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What is TranscriptFetch?

TranscriptFetch converts videos from various sources, including YouTube, TikTok, and Instagram, as well as podcasts from platforms like Spotify and Apple, into neatly organized transcripts that can be easily accessed and referenced by your application, RAG pipeline, or AI agent. The service offers outputs in both plain text and JSON formats, complete with timestamps for each segment. Importantly, even videos that do not have a caption track are automatically transcribed, guaranteeing results no matter the captioning status. Moreover, its integration with YouTube allows users to retrieve channels, playlists, and perform keyword searches, generating a comprehensive list of relevant videos. Furthermore, the batch endpoint feature permits the retrieval of up to 50 transcripts in one go, significantly improving overall efficiency for users. This combination of features makes TranscriptFetch a versatile tool for anyone in need of transcriptions.

What is Spoken?

Spoken is a cutting-edge API that transforms any publicly accessible podcast into a well-structured Markdown transcript, featuring the actual names of the speakers rather than generic identifiers such as "Speaker 1." By making a single API call, users can receive named and timestamped text that seamlessly integrates with LLMs, RAG pipelines, summarizers, and search functionalities. This eliminates the need for users to manage speech-to-text conversion and speaker recognition themselves, as Spoken delivers ready-made transcripts of published podcasts while also accurately attributing speaker identities, typically at a cost that is significantly lower—5 to 10 times less—than traditional methods for these shows. Users can enhance their search capabilities by entering specific text or by pasting a Spotify or YouTube URL, which greatly improves overall accessibility. Moreover, the service operates on a pay-per-use model, eliminating the need for subscriptions; users aren’t charged for failed attempts, and any repeated fetches are offered at no additional cost. Designed to be agent-native, the API includes an Agent Skill along with helpful resources such as agents.md, llms.txt, and an OpenAPI specification to streamline integration. To assist users in getting started, a complimentary demo key is provided, and paid credits are available for purchase starting at just $15, making it an appealing choice for those seeking to leverage podcast transcripts efficiently. With its intuitive features and affordable pricing structure, Spoken is revolutionizing access to podcast content while ensuring users can maximize their experience. Ultimately, Spoken represents a significant advancement in the way podcast transcripts are generated and utilized.

Media

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Media

No images available

Integrations Supported

Instagram
TikTok
YouTube

Integrations Supported

Instagram
TikTok
YouTube

API Availability

Has API

API Availability

Has API

Pricing Information

$5/month
Free Version
Free Trial Offered?

Pricing Information

$15
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TranscriptFetch

Date Founded

2026

Company Website

transcriptfetch.com

Company Facts

Organization Name

Spoken

Date Founded

2025

Company Location

Netherlands

Company Website

spoken.md

Categories and Features

Transcription

AI / Machine Learning
Annotations
Audio/Video File Upload
Automatic Transcription
Collaboration Tools
File Sharing
For Manual Transcription
Full Text Search
Multi-Language Support
Natural Language Processing (NLP)
Playback Controls
Speech Recognition
Subtitles
Text Editor
Timecoding

Categories and Features

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