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BA_Datensatz_V2.py

@@ -5,12 +5,8 @@ import os
 import csv
 
 # Defining Access
-_DATA_URL = "https://huggingface.co/datasets/LennyBijan/BA_Datensatz_V2/resolve/main/data/BA_Datensatz_V2.tar.gz"
+_AUDIO_URL = "https://huggingface.co/datasets/LennyBijan/BA_Datensatz_V2/resolve/main/data"
 
-_PROMPTS_URLS = {
-    "train": "data/metadata-train.csv",
-    "test": "data/metadata-test.csv",
-}
 
 class BA_Datensatz_V2(datasets.GeneratorBasedBuilder):
 
@@ -27,52 +23,55 @@ class BA_Datensatz_V2(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        audio_path = dl_manager.download(_DATA_URL)
-        prompts_paths = dl_manager.download(_PROMPTS_URLS)
+        dl_manager.download_config.ignore_url_params = True
 
-        train_dir = "BA_Datensatz_V2/train"
-        test_dir = "BA_Datensatz_V2/test"
+        audio_path = {}
+        local_extracted_archive = {}
+        metadata_path = {}
+        split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST}
+        for split in split_type:
+            audio_path[split] = dl_manager.download(f"{_DATA_URL}/audio_{split}.tgz")
+            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
+            metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/metadata_{split}.csv.gz")
+        path_to_clips = "BA_Datensatz_V2"
+
+      
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "audio_files": dl_manager.iter_archive(audio_path),
-                    "metadata_path": prompts_paths["train"],
-                    "path_to_clips": train_dir + "/wav/"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
+                name=split_type[split],
                 gen_kwargs={
-                    "audio_files": dl_manager.iter_archive(audio_path),
-                    "metadata_path": prompts_paths["test"],
-                    "path_to_clips": test_dir + "/wav/"
+                    "local_extracted_archive": local_extracted_archive[split],
+                    "audio_files": dl_manager.iter_archive(audio_path[split]),
+                    "metadata_path": dl_manager.download_and_extract(metadata_path[split]),
+                    "path_to_clips": path_to_clips,
                 },
-            ),
+            ) for split in split_type
         ] 
 
-    def _generate_examples(self, audio_files, metadata_path, path_to_clips):
+    def _generate_examples(self, audio_files, metadata_path, path_to_clips,local_extracted_archive):
+        data_fields = list(self._info().features.keys())
         metadata = {}
-        with open(metadata_path, 'rt', encoding='utf-8') as f:
-            reader = csv.reader(f)
+         with open(metadata_path, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
             for row in reader:
-                filename_with_extension, sentence = row
-                speaker_id = filename_with_extension.split('_')[0]
-                # Construct path relative to the dataset structure
-                relative_path = os.path.join(path_to_clips, speaker_id, filename_with_extension)
-                metadata[relative_path] = {
-                    "speaker_id": speaker_id,
-                    "path": relative_path,
-                    "sentence": sentence,
-                }
+                if self.config.name == "_all_" or self.config.name == row["language"]:
+                    row["path"] = os.path.join(path_to_clips, row["path"])
+                    # if data is incomplete, fill with empty values
+                    for field in data_fields:
+                        if field not in row:
+                            row[field] = ""
+                    metadata[row["path"]] = row
         id_ = 0
-        for path, file_content in audio_files:
-            if not path.endswith('/') and path.startswith(path_to_clips): # This checks if the file is relevant to the current split by checking its path
-                if path in metadata:
-                    audio_data = {
-                        "path": metadata[path]["path"],
-                        "bytes": file_content.read(),
+        for path, f in audio_files:
+            if path in metadata:
+                result = dict(metadata[path])
+                # set the audio feature and the path to the extracted file
+                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
                         }
                     yield id_, {**metadata[path], "audio": audio_data}
                     id_ += 1

data/audio_test.tgz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d9688b3f7092e579eef966101b0d441f73d7f0639b195c3f623b392b4ba9fdd
+size 300579710

data/audio_train.tgz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4c574057441b779a73933afe08f80f7172ecaed08f3c2fd43199c5f224b325b
+size 929735209

data/metadata_test.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fafa4d28d074f972ede61ae2316ed5dbe9ff49baa16a8de5411159261e910ab2
+size 66008

data/metadata_train.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a4c504bde885b56dbb822bec99ec2c19e7cb73b165ff64c97251a9f54a947c5
+size 221073