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# Diarization_Lib.py
#########################################
# Diarization Library
# This library is used to perform diarization of audio files.
# Currently, uses FIXME for transcription.
#
####################
####################
# Function List
#
# 1. speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0)
#
####################
# Import necessary libraries
import logging
from pathlib import Path
from typing import Dict, List, Any
#
# Import Local Libraries
from App_Function_Libraries.Audio.Audio_Transcription_Lib import speech_to_text
#
# Import 3rd Party Libraries
from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarization
import yaml
#
#######################################################################################################################
# Function Definitions
#
def load_pipeline_from_pretrained(path_to_config: str | Path) -> SpeakerDiarization:
path_to_config = Path(path_to_config).resolve()
logging.debug(f"Loading pyannote pipeline from {path_to_config}...")
if not path_to_config.exists():
raise FileNotFoundError(f"Config file not found: {path_to_config}")
# Load the YAML configuration
with open(path_to_config, 'r') as config_file:
config = yaml.safe_load(config_file)
# Debug: print the entire config
logging.debug(f"Loaded config: {config}")
# Create the SpeakerDiarization pipeline
try:
pipeline = SpeakerDiarization(
segmentation=config['pipeline']['params']['segmentation'],
embedding=config['pipeline']['params']['embedding'],
clustering=config['pipeline']['params']['clustering'],
)
except KeyError as e:
logging.error(f"Error accessing config key: {e}")
raise
# Set other parameters
try:
pipeline_params = {
"segmentation": {},
"clustering": {},
}
if 'params' in config and 'segmentation' in config['params']:
if 'min_duration_off' in config['params']['segmentation']:
pipeline_params["segmentation"]["min_duration_off"] = config['params']['segmentation']['min_duration_off']
if 'params' in config and 'clustering' in config['params']:
if 'method' in config['params']['clustering']:
pipeline_params["clustering"]["method"] = config['params']['clustering']['method']
if 'min_cluster_size' in config['params']['clustering']:
pipeline_params["clustering"]["min_cluster_size"] = config['params']['clustering']['min_cluster_size']
if 'threshold' in config['params']['clustering']:
pipeline_params["clustering"]["threshold"] = config['params']['clustering']['threshold']
if 'pipeline' in config and 'params' in config['pipeline']:
if 'embedding_batch_size' in config['pipeline']['params']:
pipeline_params["embedding_batch_size"] = config['pipeline']['params']['embedding_batch_size']
if 'embedding_exclude_overlap' in config['pipeline']['params']:
pipeline_params["embedding_exclude_overlap"] = config['pipeline']['params']['embedding_exclude_overlap']
if 'segmentation_batch_size' in config['pipeline']['params']:
pipeline_params["segmentation_batch_size"] = config['pipeline']['params']['segmentation_batch_size']
logging.debug(f"Pipeline params: {pipeline_params}")
pipeline.instantiate(pipeline_params)
except KeyError as e:
logging.error(f"Error accessing config key: {e}")
raise
except Exception as e:
logging.error(f"Error instantiating pipeline: {e}")
raise
return pipeline
def audio_diarization(audio_file_path: str) -> list:
logging.info('audio-diarization: Loading pyannote pipeline')
base_dir = Path(__file__).parent.resolve()
config_path = base_dir / 'models' / 'pyannote_diarization_config.yaml'
logging.info(f"audio-diarization: Loading pipeline from {config_path}")
try:
pipeline = load_pipeline_from_pretrained(config_path)
except Exception as e:
logging.error(f"Failed to load pipeline: {str(e)}")
raise
logging.info(f"audio-diarization: Audio file path: {audio_file_path}")
try:
logging.info('audio-diarization: Starting diarization...')
diarization_result = pipeline(audio_file_path)
segments = []
for turn, _, speaker in diarization_result.itertracks(yield_label=True):
segment = {
"start": turn.start,
"end": turn.end,
"speaker": speaker
}
logging.debug(f"Segment: {segment}")
segments.append(segment)
logging.info("audio-diarization: Diarization completed with pyannote")
return segments
except Exception as e:
logging.error(f"audio-diarization: Error performing diarization: {str(e)}")
