snow-mountain.py

# 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 datasets
import pandas as pd
from scipy.io import wavfile


_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])
        path_to_audios = "/".join(["data/cleaned", self.config.name])

        train_splits = [
                datasets.SplitGenerator(
                    name="train_500",
                    gen_kwargs={
                        "filepath": downloaded_files["train_500"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="train_1000",
                    gen_kwargs={
                        "filepath": downloaded_files["train_1000"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="train_2500",
                    gen_kwargs={
                        "filepath": downloaded_files["train_2500"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="train_short",
                    gen_kwargs={
                        "filepath": downloaded_files["train_short"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="train_full",
                    gen_kwargs={
                        "filepath": downloaded_files["train_full"],
                        "path_to_audios": path_to_audios,
                    },
                ),
        ]

        dev_splits = [
                datasets.SplitGenerator(
                    name="val_500",
                    gen_kwargs={
                        "filepath": downloaded_files["val_500"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="val_1000",
                    gen_kwargs={
                        "filepath": downloaded_files["val_1000"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="val_2500",
                    gen_kwargs={
                        "filepath": downloaded_files["val_2500"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="val_short",
                    gen_kwargs={
                        "filepath": downloaded_files["val_short"],
                        "path_to_audios": path_to_audios,
                    },
                ),
                datasets.SplitGenerator(
                    name="val_full",
                    gen_kwargs={
                        "filepath": downloaded_files["val_full"],
                        "path_to_audios": path_to_audios,
                    },
                ),
        ]

        test_splits = [
                datasets.SplitGenerator(
                    name="test_common",
                    gen_kwargs={
                        "filepath": downloaded_files["test_common"],
                        "path_to_audios": path_to_audios,
                    },
                ),
        ]
        return train_splits + dev_splits + test_splits

        
    def _generate_examples(self, filepath, path_to_audios):
        key = 0
        with open(filepath) as f:
            data_df = pd.read_csv(f,sep=',')
            transcripts = []
            for index,row in data_df.iterrows():
                downloaded_audio = dl_manager.download(row["path"])
                samplerate, audio_data = wavfile.read(downloaded_audio)
                yield key, {
                        "sentence": row["sentence"],
                        "path": row["path"],
                        "audio":{"path": row["path"], "bytes": audio_data}
                    }
                key+=1