Snap

clustering_segments.py

@@ -0,0 +1,97 @@
+import datasets
+import pandas as pd
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+_DATA_PATH = "https://huggingface.co/datasets/conversy/clustering_segments/resolve/main/dataset.pkl"
+
+class ClusteringSegmentsConfig(datasets.BuilderConfig):
+    """BuilderConfig for Conversy Benchmark."""
+
+    def __init__(self, name, version, **kwargs):
+        """BuilderConfig for Conversy Benchmark.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.name = name
+        self.version = version
+        self.features = kwargs.pop("features", None)
+        self.description = kwargs.pop("description", None)
+        self.data_url = kwargs.pop("data_url", None)
+        self.nb_data_shards = kwargs.pop("nb_data_shards", None)
+
+        super(ClusteringSegmentsConfig, self).__init__(
+            name=name,
+            version=version,
+            **kwargs
+        )
+
+
+class ClusteringSegments(datasets.GeneratorBasedBuilder):
+    """Conversy benchmark"""
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        ClusteringSegmentsConfig(
+                name="VPClusteringBenchmark",
+                version=VERSION,
+                description="Conversy Benchmark for ML models evaluation",
+                features=["segment_id", "filename", "speaker", "duration", "vp",
+                          "start", "end", "readable_start", "readable_end",
+                          "segment_clean"],
+                data_url=_DATA_PATH,
+                nb_data_shards=1)
+    ]
+
+    def _info(self):
+        description = (
+            "Voice Print Clustering Benchmark"
+        )
+        features = datasets.Features(
+            {
+                "segment_id": datasets.Value("int32"),
+                "filename": datasets.Value("string"),
+                "speaker": datasets.Value("string"),
+                "duration": datasets.Value("float32"),
+                "segment_clean": datasets.Value("bool"),
+                "start": datasets.Value("float32"),
+                "end": datasets.Value("float32"),
+                "readable_start": datasets.Value("string"),
+                "readable_end": datasets.Value("string"),
+                "vp": datasets.Sequence(datasets.Value("float32"))
+            })
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            version=self.config.version
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_url = self.config.data_url
+        downloaded_file = dl_manager.download_and_extract(data_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"file_path": downloaded_file},
+            ),
+        ]
+
+    def _generate_examples(self, file_path):
+        """Yields examples."""
+        df = pd.read_pickle(file_path)
+
+        for idx, row in df.iterrows():
+            yield idx, {
+                "segment_id": row["segment_id"],
+                "filename": row["filename"],
+                "speaker": row["speaker"],
+                "duration": row["duration"],
+                "segment_clean": row["segment_clean"],
+                "start": row['start'],
+                "end": row['end'],
+                "readable_start": row['readable_start'],
+                "readable_end": row['readable_end'],
+                "vp": np.asarray(row["vp"], dtype=np.float32)
+            }