YouTube Transcript Scraper ⭐

Extract transcripts, captions, and AI-powered transcriptions from any YouTube video. When YouTube has no captions available, this scraper automatically falls back to AI speech-to-text, so you never come back empty-handed.

---

What Makes This Different

Most YouTube transcript scrapers can only extract existing captions — if a video has no caption track, they return an error. This scraper is different.

It uses a three-tier approach:

1. YouTube captions (manual or auto-generated) — extracted via yt-dlp with full subtitle parsing
2. AI transcription — if no captions exist, the audio is downloaded and transcribed using an enterprise-grade speech-to-text engine
3. Speaker diarization — optionally identifies who said what, with per-utterance labels and timestamps

This means you can extract transcripts from any public YouTube video — including podcasts, interviews, lectures, live streams, and music videos — regardless of whether the uploader enabled captions.

---

🚀 Features

Extraction & Fallback

• Automatic mode — tries YouTube captions first, falls back to AI transcription if none found. Set it and forget it.
• Captions-only mode — strict mode that only returns existing YouTube captions, never uses AI
• AI-only mode — always transcribes via AI, even when captions are available (useful for comparing quality)

AI Transcription

• Enterprise-grade AI — powered by a state-of-the-art speech-to-text engine with industry-leading accuracy
• Speaker diarization — identifies up to 10 unique speakers with per-utterance labels and precise timestamps. Perfect for interviews, panel discussions, and podcasts
• Multi-language translation — translate transcripts into any target language (e.g. es, fr, de, ja) while preserving utterance structure

Technical

• yt-dlp + Deno — uses yt-dlp with Deno-based JS challenge solving for reliable YouTube access, bypassing the limitations of the InnerTube API
• Residential proxy support — uses Apify residential proxies to avoid geo-blocking and rate limiting
• Bulk processing — pass any number of YouTube video URLs in a single run
• No YouTube Data API key required — no quotas, no OAuth, no API key management

---

📋 What You Get

Every scraped video returns structured data with up to 18+ fields per result:

Field	Type	Description	Example
url	string	Full YouTube video URL	https://www.youtube.com/watch?v=dQw4w9WgXcQ
source	string	Source of the transcript — captions, ai_transcription, error, or none	captions
transcript	string | null	Full plain-text transcript when source is captions	"We're no strangers to love..."
transcription	object | null	Full AI transcription result object when source is ai_transcription	{ full_transcript: "...", utterances: [...] }
transcription.full_transcript	string | null	Complete concatenated transcript from AI	"Welcome to today's lecture on..."
transcription.utterances	array | null	Array of per-utterance segments with speaker labels and timestamps	[{ "start": 0.5, "end": 3.2, "text": "...", "speaker": "SPEAKER_00" }]
transcription.languages	array | null	Detected languages and confidence scores	[{ "language": "en", "confidence": 0.98 }]
transcription.translated_transcripts	object | null	Translated transcripts keyed by target language	{ "es": { "full_transcript": "...", "utterances": [...] } }
error	string | null	Error message if extraction failed	"No captions available"

Status Values

source	Meaning
captions	Transcript extracted from YouTube's caption track
ai_transcription	Transcript generated by AI speech-to-text from downloaded audio
error	Extraction failed
none	Captions-only mode with no captions available

---

❓ Frequently Asked Questions

How do I use this actor?

1. Open the actor on Apify Console
2. Paste one or more YouTube video URLs into the urlList field
3. Select your preferred transcription mode (Auto, AI Only, or Captions Only)
4. (Optional) Enable speaker diarization or translation
5. Click Run and wait for the results
6. Export the dataset as JSON, CSV, Excel, or HTML

What if a video has no captions?

In Auto mode (the default), the actor will download the video's audio track and transcribe it using AI. You'll get back a full transcript with timestamps — just as if captions were available. In Captions Only mode, it will skip videos without captions. In AI Only mode, it always uses AI transcription regardless.

