langchain.document_loaders.parsers.audio.OpenAIWhisperParserLocal¶
class langchain.document_loaders.parsers.audio.OpenAIWhisperParserLocal(device: str = '0', lang_model: Optional[str] = None, forced_decoder_ids: Optional[Tuple[Dict]] = None)[source]¶
Transcribe and parse audio files with OpenAI Whisper model.
Audio transcription with OpenAI Whisper model locally from transformers.
Parameters:
device - device to use
NOTE: By default uses the gpu if available,
if you want to use cpu, please set device = “cpu”
lang_model - whisper model to use, for example “openai/whisper-medium”
forced_decoder_ids - id states for decoder in multilanguage model,
usage example:
from transformers import WhisperProcessor
processor = WhisperProcessor.from_pretrained(“openai/whisper-medium”)
forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language=”french”,
task=”transcribe”)
forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language=”french”,
task=”translate”)
Initialize the parser.
Parameters
device – device to use.
lang_model – whisper model to use, for example “openai/whisper-medium”.
Defaults to None.
forced_decoder_ids – id states for decoder in a multilanguage model.
Defaults to None.
Methods
__init__([device, lang_model, ...])
Initialize the parser.
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
__init__(device: str = '0', lang_model: Optional[str] = None, forced_decoder_ids: Optional[Tuple[Dict]] = None)[source]¶
Initialize the parser.
Parameters
device – device to use.