add first version of the mile dataset

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- ta
+language_creators:
+- expert-generated
+license:
+- cc-by-2.0
+multilinguality:
+- monolingual
+pretty_name: IISc-MILE Tamil ASR Corpus
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- Tamil ASR
+- Speech Recognition
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://www.openslr.org/127/
+- **Repository:** https://github.com/MILE-IISc
+- **Paper:** https://arxiv.org/abs/2207.13331
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Tamil transcribed speech corpus for ASR 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+- Tamil
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Attribution 2.0 Generic (CC BY 2.0) 
+
+### Citation Information
+@misc{mile_1,
+  doi = {10.48550/ARXIV.2207.13331},
+  url = {https://arxiv.org/abs/2207.13331},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+
+@misc{mile_2,
+  doi = {10.48550/ARXIV.2207.13333},
+  url = {https://arxiv.org/abs/2207.13333},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+
+### Contributions
+
+Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset.

mile_dataset.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.
+
+"""IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language. It contains ~150 hours of read speech data collected from 531 speakers in a noise-free recording environment with high quality USB microphones. """
+
+
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@misc{mile_1,
+  doi = {10.48550/ARXIV.2207.13331},
+  url = {https://arxiv.org/abs/2207.13331},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+
+@misc{mile_2,
+  doi = {10.48550/ARXIV.2207.13333},
+  url = {https://arxiv.org/abs/2207.13333},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+"""
+
+_DESCRIPTION = """\
+IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language. It contains ~150 hours of read speech data collected from 531 speakers in a noise-free recording environment with high quality USB microphones. 
+"""
+
+_HOMEPAGE = "https://www.openslr.org/127/"
+
+_LICENSE = "Attribution 2.0 Generic (CC BY 2.0)"
+
+
+_METADATA_URLS = {
+    "train": "data/train.jsonl",
+    "test": "data/test.jsonl"
+}
+_URLS = {
+    "train": "data/train.tar.gz",
+    "test": "data/test.tar.gz",
+    
+}
+
+class MileDataset(datasets.GeneratorBasedBuilder):
+    """IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language."""
+
+    VERSION = datasets.Version("1.1.0")
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "file_name": datasets.Value("string"),
+                "sentence": datasets.Value("string"),
+            }
+        )
+        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 + "/mp3",
+                    "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 + "/mp3",
+                    "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["file_name"]] = 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["file_name"] = path
+                    yield id_, result
+                    id_ += 1
+            elif inside_clips_dir:
+                break
+