Does this work with YouTube Shorts and live streams?

Yes. The actor accepts any standard YouTube URL format — youtube.com/watch?v=..., youtu.be/..., youtube.com/shorts/.... Completed live streams (VODs) are fully supported. Currently live streams are not supported.

What languages are supported?

For captions extraction, any language that YouTube provides captions for. For AI transcription, the engine supports 100+ languages with state-of-the-art accuracy for English, Spanish, French, German, Portuguese, Japanese, Korean, Arabic, Hindi, and many more.

Can I process hundreds of videos at once?

Yes. Pass an array of up to hundreds of video URLs in the urlList field. Each video is processed sequentially with proper cleanup between runs. There is no hard limit — your only constraint is the Apify platform's per-run timeout.

Do I need a YouTube API key or OAuth?

No. This actor uses yt-dlp with Deno-based JS challenge solving, which does not require any YouTube Data API key, OAuth setup, or quota management. For AI transcription, you will need an API key for the AI transcription service (set as the appropriate environment variable).

How is this different from other YouTube transcript scrapers on Apify?

Most competitors only extract existing captions — if the video has no captions, they return an error or null. This actor is unique in its AI transcription fallback when captions aren't available, plus its speaker diarization capability (identifying who said what). No other YouTube transcript actor on Apify offers diarization with per-utterance labels.

---

📥 Input

Input	Type	Required	Default	Description
urlList	array<string>	✅ Yes	—	YouTube video URLs to extract transcripts from
transcriptionMode	string	❌ No	auto	auto (captions → AI fallback), ai_only, or captions_only
diarization	boolean	❌ No	false	Identify different speakers with per-utterance labels and timestamps
translationEnabled	boolean	❌ No	false	Translate the transcript to other languages
translationLanguages	array<string>	❌ No	["es"]	Target language codes (e.g. es, fr, de). Only used when translationEnabled is true

Example Input

{
  "urlList": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "https://youtu.be/jNQXAC9IVRw"
  ],
  "transcriptionMode": "auto",
  "diarization": true,
  "translationEnabled": false
}

Example Output (Captions Source)

{
  "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "source": "captions",
  "transcript": "We're no strangers to love\nYou know the rules and so do I\nA full commitment's what I'm thinking of\nYou wouldn't get this from any other guy"
}

Example Output (AI Transcription with Diarization and Translation)

