--- title: "RemoteWhisperTranscriber" id: remotewhispertranscriber slug: "/remotewhispertranscriber" description: "Use `RemoteWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model." --- # RemoteWhisperTranscriber Use `RemoteWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model.
| | | | --- | --- | | **Most common position in a pipeline** | As the first component in an indexing pipeline | | **Mandatory init variables** | `api_key`: An OpenAI API key. Can be set with an environment variable `OPENAI_API_KEY`. | | **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](/reference/audio-api) | | **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/audio/whisper_remote.py |
## Overview `RemoteWhisperTranscriber` works with OpenAI-compatible clients and isn't limited to just OpenAI as a provider. For example, [Groq](https://console.groq.com/docs/speech-text) offers a drop-in replacement that can be used as well. You can set the API key in one of two ways: 1. Through the `api_key` initialization parameter, where the key is resolved using [Secret API](../../concepts/secret-management.mdx). 2. By setting it in the `OPENAI_API_KEY` environment variable, which the system will use to access the key. ```python from haystack.components.audio import RemoteWhisperTranscriber transcriber = RemoteWhisperTranscriber() ``` Additionally, the component requires the following parameters to work: - `model` specifies the Whisper model. - `api_base_url` specifies the OpenAI base URL and defaults to `"https://api.openai.com/v1"`. If you are using Whisper provider other than OpenAI set this parameter according to provider's documentation. See other optional parameters in our [API documentation](/reference/audio-api). See the [Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper [GitHub repo](https://github.com/openai/whisper) for the supported audio formats and languages. ## Usage ### On its own Here’s an example of how to use `RemoteWhisperTranscriber` to transcribe a local file: ```python import requests from haystack.components.audio import RemoteWhisperTranscriber 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 = RemoteWhisperTranscriber() 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 `RemoteWhisperTranscriber`, and finally outputs the transcription text. ```python from haystack.components.audio import RemoteWhisperTranscriber from haystack.components.fetchers import LinkContentFetcher from haystack import Pipeline pipe = Pipeline() pipe.add_component("fetcher", LinkContentFetcher()) pipe.add_component("transcriber", RemoteWhisperTranscriber()) 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](https://haystack.deepset.ai/cookbook/multilingual_rag_podcast)