Transcript to Vocabulary

Converts podcast JSON transcripts into vocabulary TXT files for B2-C1 English learners, using keywords and AI to extract taught words.

Sby Skills Guide Bot
DevelopmentIntermediate
107/24/2026
Claude Code
#transcript-processing#vocabulary#language-learning#json-to-txt#llm-extraction

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transcript-to-vocab

Convert All Ears English podcast JSON transcripts into vocabulary learning TXT files for B2-C1 level English learners.

When to use

When the user runs /transcript-to-vocab with a JSON file name or asks to process transcript files into vocabulary files.

Instructions

When invoked, do the following:

1. Determine which files to process

  • If a file name is given (e.g. 2494.json), process only that file from cleaned_transcripts/
  • If no file is given, process all .json files in cleaned_transcripts/ that don't already have a corresponding .txt

2. For each JSON file

Read the file. It contains:

  • keywords: array of vocabulary items the course explicitly labels, each with name and definition
  • fullText: full podcast transcript

3. Build the vocabulary list using both sources

Step A — From keywords list: For each keyword, parse its definition field. The format is one of:

  • (part_of_speech) definition text "Example sentence."
  • alternate form - (part_of_speech) definition text "Example sentence."

Extract:

  • The definition text only (drop the part of speech label — but keep (idiom) if it is an idiom)
  • The example sentence from inside the quotes

Step B — From fullText (this is the LLM part): Read the fullText carefully. The hosts are English teachers. Identify every word, phrase, or expression they are actively teaching — meaning they explain its meaning, contrast it with similar words, or highlight its usage for learners. This includes words not in the keywords list.

For each such word/phrase found in fullText that is NOT already in the keywords list, add it to the vocabulary list with:

  • Definition: write a short, clear definition based strictly on how it is used and explained in the transcript. Do not bring in other meanings or contexts.
  • Example: one natural example sentence reflecting the transcript context

4. Output format

Write a .txt file with the same base name (e.g. 2494.txt) inside cleaned_transcripts/.

Format each entry as:

word (idiom): definition
 - Example sentence from definition field or written by you
 - Example sentence from fullText if the word is used naturally there (skip if the sentence is definitional like "the next phrase is X" or "X means Y")

Rules:

  • Only mark (idiom) if it is an idiom. No other part-of-speech labels.
  • Each entry is separated by a blank line
  • If a fullText sentence just names or defines the word rather than using it naturally, skip it
  • Stay faithful to the transcript context. Do not invent unrelated meanings or usages.

5. Confirm when done

After writing the file(s), report which files were created and how many vocabulary items each contains (keywords count + additionally found count).

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