Datasetpublic

eko57/spc_r_segmented

eko57/spc_r_segmented Diarized and segmented speech dataset derived from i4ds/spc_r. Description Each row is a merged speech segment belonging to a single speaker. The source audio and SRT subtitles from i4ds/spc_r were processed with the following pipeline: Diarization -- pyannote/speaker-diarization-3.1 assigned speaker labels to each SRT segment based on temporal overlap. Merging -- Consecutive SRT segments from the same speaker were merged when the silence… See the full description on the dataset page: https://huggingface.co/datasets/eko57/spc_r_segmented.

sourceHugging Facecc-by-4.0updated 7mo agoView on Hugging Face

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Dataset Card

eko57/spcrsegmented

Diarized and segmented speech dataset derived from i4ds/spc_r.

Description

Each row is a merged speech segment belonging to a single speaker. The source audio and SRT subtitles from i4ds/spc_r were processed with the following pipeline:

1.Diarization -- pyannote/speaker-diarization-3.1 assigned speaker labels to each SRT segment based on temporal overlap.
2.Merging -- Consecutive SRT segments from the same speaker were merged when the silence gap between them was below a threshold (default 1.0s) and the resulting duration stayed within bounds (default 10--20s).
3.Slicing -- The merged time ranges were used to slice the original audio waveform. Each segment is encoded as FLAC.

Columns

Column	Type	Description
`id`	string	Unique identifier (`row{NNNNN}_seg{NNN}`)
`duration`	float64	Segment duration in seconds
`audio`	audio	FLAC audio for the segment
`text`	string	Merged transcript text from the SRT segments

Usage

python

from datasets import load_dataset

ds = load_dataset("eko57/spc_r_segmented")
print(ds["train"][0])