eternalblue 1.3.1

Description

EternalBlue is a powerful tool for audio analysis, capable of identifying and separating different speakers in a .wav audio file. Additionally, it performs complete transcription of the spoken content, facilitating the analysis and interpretation of conversations.

Installation

pip install eternalblue

Usage

from eternalblue import EternalBlue
eternal_blue = EternalBlue('', 'portuguese')
eternalresponse = eternal_blue.diarize(audio, 2)

Here’s an example of how to use the EternalBlue class in a Flask application:

from eternalblue import EternalBlue

@app.route('/diarization', methods=['POST'])
def diarization():
    try:
        eternalresponse = {}
        if 'audio' in request.files:
            audio_file = request.files['audio']
            if audio_file.filename != '':
                save_path = os.path.join(AUDIO_PATH, audio_file.filename)
                audio_file.save(save_path)
                eternal_blue = EternalBlue('', 'portuguese')
                eternalresponse = eternal_blue.diarize(save_path, 2)
        return eternalresponse, 200
    except Exception as e:
        print(f"Error: {e}")
        return str(e), 500

For empty trash in failed jobs

from eternalblue import EternalBlue
EternalBlue.clear_cache()

Expected Return

The diarize method returns a list of dictionaries containing metadata and transcriptions, formatted as follows:

[
    {
        "metadata": [
            {
                "audioname": "tester.wav",
                "time_type": "seconds"
            }
        ]
    },
    {
        "end": 5.984999999999999,
        "speaker_id": "SPEAKER_01",
        "start": 1.299,
        "text": " Is your name Bento Ferreira, then? Are you Brazilian? Yes, sir."
    },
    {
        "end": 5.934,
        "speaker_id": "SPEAKER_00",
        "start": 5.323,
        "text": " Yes, sir."
    },
    {
        "end": 8.498,
        "speaker_id": "SPEAKER_01",
        "start": 6.851,
        "text": " military police officer, That's it ?."
    },
    {
        "end": 13.829,
        "speaker_id": "SPEAKER_00",
        "start": 9.856,
        "text": " Thank God, after 35 years in the reserve."
    },
    {
        "end": 16.613,
        "speaker_id": "SPEAKER_00",
        "start": 15.645,
        "text": " Mr. Miguel. Miguel! "
    },
    {
        "end": 19.618000000000002,
        "speaker_id": "SPEAKER_00",
        "start": 16.969,
        "text": " Any relation to  James ?"
    },

]

This JSON structure contains the following information for each segment: - metadata: General information about the audio file. - speaker_id: The identifier for the speaker. - start: The start time of the speech segment in seconds. - end: The end time of the speech segment in seconds. - text: The transcribed text of the speech segment.