[
  {
    "url": "https://www.youtube.com/watch?v=jNQXAC9IVRw",
    "source": "ai_transcription",
    "metadata": {
      "audio_duration": 19.008,
      "number_of_distinct_channels": 1,
      "billing_time": 19.008,
      "transcription_time": 8.95
    },
    "transcription": {
      "utterances": [
        {
          "text": "All right,",
          "language": "en",
          "start": 1.297,
          "end": 1.517,
          "confidence": 0.33,
          "channel": 0,
          "words": [
            {
              "word": "All",
              "start": 1.297,
              "end": 1.4169999999999998,
              "confidence": 0.32
            },
            {
              "word": " right,",
              "start": 1.418,
              "end": 1.517,
              "confidence": 0.35
            }
          ],
          "speaker": 0
        },
        {
          "text": "so here we are in front of the elephants.",
          "language": "en",
          "start": 1.518,
          "end": 3.379,
          "confidence": 0.32,
          "channel": 0,
          "words": [
            {
              "word": " so",
              "start": 1.518,
              "end": 1.597,
              "confidence": 0.04
            },
            {
              "word": " here",
              "start": 1.617,
              "end": 1.837,
              "confidence": 0.65
            },
            {
              "word": " we",
              "start": 1.838,
              "end": 1.917,
              "confidence": 0.03
            },
            {
              "word": " are",
              "start": 1.918,
              "end": 2.077,
              "confidence": 0.63
            },
            {
              "word": " in",
              "start": 2.0780000000000003,
              "end": 2.198,
              "confidence": 0
            },
            {
              "word": " front",
              "start": 2.318,
              "end": 2.458,
              "confidence": 0.44
            },
            {
              "word": " of",
              "start": 2.459,
              "end": 2.538,
              "confidence": 0.33
            },
            {
              "word": " the",
              "start": 2.539,
              "end": 2.638,
              "confidence": 0.25
            },
            {
              "word": " elephants.",
              "start": 2.918,
              "end": 3.379,
              "confidence": 0.53
            }
          ],
          "speaker": 0
        },
        {
          "text": "Cool thing about these guys is that they have really,",
          "language": "en",
          "start": 5.2,
          "end": 8.163,
          "confidence": 0.59,
          "channel": 0,
          "words": [
            {
              "word": " Cool",
              "start": 5.2,
              "end": 5.38,
              "confidence": 0.44
            },
            {
              "word": " thing",
              "start": 5.4,
              "end": 5.6,
              "confidence": 0.58
            },
            {
              "word": " about",
              "start": 5.841,
              "end": 5.981,
              "confidence": 0.19
            },
            {
              "word": " these",
              "start": 6.021,
              "end": 6.201,
              "confidence": 0.36
            },
            {
              "word": " guys",
              "start": 6.221,
              "end": 6.601,
              "confidence": 0.78
            },
            {
              "word": " is",
              "start": 7.022,
              "end": 7.142,
              "confidence": 0.89
            },
            {
              "word": " that",
              "start": 7.143,
              "end": 7.242,
              "confidence": 0.68
            },
            {
              "word": " they",
              "start": 7.243,
              "end": 7.362,
              "confidence": 0.93
            },
            {
              "word": " have",
              "start": 7.382,
              "end": 7.522,
              "confidence": 0.74
            },
            {
              "word": " really,",
              "start": 7.922,
              "end": 8.163,
              "confidence": 0.34
            }
          ],
          "speaker": 0
        },
        {
          "text": "really,",
          "language": "en",
          "start": 9.143999999999998,
          "end": 9.424,
          "confidence": 0.78,
          "channel": 0,
          "words": [
            {
              "word": " really,",
              "start": 9.143999999999998,
              "end": 9.424,
              "confidence": 0.78
            }
          ],
          "speaker": 0
        },
        {
          "text": "really long trunks,",
          "language": "en",
          "start": 9.623999999999999,
          "end": 12.366,
          "confidence": 0.42,
          "channel": 0,
          "words": [
            {
              "word": " really",
              "start": 9.623999999999999,
              "end": 9.844000000000001,
              "confidence": 0.8
            },
            {
              "word": " long",
              "start": 10.143999999999998,
              "end": 10.445,
              "confidence": 0.09
            },
            {
              "word": " trunks,",
              "start": 12.046,
              "end": 12.366,
              "confidence": 0.38
            }
          ],
          "speaker": 0
        },
        {
          "text": "and that's that's cool.",
          "language": "en",
          "start": 12.827000000000002,
          "end": 13.707999999999998,
          "confidence": 0.47,
          "channel": 0,
          "words": [
            {
              "word": " and",
              "start": 12.827000000000002,
              "end": 12.947,
              "confidence": 0.66
            },
            {
              "word": " that's",
              "start": 12.948,
              "end": 13.227,
              "confidence": 0.4
            },
