# Video Transcription

{% embed url="<https://www.loom.com/share/1b9137b671d04ed78a1d880a96139dc4?t=0>" %}

## What is Video Transcription?

This is our flagship product that lets you turn ON video processing capabilities for your LLMs and AI agents. This single infra lets you use videos to train your models and even process as input.&#x20;

## Capabilities&#x20;

<details>

<summary>Audio-Visual Understanding </summary>

Understands the whole video using timestammped audio-visual transcription.&#x20;

</details>

<details>

<summary>Supports 200+ Models</summary>

Easily connects with over 200+ top language models that are available over the interent.&#x20;

</details>

<details>

<summary>Import videos from anywhere</summary>

You can import videos from anywhere - YouTube, Vimeo, self-hosted or locally hosted.

</details>

<details>

<summary>Quantative Video Compression (QVC) </summary>

QVC is an internal video compression technique that we have developed to compress videos in such a way that no data is lost at the model's side while processing it, saving over 70% cost that you might have incurred otherwise.

</details>

<details>

<summary>Supports up to 1000 seconds of processing </summary>

The v0.0.21 of Transcribe API supports over 1000 seconds of video processing in single API call.

</details>

<details>

<summary>Supports Chunking</summary>

To process videos with larger lengths, Transcribe API also supports chunking to process 1 large video through multiple parallel requests. &#x20;

</details>

<details>

<summary>Asyncronous Calling</summary>

You can do parallel and asyncronous calling to Transcribe API to process multiple videos at time.

</details>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://deeptrain.gitbook.io/documentation/getting-started/transcribe-api/video-transcription.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
