Datasets:

hudsongouge
/

Podcast-Transcripts-Deduped

language:
  - en
license: other
task_categories:
  - automatic-speech-recognition
tags:
  - podcast
  - transcription
  - youtube
  - rss
  - diarization
  - deduplicated
size_categories:
  - 10K<n<100K
configs:
  - config_name: shows
    data_files:
      - split: train
        path: shows/train.parquet
  - config_name: episodes
    data_files:
      - split: train
        path: episodes/*.parquet
  - config_name: transcripts
    data_files:
      - split: train
        path: transcripts/*.parquet
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extra_gated_fields:
  First Name: text
  Last Name: text
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    type: select
    options:
      - Projxon AI Member
      - Non-Projxon AI Member
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    type: select
    options:
      - Research
      - Model Training (pre-training)
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Podcast Transcripts (Deduped)

Phase 1 deduplicated subset of hudsongouge/Podcast-Transcripts-Raw.

Updated 2026-07-19T06-28-19Z UTC.

Dedup summary

Metric	Value
Raw input rows	102,374
Keepers	99,035
Discarded	3,339
Exact discarded	882
MinHash discarded	2457
Jaccard threshold	0.88

Two-pass Phase 1:

Exact match on canonical audio source key (YouTube ID / Omny clip / audio filename)
MinHash + LSH on normalized transcript text (Jaccard ≈ 0.85–0.92)

Configs

Config	Description
`shows`	Show/channel metadata for shows that still have keeper episodes
`episodes`	Episode metadata for keeper IDs only
`transcripts`	Full ASR transcripts for keeper IDs only

Artifacts

Under artifacts/:

keepers.json — episode IDs retained
discarded.json — episode IDs dropped
stats.json / config.json — run metadata
exact_clusters.json / minhash_clusters.json — cluster detail

Load

from datasets import load_dataset

transcripts = load_dataset("hudsongouge/Podcast-Transcripts-Deduped", "transcripts", split="train")
episodes = load_dataset("hudsongouge/Podcast-Transcripts-Deduped", "episodes", split="train")
shows = load_dataset("hudsongouge/Podcast-Transcripts-Deduped", "shows", split="train")