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The SpeechToTextLoader allows to transcribe audio files with the Google Cloud Speech-to-Text API and loads the transcribed text into documents. To use it, you should have the google-cloud-speech python package installed, and a Google Cloud project with the Speech-to-Text API enabled.

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Installation & setup

First, you need to install the google-cloud-speech python package. You can find more info about it on the Speech-to-Text client libraries page. Follow the quickstart guide in the Google Cloud documentation to create a project and enable the API. 
%pip install -qU langchain-google-community[speech]

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Example

The SpeechToTextLoader must include the project_id and file_path arguments. Audio files can be specified as a Google Cloud Storage URI (gs://...) or a local file path. Only synchronous requests are supported by the loader, which has a limit of 60 seconds or 10MB per audio file. 
from langchain_google_community import SpeechToTextLoader  project_id = "<PROJECT_ID>" file_path = "gs://cloud-samples-data/speech/audio.flac" # or a local file path: file_path = "./audio.wav"  loader = SpeechToTextLoader(project_id=project_id, file_path=file_path)  docs = loader.load()
Note: Calling loader.load() blocks until the transcription is finished. The transcribed text is available in the page_content: 
docs[0].page_content 
"How old is the Brooklyn Bridge?"
The metadata contains the full JSON response with more meta information: 
docs[0].metadata 
{  'language_code': 'en-US',  'result_end_offset': datetime.timedelta(seconds=1) }

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Recognition Config

You can specify the config argument to use different speech recognition models and enable specific features. Refer to the Speech-to-Text recognizers documentation and the RecognizeRequest API reference for information on how to set a custom configuation. If you don’t specify a config, the following options will be selected automatically: 
from google.cloud.speech_v2 import (  AutoDetectDecodingConfig,  RecognitionConfig,  RecognitionFeatures, ) from langchain_google_community import SpeechToTextLoader  project_id = "<PROJECT_ID>" location = "global" recognizer_id = "<RECOGNIZER_ID>" file_path = "./audio.wav"  config = RecognitionConfig(  auto_decoding_config=AutoDetectDecodingConfig(),  language_codes=["en-US"],  model="long",  features=RecognitionFeatures(  enable_automatic_punctuation=False,  profanity_filter=True,  enable_spoken_punctuation=True,  enable_spoken_emojis=True,  ), )  loader = SpeechToTextLoader(  project_id=project_id,  location=location,  recognizer_id=recognizer_id,  file_path=file_path,  config=config, )
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