Split large audio file and transcribe it using the Whisper API from OpenAI

transcribe.py

import os
import sys
import openai
import os.path

from dotenv import load_dotenv
from pydub import AudioSegment

load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')

audio = AudioSegment.from_mp3(sys.argv[1])

segment_length = 25 * 60
duration = audio.duration_seconds
print('Segment length: %d seconds' % segment_length)
print('Duration: %d seconds' % duration)

segment_filename = os.path.basename(sys.argv[1])
segment_filename = os.path.splitext(segment_filename)[0]
number_of_segments = int(duration / segment_length)
segment_start = 0
segment_end = segment_length * 1000
enumerate = 1
prompt = ""

for i in range(number_of_segments):
    sound_export = audio[segment_start:segment_end]

    exported_file = '/tmp/' + segment_filename + '-' + str(enumerate) + '.mp3'
    sound_export.export(exported_file, format="mp3")
    print('Exported segment %d of %d' % (enumerate, number_of_segments))

    f = open(exported_file, "rb")
    data = openai.Audio.transcribe("whisper-1", f, prompt=prompt)
    f.close()
    print('Transcribed segment %d of %d' % (enumerate, number_of_segments))

    f = open(os.path.join('transcripts', segment_filename + '.txt'), "a")
    f.write(data.text)
    f.close()

    prompt += data.text
    segment_start += segment_length * 1000
    segment_end += segment_length * 1000
    enumerate += 1

I'm curious why you're adding the previous segment of transcription into the prompt for future segment transcriptions here? Docs from OpenAI says that prompt ignores anything over 224 tokens.

In addition, the prompt is limited to only 224 tokens. If the prompt is longer than 224 tokens, only the final 224 tokens of the prompt will be considered; all prior tokens will be silently ignored.