Only detecting one speaker for 99% of my audio file when it used to work properly

[ColtonBehannon]

I have been using the speaker diarization pipeline for many months now and it has worked great. My project has recently undergone many changes that were not directly related to the speaker identification portion but somehow something must have got messed up along the way and now I cannot identify what has caused the problem. When I upload my test file now it just says SPEAKER_00 for the entire file until one segment near the very end. Previously, it would properly swap back and forth between SPEAKER_00 and SPEAKER_01 several times throughout the file. Any help would be greatly appreciated.

Details:
I am using the speaker diarization pipeline from pretrained:

pipeline = Pipeline.from_pretrained(diarization_config_path)
diarization = pipeline(temp.name, min_speakers=2, max_speakers=10)

with diarization_config_path leading me to the config.yaml file (directory and files in image below)

My pyannote package versions are:

pyannote.audio==3.3.2
pyannote.core==5.0.0
pyannote.database==5.1.0
pyannote.metrics==3.2.1
pyannote.pipeline==3.0.1

Full Code:

# Import necessary libraries and modules
import os
import logging
from faster_whisper import WhisperModel
from pyannote.audio import Pipeline
from modules.transcribe_diarize import diarize_text
from modules.utils import read_write_temp_file

# Configure logging for the application
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')

# Get the directory where the current script is located
BASE_DIR = os.path.dirname(os.path.abspath(__file__))

# Define paths to the transcription and diarization model directories
large_v3_turbo_path = os.path.join(BASE_DIR, "model_files", "transcription_files", "large-v3-turbo")
transcription_model_path = large_v3_turbo_path
diarization_config_path = os.path.join(BASE_DIR, "model_files", "diarization_files", "config.yaml")

# Initialize Whisper transcription model
model = WhisperModel(model_size_or_path=transcription_model_path, device="cuda", compute_type="int8", local_files_only=True)

# Initialize diarization pipeline
pipeline = Pipeline.from_pretrained(diarization_config_path)

temp = read_write_temp_file(file)

logging.info("Transcription for diarization: Started")
# Perform transcription using the Whisper model
segments, info = model.transcribe(temp.name, beam_size=5)
# Get diarization with pyannote
diarization = pipeline(temp.name, min_speakers=2, max_speakers=10)
logging.info(f"Diarization: {diarization}")