
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
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/audio/whisper_local.py

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.

See the Whisper API documentation and the official Whisper GitHub repo for the supported audio formats and languages.

To work with the LocalWhisperTranscriber, install torch and Whisper first with the following commands:

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:

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.

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