Bases: BaseReader
Reader for AssemblyAI audio transcripts.
It uses the AssemblyAI API to transcribe audio files
and loads the transcribed text into one or more Documents,
depending on the specified format.
To use, you should have the assemblyai
python package installed, and the
environment variable ASSEMBLYAI_API_KEY
set with your API key.
Alternatively, the API key can also be passed as an argument.
Audio files can be specified via an URL or a local file path.
Source code in llama-index-integrations/readers/llama-index-readers-assemblyai/llama_index/readers/assemblyai/base.py
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100 | class AssemblyAIAudioTranscriptReader(BaseReader):
"""
Reader for AssemblyAI audio transcripts.
It uses the AssemblyAI API to transcribe audio files
and loads the transcribed text into one or more Documents,
depending on the specified format.
To use, you should have the ``assemblyai`` python package installed, and the
environment variable ``ASSEMBLYAI_API_KEY`` set with your API key.
Alternatively, the API key can also be passed as an argument.
Audio files can be specified via an URL or a local file path.
"""
def __init__(
self,
file_path: str,
*,
transcript_format: TranscriptFormat = TranscriptFormat.TEXT,
config: Optional[assemblyai.TranscriptionConfig] = None,
api_key: Optional[str] = None,
):
"""
Initializes the AssemblyAI AudioTranscriptReader.
Args:
file_path: An URL or a local file path.
transcript_format: Transcript format to use.
See class ``TranscriptFormat`` for more info.
config: Transcription options and features. If ``None`` is given,
the Transcriber's default configuration will be used.
api_key: AssemblyAI API key.
"""
if api_key is not None:
assemblyai.settings.api_key = api_key
self.file_path = file_path
self.transcript_format = transcript_format
# Instantiating the Transcriber will raise a ValueError if no API key is set.
self.transcriber = assemblyai.Transcriber(config=config)
def load_data(self) -> List[Document]:
"""Transcribes the audio file and loads the transcript into documents.
It uses the AssemblyAI API to transcribe the audio file and blocks until
the transcription is finished.
"""
transcript = self.transcriber.transcribe(self.file_path)
if transcript.error:
raise ValueError(f"Could not transcribe file: {transcript.error}")
if self.transcript_format == TranscriptFormat.TEXT:
return [Document(text=transcript.text, metadata=transcript.json_response)]
elif self.transcript_format == TranscriptFormat.SENTENCES:
sentences = transcript.get_sentences()
return [
Document(text=s.text, metadata=s.dict(exclude={"text"}))
for s in sentences
]
elif self.transcript_format == TranscriptFormat.PARAGRAPHS:
paragraphs = transcript.get_paragraphs()
return [
Document(text=p.text, metadata=p.dict(exclude={"text"}))
for p in paragraphs
]
elif self.transcript_format == TranscriptFormat.SUBTITLES_SRT:
return [Document(text=transcript.export_subtitles_srt())]
elif self.transcript_format == TranscriptFormat.SUBTITLES_VTT:
return [Document(text=transcript.export_subtitles_vtt())]
else:
raise ValueError("Unknown transcript format.")
|
load_data
Transcribes the audio file and loads the transcript into documents.
It uses the AssemblyAI API to transcribe the audio file and blocks until
the transcription is finished.
Source code in llama-index-integrations/readers/llama-index-readers-assemblyai/llama_index/readers/assemblyai/base.py
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100 | def load_data(self) -> List[Document]:
"""Transcribes the audio file and loads the transcript into documents.
It uses the AssemblyAI API to transcribe the audio file and blocks until
the transcription is finished.
"""
transcript = self.transcriber.transcribe(self.file_path)
if transcript.error:
raise ValueError(f"Could not transcribe file: {transcript.error}")
if self.transcript_format == TranscriptFormat.TEXT:
return [Document(text=transcript.text, metadata=transcript.json_response)]
elif self.transcript_format == TranscriptFormat.SENTENCES:
sentences = transcript.get_sentences()
return [
Document(text=s.text, metadata=s.dict(exclude={"text"}))
for s in sentences
]
elif self.transcript_format == TranscriptFormat.PARAGRAPHS:
paragraphs = transcript.get_paragraphs()
return [
Document(text=p.text, metadata=p.dict(exclude={"text"}))
for p in paragraphs
]
elif self.transcript_format == TranscriptFormat.SUBTITLES_SRT:
return [Document(text=transcript.export_subtitles_srt())]
elif self.transcript_format == TranscriptFormat.SUBTITLES_VTT:
return [Document(text=transcript.export_subtitles_vtt())]
else:
raise ValueError("Unknown transcript format.")
|