Create 10th_science_tamil_to_english.py

10th_science_tamil_to_english.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.
+
+"""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