Download YouTube Transcripts as CSV — Spreadsheet-Ready Data

A plain text transcript is readable. A CSV transcript is analyzable. If you're doing computational text analysis, word frequency counts, timestamp-based research annotation, or corpus analysis across multiple videos, the CSV export gives you the structured data you need without manual reformatting.

INDXR.AI exports YouTube transcripts as properly-structured CSV files with segment index, start time, end time, text, and word count per segment.

What the CSV Contains

Each row in the CSV represents one transcript segment — a continuous unit of speech as detected by YouTube's captioning system or AssemblyAI's speech recognition.

Encoding: UTF-8 with BOM (Byte Order Mark). This matters for Excel compatibility — without BOM, Excel frequently misinterprets UTF-8 encoded files and displays garbled text for non-Latin characters (Arabic, Chinese, Japanese, Korean, and others). Google Sheets handles UTF-8 with or without BOM correctly.

Opening in Excel, Google Sheets, Python, and R

Excel: Double-clicking the CSV file opens it correctly in most Excel versions because of the UTF-8 BOM. If the formatting looks wrong, use Data → From Text/CSV and specify UTF-8 encoding manually.

Google Sheets: File → Import → Upload. Sheets detects the encoding automatically and imports cleanly.

Python/pandas:

import pandas as pd

df = pd.read_csv("transcript.csv", encoding="utf-8-sig")  # utf-8-sig handles BOM
print(df.head())
print(f"Total segments: {len(df)}")
print(f"Total words: {df['word_count'].sum()}")
print(f"Duration: {df['end_time'].max():.1f} seconds")

R:

library(readr)
df <- read_csv("transcript.csv", locale = locale(encoding = "UTF-8"))

Common Research Use Cases

Computational text analysis. Load the CSV into Voyant Tools, DARIAH's Topic Explorer, or a Python NLP pipeline. The structured format — one segment per row with timestamps — makes it straightforward to apply word frequency analysis, keyword-in-context, topic modeling, or sentiment analysis with temporal context.

Corpus analysis across multiple videos. Extract a playlist and download each video as a separate CSV. Combine them in Python or R to compare vocabulary, speaking pace (words per minute derived from word_count / duration), or topic distribution across a speaker's output over time.

Timestamped annotation. The start_time and end_time columns let you link analysis results back to specific moments in the video. A keyword that appears at segment index 47 starting at 284.2 seconds maps to a specific YouTube timestamp — useful for academic citation or user-facing applications that want to surface the relevant video moment.

Subtitle timing analysis. For researchers studying accessibility or subtitle quality, the segment timing data reveals patterns in how YouTube's auto-captioning system breaks speech — average segment lengths, variance, gaps between segments.

Auto-Captions vs. AI Transcription for CSV

The same quality distinction that applies to other export formats applies here. Auto-caption CSV files will have unpunctuated lowercase text and segments of 2–5 seconds. AI transcription CSV files have properly punctuated text and more natural segment boundaries.

For text analysis tasks that don't depend on punctuation (word frequency, keyword search, topic modeling), auto-caption CSV is often sufficient and costs nothing. For tasks that rely on sentence structure — readability scoring, syntactic analysis, named entity recognition — AI transcription produces meaningfully better input data.

For playlist extractions, each video exports as a separate CSV file. Download all files as a ZIP from the playlist results page.

To extract a playlist and download as CSV, see Bulk YouTube Transcript. For structured JSON with full metadata wrapper, see YouTube Transcript JSON Export. For credit costs and packages, see the pricing page. For a full overview of the extraction pipeline, see how INDXR.AI works.