# 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.

"""Filtered Bengali ASR corpus collected from madasr, indictts, kathbath, openslr53, openslr37, and ai4bharat corpora filtered for duration between 2 - 30 secs"""


import json
import os

import datasets

_CITATION = """
"""

_DESCRIPTION = """\
The corpus contains roughly 500 hours of audio and transcripts in Bangla language. 
The transcripts have beed de-duplicated using exact match deduplication and audio has be converted to 16000 samples
"""

_HOMEPAGE = ""

_LICENSE = "https://creativecommons.org/licenses/"


_METADATA_URLS = {
    "train": "data/train.jsonl",
}
_URLS = {
    "train": "data/train.tar.gz",
    
}

class BengaliASRCorpus(datasets.GeneratorBasedBuilder):
    """Bengali ASR Corpus contains transcribed speech corpus for training ASR systems for Bengali language."""

    VERSION = datasets.Version("1.1.0")
    def _info(self):
        features = datasets.Features(
            {
                "audio": datasets.Audio(sampling_rate=16_000),
                "path": datasets.Value("string"),
                "sentence": datasets.Value("string"),
                "duration": datasets.Value("float"),
                "transcript_length": datasets.Value("float")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("sentence", "label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        metadata_paths = dl_manager.download(_METADATA_URLS)
        train_archive = dl_manager.download(_URLS["train"])
        local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None
        train_dir = "mp3"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "metadata_path": metadata_paths["train"],
                    "local_extracted_archive": local_extracted_train_archive,
                    "path_to_clips": train_dir,
                    "audio_files": dl_manager.iter_archive(train_archive),
                },
            ),   
        ]
        
    def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files):
        """Yields examples as (key, example) tuples."""
        examples = {}
        with open(metadata_path, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                examples[data["path"]] = data
        inside_clips_dir = False
        id_ = 0
        for path, f in audio_files:
            if path.startswith(path_to_clips):
                inside_clips_dir = True
                if path in examples:
                    result = examples[path]
                    path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
                    result["audio"] = {"path": path, "bytes": f.read()}
                    result["path"] = path
                    yield id_, result
                    id_ += 1
            elif inside_clips_dir:
                break