            {
              "word": " that's",
              "start": 13.286999999999999,
              "end": 13.486999999999998,
              "confidence": 0.43
            },
            {
              "word": " cool.",
              "start": 13.527000000000001,
              "end": 13.707999999999998,
              "confidence": 0.41
            }
          ],
          "speaker": 0
        },
        {
          "text": "And that's pretty much all there is to say.",
          "language": "en",
          "start": 16.97,
          "end": 18.432,
          "confidence": 0.62,
          "channel": 0,
          "words": [
            {
              "word": " And",
              "start": 16.97,
              "end": 17.11,
              "confidence": 0.78
            },
            {
              "word": " that's",
              "start": 17.13,
              "end": 17.291,
              "confidence": 0.45
            },
            {
              "word": " pretty",
              "start": 17.331,
              "end": 17.471,
              "confidence": 0.36
            },
            {
              "word": " much",
              "start": 17.511,
              "end": 17.671,
              "confidence": 0.96
            },
            {
              "word": " all",
              "start": 17.750999999999998,
              "end": 17.910999999999998,
              "confidence": 0.83
            },
            {
              "word": " there",
              "start": 17.930999999999997,
              "end": 18.051,
              "confidence": 0.04
            },
            {
              "word": " is",
              "start": 18.052,
              "end": 18.111,
              "confidence": 0.42
            },
            {
              "word": " to",
              "start": 18.112,
              "end": 18.211,
              "confidence": 0.84
            },
            {
              "word": " say.",
              "start": 18.250999999999998,
              "end": 18.432,
              "confidence": 0.93
            }
          ],
          "speaker": 0
        }
      ],
      "full_transcript": "All right, so here we are in front of the elephants. Cool thing about these guys is that they have really, really, really long trunks, and that's that's cool. And that's pretty much all there is to say.",
      "languages": [
        "en"
      ]
    },
    "diarization": {
      "success": true,
      "is_empty": false,
      "exec_time": 7,
      "results": [
        {
          "text": "All right,",
          "language": "en",
          "start": 1.297,
          "end": 1.517,
          "confidence": 0.33,
          "channel": 0,
          "words": [
            {
              "word": "All",
              "start": 1.297,
              "end": 1.4169999999999998,
              "confidence": 0.32
            },
            {
              "word": " right,",
              "start": 1.418,
              "end": 1.517,
              "confidence": 0.35
            }
          ],
          "speaker": 0
        },
        {
          "text": "so here we are in front of the elephants.",
          "language": "en",
          "start": 1.518,
          "end": 3.379,
          "confidence": 0.32,
          "channel": 0,
          "words": [
            {
              "word": " so",
              "start": 1.518,
              "end": 1.597,
              "confidence": 0.04
            },
            {
              "word": " here",
              "start": 1.617,
              "end": 1.837,
              "confidence": 0.65
            },
            {
              "word": " we",
              "start": 1.838,
              "end": 1.917,
              "confidence": 0.03
            },
            {
              "word": " are",
              "start": 1.918,
              "end": 2.077,
              "confidence": 0.63
            },
            {
              "word": " in",
              "start": 2.0780000000000003,
              "end": 2.198,
              "confidence": 0
            },
            {
              "word": " front",
              "start": 2.318,
              "end": 2.458,
              "confidence": 0.44
            },
            {
              "word": " of",
              "start": 2.459,
              "end": 2.538,
              "confidence": 0.33
            },
            {
              "word": " the",
              "start": 2.539,
              "end": 2.638,
              "confidence": 0.25
            },
            {
              "word": " elephants.",
              "start": 2.918,
              "end": 3.379,
              "confidence": 0.53
            }
          ],
          "speaker": 0
        },
        {
          "text": "Cool thing about these guys is that they have really,",
          "language": "en",
          "start": 5.2,
          "end": 8.163,
          "confidence": 0.59,
          "channel": 0,
          "words": [
            {
              "word": " Cool",
              "start": 5.2,
              "end": 5.38,
              "confidence": 0.44
            },
            {
              "word": " thing",
              "start": 5.4,
              "end": 5.6,
              "confidence": 0.58
            },
            {
              "word": " about",
              "start": 5.841,
              "end": 5.981,
              "confidence": 0.19
            },
            {
              "word": " these",
              "start": 6.021,
              "end": 6.201,
              "confidence": 0.36
            },
            {
              "word": " guys",
              "start": 6.221,
              "end": 6.601,
              "confidence": 0.78
            },
            {
              "word": " is",
              "start": 7.022,
              "end": 7.142,
              "confidence": 0.89
            },
            {
              "word": " that",
              "start": 7.143,
              "end": 7.242,
              "confidence": 0.68
            },
            {
              "word": " they",
