Update aesdd.py

aesdd.py

@@ -12,7 +12,11 @@
 # 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.
-
+#------------------------------------------------------------------------------
+# Standard Libraries
+import datasets
+import os
+#------------------------------------------------------------------------------
 """Acted Emotional Speech Dynamic Database v1.0"""
 
 _CITATION = """\
@@ -45,6 +49,103 @@ Consequently, around 500 utterances occured in the final database.
 
 _HOMEPAGE = "http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/"
 
+_LICENSE = "CC BY 4.0"
 
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1-pelMaCrfwoUCmwxUtlacRUBwbFnXlXA"
 
-
+#------------------------------------------------------------------------------
+# Define Dataset Configuration (e.g., subset of dataset, but it is not used here.)
+class AESDDConfig(datasets.BuilderConfig):
+    #--------------------------------------------------------------------------
+    def __init__(self, name, description, homepage, data_url):
+        
+        super(AESDDConfig, self).__init__(
+            name = self.name,
+            version = datasets.Version("1.0.0"),
+            description = self.description,
+        )
+        self.name = name
+        self.description = description
+        self.homepage = homepage
+        self.data_url = data_url
+#------------------------------------------------------------------------------
+# Define Dataset Class
+class AESDD(datasets.GeneratorBasedBuilder):
+    #--------------------------------------------------------------------------
+    BUILDER_CONFIGS = [AESDDConfig(
+        name = "AESDD",
+        description = _DESCRIPTION,
+        homepage = _HOMEPAGE,
+        data_url = _DATA_URL
+        )]
+    #--------------------------------------------------------------------------
+    '''
+        Define the "column header" (feature) of a datum.
+        3 Features:
+            1) path_to_file
+            2) audio samples
+            3) emotion label
+            4) utterance: 1,2,...,20
+            5) speaker id
+    '''
+    def _info(self):
+        
+        features = datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate = 441000),
+                    "label": datasets.ClassLabel(
+                        names = [
+                            "anger",
+                            "disgust",
+                            "fear",
+                            "happiness",
+                            "sadness",
+                        ]),
+                    "utterance": datasets.Value("float"),
+                    "speaker": datasets.Value("float")
+                }
+            )
+        
+        # return dataset info and data feature info
+        return datasets.DatasetInfo(
+            description = _DESCRIPTION,
+            features = features,
+            homepage = _HOMEPAGE,
+            citation = _CITATION,
+        )
+    #--------------------------------------------------------------------------
+    def _split_generators(self, dl_manager):
+        
+        dataset_path = dl_manager.download_and_extract(self.config.data_url)
+        
+        return [
+            datasets.SplitGenerator(
+                # set the whole dataset as "training set". No worry, can split later! 
+                name = datasets.Split.TRAIN,
+                # _generate_examples()'s parameters, thus name must match!
+                gen_kwargs = {
+                    "dataset_path": dataset_path
+                },
+            )
+        ]
+    #--------------------------------------------------------------------------   
+    def _generate_examples(self, dataset_path):
+        '''
+            Get the audio file and set the corresponding labels
+        '''
+        key = 0
+        for dir_name in ["anger", "disgust", "fear", "happiness", "sadness"]:
+            dir_path = dataset_path + "/AESDD/" + dir_name
+            for file_name in os.listdir(dir_path):
+                if file_name.endswith(".wav"):
+                    yield key, {
+                        "path": dir_path + "/" + file_name,
+                        # huggingface dataset's will use soundfile to read the audio file
+                        "audio": dir_path + "/" + file_name,
+                        "label": dir_name,
+                        "utterance": float(file_name[1:3]),
+                        "speaker": float(file_name[file_name.find("(")+1:file_name.find(")")]),
+                    }
+                    key += 1    
+#------------------------------------------------------------------------------