---
title: "LocalWhisperTranscriber"
id: localwhispertranscriber
slug: "/localwhispertranscriber"
description: "Use `LocalWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model using your local installation of Whisper."
---

# LocalWhisperTranscriber

Use `LocalWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model using your local installation of Whisper.

<div className="key-value-table">

|                                        |                                                                                               |
| :------------------------------------- | :-------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | As the first component in an indexing pipeline                                                |
| **Mandatory run variables**            | `sources`: A list of paths or binary streams that you want to transcribe                      |
| **Output variables**                   | `documents`: A list of documents                                                              |
| **API reference**                      | [Audio](/reference/audio-api)                                                                        |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/audio/whisper_local.py |

</div>

## Overview

The component also needs to know which Whisper model to work with. Specify this in the `model` parameter when initializing the component. All transcription is completed on the executing machine, and the audio is never sent to a third-party provider.

See other optional parameters you can specify in our [API documentation](/reference/audio-api).

See the [Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper [GitHub repo](https://github.com/openai/whisper) for the supported audio formats and languages.

To work with the `LocalWhisperTranscriber`, install torch and [Whisper](https://github.com/openai/whisper) first with the following commands:

```python
pip install 'transformers[torch]'
pip install -U openai-whisper
```

## Usage

### On its own

Here’s an example of how to use `LocalWhisperTranscriber` on its own:

```python
import requests
from haystack.components.audio import LocalWhisperTranscriber

response = requests.get(
    "https://ia903102.us.archive.org/19/items/100-Best--Speeches/EK_19690725_64kb.mp3",
)
with open("kennedy_speech.mp3", "wb") as file:
    file.write(response.content)

transcriber = LocalWhisperTranscriber(model="tiny")
transcriber.warm_up()

transcription = transcriber.run(sources=["./kennedy_speech.mp3"])
print(transcription["documents"][0].content)
```

### In a pipeline

The pipeline below fetches an audio file from a specified URL and transcribes it. It first retrieves the audio file using `LinkContentFetcher`, then transcribes the audio into text with `LocalWhisperTranscriber`, and finally outputs the transcription text.

```python
from haystack.components.audio import LocalWhisperTranscriber
from haystack.components.fetchers import LinkContentFetcher
from haystack import Pipeline

pipe = Pipeline()
pipe.add_component("fetcher", LinkContentFetcher())
pipe.add_component("transcriber", LocalWhisperTranscriber(model="tiny"))

pipe.connect("fetcher", "transcriber")
result = pipe.run(
    data={
        "fetcher": {
            "urls": [
                "https://ia903102.us.archive.org/19/items/100-Best--Speeches/EK_19690725_64kb.mp3",
            ],
        },
    },
)
print(result["transcriber"]["documents"][0].content)
```

## Additional References

🧑‍🍳 Cookbook: [Multilingual RAG from a podcast with Whisper, Qdrant and Mistral](https://haystack.deepset.ai/cookbook/multilingual_rag_podcast)