              "start": 7.243,
              "end": 7.362,
              "confidence": 0.93
            },
            {
              "word": " have",
              "start": 7.382,
              "end": 7.522,
              "confidence": 0.74
            },
            {
              "word": " really,",
              "start": 7.922,
              "end": 8.163,
              "confidence": 0.34
            }
          ],
          "speaker": 0
        },
        {
          "text": "really,",
          "language": "en",
          "start": 9.143999999999998,
          "end": 9.424,
          "confidence": 0.78,
          "channel": 0,
          "words": [
            {
              "word": " really,",
              "start": 9.143999999999998,
              "end": 9.424,
              "confidence": 0.78
            }
          ],
          "speaker": 0
        },
        {
          "text": "really long trunks,",
          "language": "en",
          "start": 9.623999999999999,
          "end": 12.366,
          "confidence": 0.42,
          "channel": 0,
          "words": [
            {
              "word": " really",
              "start": 9.623999999999999,
              "end": 9.844000000000001,
              "confidence": 0.8
            },
            {
              "word": " long",
              "start": 10.143999999999998,
              "end": 10.445,
              "confidence": 0.09
            },
            {
              "word": " trunks,",
              "start": 12.046,
              "end": 12.366,
              "confidence": 0.38
            }
          ],
          "speaker": 0
        },
        {
          "text": "and that's that's cool.",
          "language": "en",
          "start": 12.827000000000002,
          "end": 13.707999999999998,
          "confidence": 0.47,
          "channel": 0,
          "words": [
            {
              "word": " and",
              "start": 12.827000000000002,
              "end": 12.947,
              "confidence": 0.66
            },
            {
              "word": " that's",
              "start": 12.948,
              "end": 13.227,
              "confidence": 0.4
            },
            {
              "word": " that's",
              "start": 13.286999999999999,
              "end": 13.486999999999998,
              "confidence": 0.43
            },
            {
              "word": " cool.",
              "start": 13.527000000000001,
              "end": 13.707999999999998,
              "confidence": 0.41
            }
          ],
          "speaker": 0
        },
        {
          "text": "And that's pretty much all there is to say.",
          "language": "en",
          "start": 16.97,
          "end": 18.432,
          "confidence": 0.62,
          "channel": 0,
          "words": [
            {
              "word": " And",
              "start": 16.97,
              "end": 17.11,
              "confidence": 0.78
            },
            {
              "word": " that's",
              "start": 17.13,
              "end": 17.291,
              "confidence": 0.45
            },
            {
              "word": " pretty",
              "start": 17.331,
              "end": 17.471,
              "confidence": 0.36
            },
            {
              "word": " much",
              "start": 17.511,
              "end": 17.671,
              "confidence": 0.96
            },
            {
              "word": " all",
              "start": 17.750999999999998,
              "end": 17.910999999999998,
              "confidence": 0.83
            },
            {
              "word": " there",
              "start": 17.930999999999997,
              "end": 18.051,
              "confidence": 0.04
            },
            {
              "word": " is",
              "start": 18.052,
              "end": 18.111,
              "confidence": 0.42
            },
            {
              "word": " to",
              "start": 18.112,
              "end": 18.211,
              "confidence": 0.84
            },
            {
              "word": " say.",
              "start": 18.250999999999998,
              "end": 18.432,
              "confidence": 0.93
            }
          ],
          "speaker": 0
        }
      ],
      "error": null
    },
    "translation": {
      "success": true,
      "is_empty": false,
      "results": [
        {
          "languages": [
            "es"
          ],
          "full_transcript": "Muy bien, aquí estamos frente a los elefantes. Lo genial de estos animales es que tienen trompas muy largas, y eso es genial. Y eso es prácticamente todo lo que hay que decir.",
          "utterances": [
            {
              "words": [
                {
                  "word": "Muy",
                  "start": 1.297,
                  "end": 1.4169999999999998,
                  "confidence": 0.32
                },
                {
                  "word": " bien,",
                  "start": 1.418,
                  "end": 1.517,
                  "confidence": 0.35
                }
              ],
              "text": "Muy bien,",
              "language": "es",
              "start": 1.297,
              "end": 1.517,
              "channel": 0,
              "speaker": 0,
              "confidence": 0.33499999999999996
            },
            {
              "words": [
                {
                  "word": " aquí",
                  "start": 1.518,
                  "end": 1.837,
                  "confidence": 0.34500000000000003
                },
                {
                  "word": " estamos",
                  "start": 1.838,
                  "end": 2.077,
                  "confidence": 0.33
                },
                {
                  "word": " frente",
                  "start": 2.0780000000000003,
                  "end": 2.198,
                  "confidence": 0
                },
                {
                  "word": " a",
