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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/vps_clustering_benchmark/resolve/main/dataset.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",
"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="segments",
gen_kwargs={"file_path": downloaded_file, "split": "segments"},
),
datasets.SplitGenerator(
name="files",
gen_kwargs={"file_path": downloaded_file, "split": "files"},
),
]
def _generate_examples(self, file_path, split):
"""Yields examples."""
df = pd.read_pickle(file_path)
if split == "segments":
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)
}
elif split == "files":
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 |