['utterance_id'][0]], 'cleaned',
+                        'asr_nepali', 'data', row['utterance_id'][:2],
+                        f"{row['utterance_id']}.flac"
+                    )
+                else:
+                    path = os.path.join(
+                        audio_paths[row['utterance_id'][0]],
+                        'asr_nepali', 'data', row['utterance_id'][:2],
+                        f"{row['utterance_id']}.flac"
+                    )
+                yield key, {
+                    "utterance_id": row['utterance_id'],
+                    "speaker_id": row['speaker_id'],
+                    "utterance": path,
+                    "transcription": row['transcription'],
+                    "num_frames": int(row['num_frames']),
+                }