                  "start": 2.318,
                  "end": 2.458,
                  "confidence": 0.44
                },
                {
                  "word": " los",
                  "start": 2.459,
                  "end": 2.638,
                  "confidence": 0.29000000000000004
                },
                {
                  "word": " elefantes.",
                  "start": 2.918,
                  "end": 3.379,
                  "confidence": 0.53
                }
              ],
              "text": " aquí estamos frente a los elefantes.",
              "language": "es",
              "start": 1.518,
              "end": 3.379,
              "channel": 0,
              "speaker": 0,
              "confidence": 0.3222222222222222
            },
            {
              "words": [
                {
                  "word": " Lo",
                  "start": 5.2,
                  "end": 5.38,
                  "confidence": 0.44
                },
                {
                  "word": " genial",
                  "start": 5.4,
                  "end": 5.6,
                  "confidence": 0.58
                },
                {
                  "word": " de",
                  "start": 5.841,
                  "end": 5.981,
                  "confidence": 0.19
                },
                {
                  "word": " estos",
                  "start": 6.021,
                  "end": 6.201,
                  "confidence": 0.36
                },
                {
                  "word": " animales",
                  "start": 6.221,
                  "end": 6.601,
                  "confidence": 0.78
                },
                {
                  "word": " es",
                  "start": 7.022,
                  "end": 7.362,
                  "confidence": 0.8475
                },
                {
                  "word": " que",
                  "start": 7.382,
                  "end": 7.522,
                  "confidence": 0.74
                },
                {
                  "word": " tienen",
                  "start": 7.922,
                  "end": 8.163,
                  "confidence": 0.34
                }
              ],
              "text": " Lo genial de estos animales es que tienen",
              "language": "es",
              "start": 5.2,
              "end": 8.163,
              "channel": 0,
              "speaker": 0,
              "confidence": 0.593
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              "words": [
                {
                  "word": " trompas",
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                  "confidence": 0.78
                }
              ],
              "text": " trompas",
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              "channel": 0,
              "speaker": 0,
              "confidence": 0.78
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              "words": [
                {
                  "word": " muy",
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                  "confidence": 0.8
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                {
                  "word": " largas,",
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                },
                {
                  "word": " y",
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              ],
              "text": " muy largas, y",
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              "speaker": 0,
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              "words": [
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                  "word": " eso",
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                  "word": " es",
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                },
                {
                  "word": " genial.",
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                }
              ],
              "text": " eso es genial.",
              "language": "es",
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              "end": 13.707999999999998,
              "channel": 0,
              "speaker": 0,
              "confidence": 0.475
            },
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              "words": [
                {
                  "word": " Y eso",
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                  "confidence": 0.78
                },
                {
                  "word": " es",
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                {
                  "word": " prácticamente",
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                },
                {
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                  "word": " lo",
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                  "confidence": 0.83
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                {
                  "word": " que",
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                {
                  "word": " hay",
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                },
                {
                  "word": " que",
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                  "confidence": 0.84
                },
                {
                  "word": " decir.",
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                  "confidence": 0.93
                }
              ],
              "text": " Y eso es prácticamente todo lo que hay que decir.",
              "language": "es",
              "start": 16.97,
              "end": 18.432,
              "channel": 0,
              "speaker": 0,
              "confidence": 0.6233333333333334
            }
          ],
          "error": null
        }
      ],
      "exec_time": 1.4865992790013551,
      "error": null
    }
  }
]

