Snap

clustering_segments.py

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+import datasets
+import pandas as pd
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+_DATA_PATH = "https://huggingface.co/datasets/conversy/clustering_files/resolve/main/dataset.pkl"
+
+class ClusteringFilesConfig(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(ClusteringFilesConfig, self).__init__(
+            name=name,
+            version=version,
+            **kwargs
+        )
+
+
+class ClusteringFiles(datasets.GeneratorBasedBuilder):
+    """Conversy benchmark"""
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        ClusteringFilesConfig(
+                name="ClusteringFiles",
+                version=VERSION,
+                description="Conversy Benchmark for ML models evaluation",
+                features=["filename", "segments"],
+                data_url=_DATA_PATH,
+                nb_data_shards=1)
+    ]
+
+    def _info(self):
+        description = (
+            "Voice Print Clustering Benchmark"
+        )
+        features = datasets.Features(
+            {
+                "filename": datasets.Value("string"),
+                "segments": [
+                    {
+                        "segment_id": datasets.Value("int32"),
+                        "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)
+
+        files = {}
+        for idx, row in df.iterrows():
+            if row["filename"] not in files:
+                files[row["filename"]] = {
+                    "filename": row["filename"],
+                    "segments": []
+                }
+            files[row["filename"]]["segments"].append({
+                "segment_id": row["segment_id"],
+                "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)
+            })
+
+        for idx, file_data in enumerate(files.values()):
+            yield idx, file_data

dataset.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7be56f2d87b72d421e97b8158176eebf3317e0c4ac697e4a9fa671ad1eec949a
+size 198842