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68 lines
2.4 KiB
Plaintext
68 lines
2.4 KiB
Plaintext
---
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title: "FunASRTranscriber"
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id: funasrtranscriber
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slug: "/funasrtranscriber"
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description: "Transcribe audio files to Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages."
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---
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# FunASRTranscriber
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Transcribe audio files to Haystack Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| **Most common position in a pipeline** | As the first component in an indexing pipeline |
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| **Mandatory run variables** | `sources`: A list of audio file paths (`str` or `Path`) or `ByteStream` objects |
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| **Output variables** | `documents`: A list of Haystack Documents, one per source, with transcript text in `content` |
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| **API reference** | [FunASR integration](/reference/integrations-funasr) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/funasr/src/haystack_integrations/components/audio/funasr/transcriber.py |
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</div>
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## Overview
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`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.
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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`.
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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.
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## Usage
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### On its own
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```python
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from haystack_integrations.components.audio.funasr import FunASRTranscriber
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transcriber = FunASRTranscriber()
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transcriber.warm_up()
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result = transcriber.run(sources=["speech.wav"])
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print(result["documents"][0].content)
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```
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### In a pipeline
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```python
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from haystack import Pipeline
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from haystack.components.fetchers import LinkContentFetcher
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from haystack_integrations.components.audio.funasr import FunASRTranscriber
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pipe = Pipeline()
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pipe.add_component("fetcher", LinkContentFetcher())
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pipe.add_component("transcriber", FunASRTranscriber())
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pipe.connect("fetcher", "transcriber")
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result = pipe.run(
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data={
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"fetcher": {
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"urls": ["https://example.com/interview.wav"],
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},
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}
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)
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print(result["transcriber"]["documents"][0].content)
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```
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