---

🛠️ Technical Details

How It Works

The actor uses a two-phase extraction pipeline. Phase 1 attempts to extract existing YouTube caption tracks using yt-dlp with Deno-powered JS challenge solving — this handles the vast majority of videos. If no captions are available and AI transcription is enabled, Phase 2 downloads the audio stream (opus 48kHz format 251) and sends it to an AI transcription service, polling for completion.

Error Handling

• Missing captions — gracefully falls back to AI transcription (in auto mode) or returns a none status (in captions_only mode)
• Audio download failure — if yt-dlp fails to download the audio track (e.g. the video is private, deleted, or geo-blocked), the actor returns a clear error status
• AI transcription failure — if the transcription service is unreachable, the API key is invalid, or transcription times out, the error is captured and returned
• Missing API key — when AI transcription is requested but no API key is configured for the transcription service, the actor logs a warning and continues with an error result for that video
• Temporary files — all downloaded audio files are cleaned up immediately after transcription to avoid disk bloat on long runs

Data Integrity

• No duplicate data — each video URL produces exactly one result in the dataset
• Original format preserved — captions are returned as clean plain text with SRT/VTT timestamps stripped during parsing
• Full audit trail — every result includes a source field indicating exactly how the transcript was obtained
• No silent failures — every error is captured with a human-readable message in the error field

---

💘 Comparison: YouTube Transcript Scrapers

Feature	YouTube Transcript Scraper ⭐	akash9078	scrapesmith	crawlerbros
YouTube captions extraction	✅	✅	✅	✅
AI transcription fallback (no captions)	✅ — speech-to-text engine	❌	❌	✅ — Whisper (local)
Speaker diarization	✅ — per-utterance labels	❌	❌	❌
Translation	✅ — multi-language AI translation	✅ — Mistral AI	❌	❌
Bulk processing	✅ — unlimited URLs	✅	✅ — state migration	✅
Timestamped segments	✅ — via utterances	❌	✅ — per segment	✅ — per segment
Rich video metadata	❌ (focused on transcript)	✅ — title, views, thumbnails	✅ — title, views, channel, duration	✅ — title, views, channel, duration
Proxy support	✅ — Apify residential	✅ — built-in rotation	❌ (not required)	❌ (not required)
No YouTube API key	✅ — yt-dlp + Deno	✅ — InnerTube API	✅ — session cookies	✅ — multiple fallback paths
Pricing model	Per-result	Per-result	Per-result	Per-result

---

💡 Use Cases

AI Training Data Pipeline

A machine learning engineer is building a large-scale speech-to-text training dataset. They need to collect thousands of hours of transcribed speech across multiple domains — news, podcasts, lectures, interviews, and casual conversations. Many of the most valuable videos (podcasts, interviews) don't have captions because they're too long for uploaders to manually caption, and auto-captions may be disabled.

Using this actor in auto mode, the engineer can feed in video URLs from any domain and get back clean, structured transcripts for every video — captions where they exist, AI transcription where they don't. The transcription.full_transcript field provides the complete text, while transcription.languages gives confidence scores for filtering. With speaker diarization enabled, the resulting dataset includes natural speaker turns, which is invaluable for training dialogue-aware models.

The engineer can run this actor on a weekly schedule via Apify's scheduler, feeding in new videos from a curated channel list, and the dataset grows continuously without any manual intervention.

Podcast and Interview Analysis

A media analyst needs to extract actionable insights from a library of 500+ podcast episodes. Each episode features multiple guests discussing specific topics, but only a handful have captions. Manual transcription would cost thousands of dollars and take weeks.

