updating the code again!!

bn_emotion_speech_corpus.py

@@ -1,73 +1,90 @@
+import json
+import os
+
 import datasets
 
 _CITATION = """\
-@dataset{sadia_sultana_2021_4526477,
-  author       = {Sadia Sultana},
-  title        = {SUST Bangla Emotional Speech Corpus (SUBESCO)},
-  month        = feb,
-  year         = 2021,
-  note         = {{This database was created as a part of PhD thesis 
-                   project of the author Sadia Sultana. It was
-                   designed and developed by the author in the
-                   Department of Computer Science and Engineering  of
-                   Shahjalal University of Science and Technology.
-                   Financial grant was supported by the university.
-                   If you use the dataset please cite SUBESCO and the
-                   corresponding academic journal publication in Plos
-                   One.}},
-  publisher    = {Zenodo},
-  version      = {version - 1.1},
-  doi          = {10.5281/zenodo.4526477},
-  url          = {https://doi.org/10.5281/zenodo.4526477}
-}
 """
 
 _DESCRIPTION = """\
-SUST Bangla Emotional Speech Corpus Dataset
+The corpus contains roughly 360 hours of audio and transcripts in Kannada language. The transcripts have beed de-duplicated using exact match deduplication.
 """
-_HOMEPAGE = "https://huggingface.co/datasets/sustcsenlp/bn_emotion_speech_corpus"
 
-_LICENSE = ""
+_HOMEPAGE = ""
+
+_LICENSE = "https://creativecommons.org/licenses/"
 
-#_REPO = "https://huggingface.co/datasets/sustcsenlp/SUBESCO/resolve/main/corpus/speech/subesco.tar.gz"
 
-class AudioSet(datasets.GeneratorBasedBuilder):
-    """"""
+_METADATA_URLS = {
+    "train": "train.jsonl",
+}
+_URLS = {
+    "train": "subesco.tar.gz",
+    
+}
+
+class KannadaASRCorpus(datasets.GeneratorBasedBuilder):
+    """Kannada ASR Corpus contains transcribed speech corpus for training ASR systems for Kannada language."""
 
+    VERSION = datasets.Version("1.1.0")
     def _info(self):
+        features = datasets.Features(
+            {
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),
+                "speaker_gender": datasets.Value("string"),
+                "speaker_number": datasets.Value("int32"),
+                "speaker_name": datasets.Value("string"),
+                "sentence_number": datasets.Value("int32"),
+                "emotional_state": datasets.Value("string"),
+                "take_number": datasets.Value("int32"),
+            }
+        )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    'text': datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16000),
-                }
-            ),
+            features=features,
             supervised_keys=None,
             homepage=_HOMEPAGE,
+            license=_LICENSE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        audio_archive = dl_manager.download("https://huggingface.co/datasets/sustcsenlp/bn_emotion_speech_corpus/resolve/main/subesco.tar.gz")
-        audio_iters = dl_manager.iter_archive(audio_archive)
+        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 = "train"
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "audios": audio_iters
-                }
-            ),
+                    "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, audios):
-        """This function returns the examples in the raw (text) form."""
-        idx = 0
-        for filepath, audio in audios:
-            description = filepath.split('/')[-1][:-4]
-            description = description.replace('_', ' ')
-            yield idx, {
-                "audio": {"path": filepath, "bytes": audio.read()},
-                "text": description,
-            }
-            idx += 1
+        
+    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