bolAIndia

Human-side speech from production call recordings, cut into utterance-level chunks by a two-engine VAD (Silero + TEN) and transcribed by third-party ASR providers. Each row keeps the transcript, the provider's confidence, and full provenance back to the source recording.

Sources

One config per transcription system, so their output stays separable.

config (source_id)	provider	model	hours	rows	shards
vendor-a	vendor-a	undisclosed	420.03	480774	42
combined	all vendors	mixed	12536.81	14960941	2275
vendor-b	vendor-b	undisclosed	10.00	12244	1

Total: 12966.84 hours across 15453959 chunks.

Who transcribed what

The combined config interleaves several transcription systems within each shard, so this is the breakdown across the whole dataset. The systems differ substantially, so treat them as separate sources when training.

Each row carries the system that produced it in its provider and model columns; the labels below are withheld aliases for the same systems, in the same order of size.

vendor	hours	chunks	share of hours
vendor-a	10479.72	12420097	80.8%
vendor-b	2365.23	2892597	18.2%
vendor-c	119.16	137982	0.9%

Loading

Rows are keyed by chunk_id. A small fraction appear twice, from a window when two workers overlapped before their work was partitioned; the copies are identical, so dropping duplicates on chunk_id is enough:

seen = set()
ds = ds.filter(lambda r: not (r["chunk_id"] in seen or seen.add(r["chunk_id"])))

from datasets import load_dataset

ds = load_dataset("kapturecx/bolAIndia", "combined", split="train")
print(ds[0]["text"], ds[0]["confidence"])

Fields

field	meaning
audio	16 kHz mono chunk of the human channel
text	provider transcript; empty means nothing intelligible was heard
confidence	provider confidence for the chunk (0-1)
word_confidence_mean / word_confidence_min	aggregated per-word confidence
words	JSON per-word timings and confidences
language_code / language_confidence	detected language and its confidence
is_unintelligible	true when the provider returned no text
provider / model / source_id	which system produced the transcript
speech_ratio / vad_detail	fused and per-engine VAD speech ratios
channel	side of the dual-channel recording (human)
source_db / mongo_id / conversation_id	provenance of the call; resolving these needs access to the source database
recording_url / s3_key / bucket	blank by design — the source recording is not distributed with this dataset
chunk_start_ms / chunk_end_ms	position of the chunk inside that recording

Phone numbers are stored only as salted hashes.