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chore: adjust docstrings in the audio package (#7246)
* adjust docstrings in the audio package * Apply suggestions from code review Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com> * Update haystack/components/audio/whisper_remote.py * black complaining for apparently no reason --------- Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>
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Massimiliano Pippi
GitHub
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@ -20,11 +20,20 @@ WhisperLocalModel = Literal["tiny", "small", "medium", "large", "large-v2"]
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@component
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class LocalWhisperTranscriber:
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"""
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Transcribes audio files using OpenAI's Whisper's model on your local machine.
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Transcribes audio files using OpenAI's Whisper model in your local machine.
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For the supported audio formats, languages, and other parameters, see the
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[Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
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[github repo](https://github.com/openai/whisper).
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[github repository](https://github.com/openai/whisper).
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Usage example:
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```python
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from haystack.components.audio import LocalWhisperTranscriber
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whisper = LocalWhisperTranscriber(model="small")
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whisper.warm_up()
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transcription = whisper.run(audio_files=["path/to/audio/file"])
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```
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"""
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def __init__(
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@ -34,10 +43,14 @@ class LocalWhisperTranscriber:
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whisper_params: Optional[Dict[str, Any]] = None,
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):
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"""
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:param model: Name of the model to use. Set it to one of the following values:
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:type model: Literal["tiny", "small", "medium", "large", "large-v2"]
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:param device: The device on which the model is loaded. If `None`, the default device is automatically
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selected.
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Creates an instance of the LocalWhisperTranscriber component.
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:param model:
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Name of the model to use. Set it to one of the following values:
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:type model:
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Literal["tiny", "small", "medium", "large", "large-v2"]
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:param device:
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The device on which the model is loaded. If `None`, the default device is automatically selected.
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"""
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whisper_import.check()
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if model not in get_args(WhisperLocalModel):
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@ -51,21 +64,29 @@ class LocalWhisperTranscriber:
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def warm_up(self) -> None:
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"""
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Loads the model.
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Loads the model in memory.
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"""
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if not self._model:
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self._model = whisper.load_model(self.model, device=self.device.to_torch())
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def to_dict(self) -> Dict[str, Any]:
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"""
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Serialize this component to a dictionary.
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Serializes the component to a dictionary.
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:returns:
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Dictionary with serialized data.
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"""
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return default_to_dict(self, model=self.model, device=self.device.to_dict(), whisper_params=self.whisper_params)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "LocalWhisperTranscriber":
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"""
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Create a `LocalWhisperTranscriber` instance from a dictionary.
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Deserializes the component from a dictionary.
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:param data:
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The dictionary to deserialize from.
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:returns:
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The deserialized component.
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"""
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serialized_device = data["init_parameters"]["device"]
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data["init_parameters"]["device"] = ComponentDevice.from_dict(serialized_device)
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@ -75,17 +96,19 @@ class LocalWhisperTranscriber:
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@component.output_types(documents=List[Document])
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def run(self, sources: List[Union[str, Path, ByteStream]], whisper_params: Optional[Dict[str, Any]] = None):
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"""
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Transcribe the audio files into a list of Documents, one for each input file.
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Transcribes the audio files into a list of Documents, one for each input file.
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For the supported audio formats, languages, and other parameters, see the
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[Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
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[github repo](https://github.com/openai/whisper).
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:param audio_files: A list of paths or binary streams to transcribe.
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:returns: A list of Documents, one for each file. The content of the document is the transcription text,
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while the document's metadata contains all the other values returned by the Whisper model, such as the
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alignment data. Another key called `audio_file` contains the path to the audio file used for the
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transcription.
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:param audio_files:
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A list of paths or binary streams to transcribe.
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:returns: A dictionary with the following keys:
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- `documents`: A list of Documents, one for each file. The content of the document is the transcription text,
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while the document's metadata contains the values returned by the Whisper model, such as the
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alignment data and the path to the audio file used for the transcription.
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"""
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if self._model is None:
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raise ComponentError("The component was not warmed up. Run 'warm_up()' before calling 'run()'.")
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@ -98,17 +121,16 @@ class LocalWhisperTranscriber:
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def transcribe(self, sources: List[Union[str, Path, ByteStream]], **kwargs) -> List[Document]:
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"""
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Transcribe the audio files into a list of Documents, one for each input file.
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Transcribes the audio files into a list of Documents, one for each input file.
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For the supported audio formats, languages, and other parameters, see the
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[Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
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[github repo](https://github.com/openai/whisper).
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:param audio_files: A list of paths or binary streams to transcribe.
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:returns: A list of Documents, one for each file. The content of the document is the transcription text,
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while the document's metadata contains all the other values returned by the Whisper model, such as the
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alignment data. Another key called `audio_file` contains the path to the audio file used for the
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transcription.
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:param audio_files:
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A list of paths or binary streams to transcribe.
