dataset card, loading script

README.md

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+---
+pretty_name: 'Snow Mountain'
+language:
+- hi
+- bgc
+- kfs
+- dgo
+- bhd
+- gbk
+- xnr
+- kfx
+- mjl
+- kfo
+- bfz
+annotations_creators:
+- ?
+language_creators:
+- ?
+license: []
+multilinguality:
+- multilingual
+size_categories:
+- 
+source_datasets:
+- Snow Mountain
+tags: []
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+configs:
+  - hi
+  - bgc
+dataset_info:
+  - config_name: hi
+    features:
+      - name: Unnamed
+        dtype: int64
+      - name: sentence
+        dtype: string
+      - name: path
+        dtype: string
+    splits:
+      - name: train_500
+        num_examples: 400
+      - name: val_500  
+        num_examples: 100
+      - name: train_1000
+        num_examples: 800
+      - name: val_1000
+        num_examples: 200
+      - name: test_common
+        num_examples: 500
+    dataset_size: 71.41 hrs
+  - config_name: bgc
+    features:
+      - name: Unnamed
+        dtype: int64
+      - name: sentence
+        dtype: string
+      - name: path
+        dtype: string
+    splits:
+      - name: train_500
+        num_examples: 400
+      - name: val_500  
+        num_examples: 100
+      - name: train_1000
+        num_examples: 800
+      - name: val_1000
+        num_examples: 200
+      - name: test_common
+        num_examples: 500
+    dataset_size: 27.41 hrs
+
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [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 Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [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:**
+- **Repository:https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain**
+- **Paper:https://arxiv.org/abs/2206.01205**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription. 
+
+We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.
+
+### Supported Tasks and Leaderboards
+
+Atomatic speech recognition, Speaker recognition, Language identification
+
+### Languages
+
+Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui 
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+The Bible recordings were done in a studio setting by native speakers. 
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
+
+
+### Citation Information
+
+@inproceedings{Raju2022SnowMD,
+  title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},
+  author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},
+  year={2022}
+}
+
+
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

snow_mountain.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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import os
+import csv
+import json
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{Raju2022SnowMD,
+  title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},
+  author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},
+  year={2022}
+}
+
+"""
+
+_DESCRIPTION = """\
+The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible 
+in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single 
+speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around 
+the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription.
+"""
+
+_HOMEPAGE = "https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain"
+
+_LICENSE = ""
+
+_URL = "https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain/"
+
+_FILES = {}
+_LANGUAGES = ['hindi']
+for lang in _LANGUAGES:
+    file_dic = {
+        "train_500": f"data/experiments/{lang}/train_500.csv",
+        "val_500": f"data/experiments/{lang}/val_500.csv",
+        "train_1000": f"data/experiments/{lang}/train_1000.csv",
+        "val_1000": f"data/experiments/{lang}/val_1000.csv",
+        "train_2500": f"data/experiments/{lang}/train_2500.csv",
+        "val_2500": f"data/experiments/{lang}/val_2500.csv",
+        "train_short": f"data/experiments/{lang}/train_short.csv",
+        "val_short": f"data/experiments/{lang}/val_short.csv",
+        "train_full": f"data/experiments/{lang}/train_full.csv",
+        "val_full": f"data/experiments/{lang}/val_full.csv",
+        "test_common": f"data/experiments/{lang}/test_common.csv",
+    }
+    _FILES[lang] = file_dic
+
+
+class Test(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = []
+    for lang in _LANGUAGES: 
+        text = lang.capitalize()+" data"
+        BUILDER_CONFIGS.append(datasets.BuilderConfig(name=f"{lang}", version=VERSION, description=text))
+    
+
+    DEFAULT_CONFIG_NAME = "hindi" 
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "sentence": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "path": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("sentence", "path"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        downloaded_files = dl_manager.download(_FILES[self.config.name])
+
+        train_splits = [
+                datasets.SplitGenerator(
+                    name="train_500",
+                    gen_kwargs={
+                        "filepath": downloaded_files["train_500"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train_1000",
+                    gen_kwargs={
+                        "filepath": downloaded_files["train_1000"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train_2500",
+                    gen_kwargs={
+                        "filepath": downloaded_files["train_2500"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train_short",
+                    gen_kwargs={
+                        "filepath": downloaded_files["train_short"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train_full",
+                    gen_kwargs={
+                        "filepath": downloaded_files["train_full"],
+                    },
+                ),
+        ]
+
+        dev_splits = [
+                datasets.SplitGenerator(
+                    name="val_500",
+                    gen_kwargs={
+                        "filepath": downloaded_files["val_500"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="val_1000",
+                    gen_kwargs={
+                        "filepath": downloaded_files["val_1000"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="val_2500",
+                    gen_kwargs={
+                        "filepath": downloaded_files["val_2500"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="val_short",
+                    gen_kwargs={
+                        "filepath": downloaded_files["val_short"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="val_full",
+                    gen_kwargs={
+                        "filepath": downloaded_files["val_full"],
+                    },
+                ),
+        ]
+
+        test_splits = [
+                datasets.SplitGenerator(
+                    name="test_common",
+                    gen_kwargs={
+                        "filepath": downloaded_files["test_common"],
+                    },
+                ),
+        ]
+        return train_splits + dev_splits + test_splits
+
+        
+    def _generate_examples(self, filepath):
+        key = 0
+        with open(filepath) as f:
+            data_df = pd.read_csv(f,sep=',')
+            transcripts = []
+            for index,row in data_df.iterrows():
+                yield key, {
+                        "sentence": row["sentence"],
+                        "path": row["path"],
+                    }
+                key+=1