tamil_eng_data.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.

"""Simple sentences Dataset - contains 90 mins of speech data"""

import csv
import json
import os

import datasets

_CITATION = """\
@misc{simpledata_1,
  title = {Whisper model for tamil-to-eng translation},
  publisher = {Achitha},
  year = {2022},
}
@misc{simpledata_2,
  title = {Fine-tuning whisper model},
  publisher = {Achitha},
  year = {2022},
}
"""
_DESCRIPTION = """\
The data contains roughly one and half hours of audio and transcripts in Tamil language.
"""

_HOMEPAGE = ""

_LICENSE = "MIT"


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

class simple_data(datasets.GeneratorBasedBuilder):
   

    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"),
                "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"])
        test_archive = dl_manager.download(_URLS["test"])
        local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None
        local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None
        test_archive = dl_manager.download(_URLS["test"])
        train_dir = "train"
        test_dir = "test"

        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),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "metadata_path": metadata_paths["test"],
                    "local_extracted_archive": local_extracted_test_archive,
                    "path_to_clips": test_dir,
                    "audio_files": dl_manager.iter_archive(test_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