raise RuntimeError("audio-diarization: Error performing diarization") from e
# Old
# def audio_diarization(audio_file_path):
# logging.info('audio-diarization: Loading pyannote pipeline')
#
# #config file loading
# current_dir = os.path.dirname(os.path.abspath(__file__))
# # Construct the path to the config file
# config_path = os.path.join(current_dir, 'Config_Files', 'config.txt')
# # Read the config file
# config = configparser.ConfigParser()
# config.read(config_path)
# processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
#
# base_dir = Path(__file__).parent.resolve()
# config_path = base_dir / 'models' / 'config.yaml'
# pipeline = load_pipeline_from_pretrained(config_path)
#
# time_start = time.time()
# if audio_file_path is None:
# raise ValueError("audio-diarization: No audio file provided")
# logging.info("audio-diarization: Audio file path: %s", audio_file_path)
#
# try:
# _, file_ending = os.path.splitext(audio_file_path)
# out_file = audio_file_path.replace(file_ending, ".diarization.json")
# prettified_out_file = audio_file_path.replace(file_ending, ".diarization_pretty.json")
# if os.path.exists(out_file):
# logging.info("audio-diarization: Diarization file already exists: %s", out_file)
# with open(out_file) as f:
# global diarization_result
# diarization_result = json.load(f)
# return diarization_result
#
# logging.info('audio-diarization: Starting diarization...')
# diarization_result = pipeline(audio_file_path)
#
# segments = []
# for turn, _, speaker in diarization_result.itertracks(yield_label=True):
# chunk = {
# "Time_Start": turn.start,
# "Time_End": turn.end,
# "Speaker": speaker
# }
# logging.debug("Segment: %s", chunk)
# segments.append(chunk)
# logging.info("audio-diarization: Diarization completed with pyannote")
#
# output_data = {'segments': segments}
#
# logging.info("audio-diarization: Saving prettified JSON to %s", prettified_out_file)
# with open(prettified_out_file, 'w') as f:
# json.dump(output_data, f, indent=2)
#
# logging.info("audio-diarization: Saving JSON to %s", out_file)
# with open(out_file, 'w') as f:
# json.dump(output_data, f)
#
# except Exception as e:
# logging.error("audio-diarization: Error performing diarization: %s", str(e))
# raise RuntimeError("audio-diarization: Error performing diarization")
# return segments
def combine_transcription_and_diarization(audio_file_path: str) -> List[Dict[str, Any]]:
logging.info('combine-transcription-and-diarization: Starting transcription and diarization...')
try:
logging.info('Performing speech-to-text...')
transcription_result = speech_to_text(audio_file_path)
logging.info(f"Transcription result type: {type(transcription_result)}")
logging.info(f"Transcription result: {transcription_result[:3] if isinstance(transcription_result, list) and len(transcription_result) > 3 else transcription_result}")
logging.info('Performing audio diarization...')
diarization_result = audio_diarization(audio_file_path)
logging.info(f"Diarization result type: {type(diarization_result)}")
logging.info(f"Diarization result sample: {diarization_result[:3] if isinstance(diarization_result, list) and len(diarization_result) > 3 else diarization_result}")
if not transcription_result:
logging.error("Empty result from transcription")
return []
if not diarization_result:
logging.error("Empty result from diarization")
return []
# Handle the case where transcription_result is a dict with a 'segments' key
if isinstance(transcription_result, dict) and 'segments' in transcription_result:
transcription_segments = transcription_result['segments']
elif isinstance(transcription_result, list):
transcription_segments = transcription_result
else:
logging.error(f"Unexpected transcription result format: {type(transcription_result)}")
return []
logging.info(f"Number of transcription segments: {len(transcription_segments)}")
logging.info(f"Transcription segments sample: {transcription_segments[:3] if len(transcription_segments) > 3 else transcription_segments}")
if not isinstance(diarization_result, list):
logging.error(f"Unexpected diarization result format: {type(diarization_result)}")
return []
combined_result = []
for transcription_segment in transcription_segments:
if not isinstance(transcription_segment, dict):
logging.warning(f"Unexpected transcription segment format: {transcription_segment}")
continue
for diarization_segment in diarization_result:
if not isinstance(diarization_segment, dict):
logging.warning(f"Unexpected diarization segment format: {diarization_segment}")
continue
try:
trans_start = transcription_segment.get('Time_Start', 0)
trans_end = transcription_segment.get('Time_End', 0)
diar_start = diarization_segment.get('start', 0)
diar_end = diarization_segment.get('end', 0)
if trans_start >= diar_start and trans_end <= diar_end:
combined_segment = {
"Time_Start": trans_start,
"Time_End": trans_end,
"Speaker": diarization_segment.get('speaker', 'Unknown'),
"Text": transcription_segment.get('Text', '')
}
combined_result.append(combined_segment)
break
except Exception as e:
logging.error(f"Error processing segment: {str(e)}")
logging.error(f"Transcription segment: {transcription_segment}")
logging.error(f"Diarization segment: {diarization_segment}")
continue
logging.info(f"Combined result length: {len(combined_result)}")
logging.info(f"Combined result sample: {combined_result[:3] if len(combined_result) > 3 else combined_result}")
return combined_result
except Exception as e:
logging.error(f"Error in combine_transcription_and_diarization: {str(e)}", exc_info=True)
return []
#
#
####################################################################################################################### |