chore: import upstream snapshot with attribution

docs-website/versioned_docs/version-2.30/pipeline-components/audio/external-integrations-audio.mdx

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+---
+title: "External Integrations"
+id: external-integrations-audio
+slug: "/external-integrations-audio"
+description: "External integrations that enable working with audio in Haystack by transcribing files or converting text to audio."
+---
+
+# External Integrations
+
+External integrations that enable working with audio in Haystack by transcribing files or converting text to audio.
+
+| Name | Description |
+| --- | --- |
+| [AssemblyAI](https://haystack.deepset.ai/integrations/assemblyai) | Perform speech recognition, speaker diarization and summarization. |
+| [Elevenlabs](https://haystack.deepset.ai/integrations/elevenlabs) | Convert text to speech using ElevenLabs’ API.                      |

docs-website/versioned_docs/version-2.30/pipeline-components/audio/funasrtranscriber.mdx

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+---
+title: "FunASRTranscriber"
+id: funasrtranscriber
+slug: "/funasrtranscriber"
+description: "Transcribe audio files to Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages."
+---
+
+# FunASRTranscriber
+
+Transcribe audio files to Haystack Documents using FunASR — a local, open-source speech recognition toolkit supporting 50+ languages.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **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](/reference/integrations-funasr) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/funasr/src/haystack_integrations/components/audio/funasr/transcriber.py |
+
+</div>
+
+## Overview
+
+`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.
+
+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)
+```

docs-website/versioned_docs/version-2.30/pipeline-components/audio/localwhispertranscriber.mdx

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+---
+title: "LocalWhisperTranscriber"
+id: localwhispertranscriber
+slug: "/localwhispertranscriber"
+description: "Use `LocalWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model using your local installation of Whisper."
+---
+
+# LocalWhisperTranscriber
+
+Use `LocalWhisperTranscriber` to transcribe audio files using OpenAI's Whisper model using your local installation of Whisper.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **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**                      | [Whisper](/reference/integrations-whisper)                                                                        |
+| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/whisper |
+| **Package name** | `whisper-haystack` |
+
+</div>
+
+## 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](/reference/integrations-whisper).
+
+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.
+
+The `LocalWhisperTranscriber` is part of the `whisper-haystack` integration package. To work with it, install the package along with [Whisper](https://github.com/openai/whisper) (which also pulls in torch) using the following commands:
+
+```python
+pip install whisper-haystack
+pip install -U openai-whisper
+```
+
+## Usage
+
+### On its own
+
+Here’s an example of how to use `LocalWhisperTranscriber` on its own:
+
+```python
+import requests
+from haystack_integrations.components.audio.whisper 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")
+
+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.
+
+```python
+from haystack_integrations.components.audio.whisper 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](https://haystack.deepset.ai/cookbook/multilingual_rag_podcast)

docs-website/versioned_docs/version-2.30/pipeline-components/audio/remotewhispertranscriber.mdx

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+---
+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.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **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**                      | [Whisper](/reference/integrations-whisper)                                                                         |
+| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/whisper |
+| **Package name** | `whisper-haystack` |
+
+</div>
+
+## Overview
+
+The `RemoteWhisperTranscriber` is part of the `whisper-haystack` integration package. Install it with:
+
+```python
+pip install whisper-haystack
+```
+
+`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_integrations.components.audio.whisper 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/integrations-whisper).
+
+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_integrations.components.audio.whisper 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_integrations.components.audio.whisper 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)