With diarization enabled, the actor identifies each speaker across every utterance and returns structured data showing exactly who said what and when. The analyst can filter by speaker to isolate a specific guest's comments across dozens of episodes, or search the full transcript corpus for mentions of a specific topic. The utterances array with precise start and end timestamps makes it trivial to clip specific soundbites for social media or create show notes with page-accurate references.

The result: 500 podcast episodes transcribed and searchable in a few hours, for a fraction of the cost of professional transcription services.

Multilingual Content Localization

A content strategist for a global SaaS company wants to repurpose the CEO's quarterly all-hands videos into blog posts and social content for Spanish, French, and German markets. The videos are internal — no captions at all.

By enabling translationEnabled: true and setting translationLanguages: ["es", "fr", "de"], the actor transcribes the videos and returns the full transcript in each target language. The transcription.translated_transcripts object contains separate full_transcript and utterance structures for every requested language. The strategist feeds the English transcript into their CMS as a blog draft and the translated versions into the appropriate regional channels.

No external translation service needed — the actor handles transcription and translation in one run, cutting the localization workflow from three steps to one.

Competitive Intelligence Monitoring

A product marketing manager needs to track what competitors are saying in their quarterly earnings calls, product launch videos, and conference presentations. These videos are typically long-form (45-90 minutes) and rarely have captions.

The manager sets up a scheduled Apify actor run every Monday morning with a curated list of competitor video URLs. The actor downloads each video's audio, transcribes it, and pushes the results to an Apify dataset. With speaker diarization enabled, the manager can distinguish between the CEO's prepared remarks and analyst Q&A segments.

The dataset feeds into a Slack webhook that alerts the team about specific keyword mentions ("partnership", "new feature", "pricing change"). The full transcripts are searchable in a vector database for downstream analysis. The entire pipeline runs automatically — no manual transcription, no missed videos.

Academic Research — Discourse Analysis

A linguistics PhD student is studying discourse patterns in a corpus of 200 TED Talks. They need precise, time-aligned transcripts to analyze turn-taking, filler word usage, and rhetorical structures across different speakers and topics.

TED Talks typically have captions, but the student also wants to analyze off-script remarks and audience Q&A sessions, which are often uncaptioned. The actor's auto mode handles both: captioned talks are extracted in milliseconds via yt-dlp, while uncaptioned Q&A segments trigger the AI fallback.

The transcription.utterances array provides sub-second timing precision for every phrase, enabling quantitative analysis of speaking pace, pause duration, and speaker overlap. The transcription.languages field confirms language detection for multilingual talks. The structured JSON output integrates directly with the student's Python analysis pipeline using pandas and spaCy.

Accessibility Compliance for Public Content

A university's digital accessibility coordinator needs to ensure that all 300+ publicly posted lecture videos on the department's YouTube channel have accompanying transcripts for hearing-impaired students. Many of the older videos predate YouTube's auto-captioning and have no transcript whatsoever.

Using the actor with default auto mode, the coordinator feeds the entire channel's video list into a single run. Videos with existing captions are extracted instantly. The remaining videos — the bulk of the work — are automatically transcribed via AI with no manual review needed. The source field clearly distinguishes between native captions and AI-generated transcripts.

The result: a complete transcript archive for every public lecture, exportable as JSON for the learning management system or CSV for the accessibility audit. The entire compliance project goes from weeks of manual work to a single afternoon of setup.

---

🌐 Supported URL Formats

https://www.youtube.com/watch?v=VIDEO_ID
https://youtu.be/VIDEO_ID
https://www.youtube.com/shorts/VIDEO_ID
https://www.youtube.com/embed/VIDEO_ID
https://www.youtube.com/live/VIDEO_ID

---

⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by YouTube, Google LLC, or any of their subsidiaries. All trademarks are the property of their respective owners.

This Actor accesses only publicly available transcript and caption data from youtube.com. You are solely responsible for ensuring your use complies with YouTube's Terms of Service and applicable laws.

---

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