Delete tamil_eng_data.py

tamil_eng_data.py

@@ -1,129 +0,0 @@
-# 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