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:returns:
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A list of Documents, one for each file.
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"""
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transcriptions = self._raw_transcribe(sources, **kwargs)
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documents = []
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@ -120,14 +142,16 @@ class LocalWhisperTranscriber:
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def _raw_transcribe(self, sources: List[Union[str, Path, ByteStream]], **kwargs) -> Dict[Path, Any]:
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"""
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Transcribe the given audio files. Returns the output of the model, a dictionary, for each input file.
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Transcribes the given audio files. Returns the output of the model, a dictionary, for each input file.
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For the supported audio formats, languages, and other parameters, see the
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[Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
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[github repo](https://github.com/openai/whisper).
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:param audio_files: A list of paths or binary streams to transcribe.
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:returns: A dictionary of file_path -> transcription.
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:param audio_files:
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A list of paths or binary streams to transcribe.
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:returns:
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A dictionary mapping 'file_path' to 'transcription'.
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"""
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if self._model is None:
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raise ComponentError("Model is not loaded, please run 'warm_up()' before calling 'run()'")
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@ -15,12 +15,20 @@ logger = logging.getLogger(__name__)
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@component
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class RemoteWhisperTranscriber:
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"""
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Transcribes audio files using OpenAI's Whisper using OpenAI API. Requires an API key. See the
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[OpenAI blog post](https://beta.openai.com/docs/api-reference/whisper) for more details.
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You can get one by signing up for an [OpenAI account](https://beta.openai.com/).
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Transcribes audio files using the Whisper API from OpenAI.
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The component requires an API key, see the relative
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[OpenAI documentation](https://platform.openai.com/docs/api-reference/authentication) for more details.
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For the supported audio formats, languages, and other parameters, see the
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[Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text)
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Usage example:
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```python
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from haystack.components.audio import RemoteWhisperTranscriber
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whisper = RemoteWhisperTranscriber(api_key=Secret.from_token("<your-api-key>"), model="tiny")
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transcription = whisper.run(sources=["path/to/audio/file"])
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```
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"""
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def __init__(
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@ -32,14 +40,19 @@ class RemoteWhisperTranscriber:
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**kwargs,
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):
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"""
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Transcribes a list of audio files into a list of Documents.
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Creates an instance of the RemoteWhisperTranscriber component.
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:param api_key: OpenAI API key.
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:param model: Name of the model to use. It now accepts only `whisper-1`.
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:param organization: The Organization ID, defaults to `None`. See
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:param api_key:
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OpenAI API key.
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:param model:
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Name of the model to use. It now accepts only `whisper-1`.
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:param organization:
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The Organization ID. See
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[production best practices](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization).
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:param api_base: An optional URL to use as the API base. Defaults to `None`. See OpenAI [docs](https://platform.openai.com/docs/api-reference/audio).
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:param kwargs: Other parameters to use for the model. These parameters are all sent directly to the OpenAI
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:param api_base:
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An optional URL to use as the API base. See OpenAI [docs](https://platform.openai.com/docs/api-reference/audio).
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:param kwargs:
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Other parameters to use for the model. These parameters are all sent directly to the OpenAI
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endpoint. See OpenAI [documentation](https://platform.openai.com/docs/api-reference/audio) for more details.
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Some of the supported parameters:
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- `language`: The language of the input audio.
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@ -77,9 +90,10 @@ class RemoteWhisperTranscriber:
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def to_dict(self) -> Dict[str, Any]:
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"""
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Serialize this component to a dictionary.
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This method overrides the default serializer in order to
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avoid leaking the `api_key` value passed to the constructor.
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Serializes the component to a dictionary.
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:returns:
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Dictionary with serialized data.
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"""
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return default_to_dict(
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self,
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@ -93,7 +107,12 @@ class RemoteWhisperTranscriber:
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "RemoteWhisperTranscriber":
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"""
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Deserialize this component from a dictionary.
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Deserializes the component from a dictionary.
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:param data:
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The dictionary to deserialize from.
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:returns:
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The deserialized component.
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"""
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deserialize_secrets_inplace(data["init_parameters"], keys=["api_key"])
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return default_from_dict(cls, data)
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@ -101,10 +120,13 @@ class RemoteWhisperTranscriber:
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@component.output_types(documents=List[Document])
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def run(self, sources: List[Union[str, Path, ByteStream]]):
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"""
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Transcribe the audio files into a list of Documents, one for each input file.
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Transcribes the audio files into a list of Documents, one for each input file.
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:param sources: A list of file paths or ByteStreams containing the audio files to transcribe.
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:returns: A list of Documents, one for each file. The content of the document is the transcription text.
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:param sources:
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A list of file paths or ByteStreams containing the audio files to transcribe.
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:returns: A dictionary with the following keys:
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- `documents`: A list of Documents, one for each file. The content of the document is the transcribed text.
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"""
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documents = []
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