FunASRTranscriber

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

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
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/funasr/src/haystack_integrations/components/audio/funasr/transcriber.py

Overview

FunASRTranscriber uses 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. Call warm_up() before running in a pipeline to load the model into memory.

Usage

On its own

python

from haystack_integrations.components.audio.funasr import FunASRTranscriber

transcriber = FunASRTranscriber()
transcriber.warm_up()

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)