Snap

pyproject.toml

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+[tool.poetry]
+name = "vps-clustering-benchmark"
+version = "0.1.0"
+description = ""
+authors = ["RafalCer <rafal.cerniawski@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+numpy = "^1.26.4"
+torch = "^2.3.0"
+nemo-toolkit = {extras = ["asr"], version = "1.23.0"}
+youtokentome = { git = "https://github.com/conversy-ai/YouTokenToMe.git", branch = "master" }
+pandas = "^2.2.2"
+pydub = "^0.25.1"
+protobuf = "^3.20.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

voice_prints.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6cf4081989aa7ced1264898011be265b7567d78e9f456fc329fde435add0119
+size 187836

vps_clustering_benchmark.py

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+import os
+import json
+import datasets
+import pandas as pd
+import numpy as np
+from typing import List
+from tqdm import tqdm
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+This dataset consists of a small sample of audio clips with annotated
+speaker identities, their age and gender, diarization-based speech segments,
+and transcription. The dataset is intended for the benchmarking of VBI-core.
+"""
+
+_HF_REPO_PATH = "https://huggingface.co/datasets/conversy/vps_clustering_benchmark/resolve/main/voice_prints.pkl"
+
+
+class VPClusteringBenchmarkConfig(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(VPClusteringBenchmarkConfig, self).__init__(
+            name=name,
+            version=version,
+            **kwargs
+        )
+
+
+class VPClusteringBenchmark(datasets.GeneratorBasedBuilder):
+    """Conversy benchmark"""
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        VPClusteringBenchmarkConfig(
+                name="VPClusteringBenchmark",
+                version=VERSION,
+                description="Conversy Benchmark for ML models evaluation",
+                features=["segment_id", "filename", "speaker", "duration", "vp",
+                          "segment_clean"],
+                data_url=_HF_REPO_PATH,
+                nb_data_shards=1)
+    ]
+
+    def _info(self):
+        description = (
+            "Voice Print Clustering Benchmark"
+        )
+        features = datasets.Features(
+            {
+                "segment_id": datasets.Value("string"),
+                "filename": datasets.Value("string"),
+                "speaker": datasets.Value("string"),
+                "duration": datasets.Value("float32"),
+                "segment_clean": datasets.Value("bool"),
+                "vp": datasets.Array2D(shape=(192,), dtype="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"],
+                "vp": np.array(row["vp"], dtype=np.float32)  # Ensure vp is a NumPy array
+            }