LocalWhisperTranscriber
Use LocalWhisperTranscriber
to transcribe audio files using OpenAI's Whisper model using your local installation of Whisper.
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)
Updated about 1 month ago