Spaces:
Running
Running
File size: 12,264 Bytes
43cd37c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
# 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 []
#
#
####################################################################################################################### |