---
title: "FunASRTranscriber"
id: funasrtranscriber
slug: "/funasrtranscriber"
description: "Transcribe audio files to Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages."
---

# FunASRTranscriber

Transcribe audio files to Haystack Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages.

<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 audio file paths (`str` or `Path`) or `ByteStream` objects |
| **Output variables** | `documents`: A list of Haystack Documents, one per source, with transcript text in `content` |
| **API reference** | [FunASR integration](/reference/integrations-funasr) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/funasr/src/haystack_integrations/components/audio/funasr/transcriber.py |

</div>

## Overview

`FunASRTranscriber` uses [FunASR](https://github.com/modelscope/FunASR), an open-source speech recognition toolkit from Alibaba DAMO Academy, to transcribe audio files into Haystack `Document` objects. It runs entirely locally — no API key required.

The default model is `iic/SenseVoiceSmall`, a multilingual model supporting 50+ languages that is 5–10x faster than Whisper. Models are downloaded from ModelScope on first use and cached in `~/.cache/modelscope`.

The component accepts audio file paths (`str` or `Path`) as well as `ByteStream` objects. The model is loaded into memory automatically the first time the component runs.

## Usage

### On its own

```python
from haystack_integrations.components.audio.funasr import FunASRTranscriber

transcriber = FunASRTranscriber()

result = transcriber.run(sources=["speech.wav"])
print(result["documents"][0].content)
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.components.fetchers import LinkContentFetcher
from haystack_integrations.components.audio.funasr import FunASRTranscriber

pipe = Pipeline()
pipe.add_component("fetcher", LinkContentFetcher())
pipe.add_component("transcriber", FunASRTranscriber())

pipe.connect("fetcher", "transcriber")

result = pipe.run(
    data={
        "fetcher": {
            "urls": ["https://example.com/interview.wav"],
        },
    },
)
print(result["transcriber"]["documents"][0].content)
```