def score_folder_SD(fileList, fileDict, diarcollar, write_msg, out_path):
sctk_path = util.get_fs02sctk_path()
py_val_path = sctk_path + 'scutils/dscore/validate_rttm.py'
py_score_path = sctk_path + 'scutils/dscore/score.py'
log_write_path = util.get_logs_path() + util.get_bname(out_path) + '.log'
derDict = {}
log_list = []
unsuc_files = []
for fn in fileList:
ref_rttm = fileDict['ref'][fn]
hyp_rttm = fileDict['hyp'][fn]
ref_uem = ref_rttm.replace('/RTTM/', '/UEM/').replace('.rttm', '.uem')
der, curr_log = score_file_SAD(py_val_path, py_score_path, fn,
ref_rttm, hyp_rttm, ref_uem, diarcollar)
if der != 'NaN':
derDict[fn] = der
else:
unsuc_files.append(fn)
log_list += curr_log
with open(log_write_path, 'w') as file:
file.write('\n'.join(log_list))
print('\n\nLog File for SD Task - DER evaluation',
'written to path:\n\t', log_write_path, '\n\n')
write_msg.append('\n\n')
write_msg.append('Number of Files to be Evaluated:' + str(len(fileList)) +
'\n\n')
write_msg.append('Number of Files Successfully Evaluated:' +
str(len(derDict)) + '\n\n')
if len(unsuc_files) > 0:
write_msg.append('Files that could not be evaluated:\n\t' +
' '.join(unsuc_files))
return derDict, write_msg
def parse_arguments():
sctk_path = util.get_fs02sctk_path()
def_out_path = util.get_results_path(
) + 'SID_TopN_Result_' + util.getDateTimeStrStamp() + '.txt'
desc='Wrapper File to generate Top-N Accuracy Scores for FS02 Challenge SID Task.' +\
'For more information regarding scoring input and hypothesis files, '+\
'refer below arguments description.'
ref_mp = 'egs/ref_gt/SID/FS01_SID_uttID2spkID_Dev.txt'
hyp_mp = 'egs/sys_results/SID/FS01_SID_uttID2spkID_Dev.txt'
ref_def = sctk_path + ref_mp
hyp_def = sctk_path + hyp_mp
ref_str = 'Reference (ground truth) File Path. '+\
'This file must be the SID ground truth file. '+\
'Please refer ./'+ref_mp+' file for example.'
hyp_str = 'Hypothesis (system output) File Path. '+\
'This directory must include only SAD system output files. '+\
'Please refer ./'+hyp_mp+' file for example and file format.'
out_str = 'Output (overall system score) File Path. '+\
'Default: Result file will stored in '+util.get_results_path()+' directory. '+\
'Additional log files will be stored in '+util.get_logs_path()
clr_str = 'Desired Top-N Accuracy for SID evaluation. '+\
'Default: Top-5 Accuracy.'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-ref',
'--ref',
type=str,
default=ref_def,
help=ref_str)
parser.add_argument('-hyp',
'--hyp',
type=str,
default=hyp_def,
help=hyp_str)
parser.add_argument('-out',
'--out',
type=str,
default=def_out_path,
help=out_str)
parser.add_argument('-topN', '--topN', type=int, default=5, help=clr_str)
args = parser.parse_args()
ref_path = util.processInpPath(args.ref, inpType='file', checkExists=True)
hyp_path = util.processInpPath(args.hyp, inpType='file', checkExists=True)
out_path = util.processInpPath(args.out, inpType='file')
max_TopN = validate_hyp_file(hyp_path)
topN_num = proc_topN_inp(args.topN, max_TopN)
return ref_path, hyp_path, out_path, topN_num
def score_SID(fileList, fileDict, topN_num, write_msg, out_path):
clsfd_Dict = {
n: {
'corr': [],
'incorr': []
}
for n in range(1, topN_num + 1)
}
for fn in fileList:
ref_str = fileDict['ref'][fn]
hyp_str_list = fileDict['hyp'][fn]
for topn in clsfd_Dict:
hyp_topn = hyp_str_list[:topn]
if ref_str in hyp_topn:
clsfd_Dict[topn]['corr'].append(fn)
else:
clsfd_Dict[topn]['incorr'].append(fn)
topNDict = {
n: round((100.0 * len(clsfd_Dict[n]['corr'])) / len(fileList), 3)
for n in clsfd_Dict
}
write_msg.append(
'Individual Results (per file) written to following paths:\n')
strz = '\t' + '*' * 40 + '\n'
for n in clsfd_Dict:
write_path = util.get_logs_path() + util.get_bname(
out_path) + '.Top-' + str(n)
write_msg.append(write_path)
write_list = [
strz + '\tPer File SID Top-' + str(n) + ' Accuracy Results\n' +
strz
]
write_list.append('\n\nCorrect Predictions:\n' +
' '.join(clsfd_Dict[n]['corr']))
write_list.append('\n\nIncorrect Predictions:\n' +
' '.join(clsfd_Dict[n]['incorr']))
write_list.append('\n')
util.writeList(write_list, write_path, isOverWrite=True)
write_msg.append('\n\n\n')
write_msg.append(strz + '\tTop-N Acurracy System Evaluation Results:\n' +
strz)
for n in topNDict:
write_msg.append('\tTop-' + str(n) + ' Accuracy : ' +
str(topNDict[n]) + ' %')
write_msg.append(strz + '\n')
return topNDict, write_msg
def parse_arguments():
sctk_path = util.get_fs02sctk_path()
def_out_path = util.get_results_path(
) + 'SD_DER_Result_' + util.getDateTimeStrStamp() + '.txt'
coll_inps_str = 'Allowed Inputs: 0, 0.25, 0.5, 1, 2'
desc='Wrapper File to generate DER Scores for FS02 Challenge SD (track1 and track2) Tasks.' +\
'Scoring mechanism for both tracks will be the same. For more '+\
'information regarding scoring input and hypothesis files, refer '+\
'below arguments description. Open-Source Software Credits: '+\
'This script uses dscore toolkit developed by Neville Ryant for '+\
'generating DER scores. for more info, refer: (https://github.com/nryant/dscore)'
ref_mp = 'egs/ref_gt/SD/'
hyp_mp = 'egs/sys_results/SD/'
ref_def = sctk_path + ref_mp
hyp_def = sctk_path + hyp_mp
ref_str = 'Reference (ground truth) Directory Path. '+\
'This directory must include only SD ground truth RTTM '+\
'and UEM folders. Please refer ./'+ref_mp+' directory for examples.'
hyp_str = 'Hypothesis (system output) Directory Path. '+\
'This directory must include only diarization system output RTTM files. '+\
'Please refer ./'+hyp_mp+' directory for examples and file format.'
out_str = 'Output (per file and overall system score) File Path. '+\
'Default: Result file will stored in '+util.get_results_path()+' directory. '+\
'Additional log files if generated will be stored in '+util.get_logs_path()
clr_str = 'Desired forgiveness Collar for SD evaluation. '+coll_inps_str+\
' Default collar length: 0.25 secs.'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-ref',
'--ref',
type=str,
default=ref_def,
help=ref_str)
parser.add_argument('-hyp',
'--hyp',
type=str,
default=hyp_def,
help=hyp_str)
parser.add_argument('-out',
'--out',
type=str,
default=def_out_path,
help=out_str)
parser.add_argument('-diarcollar',
'--diarcollar',
type=float,
default=0.25,
help=clr_str)
args = parser.parse_args()
ref_path = proc_sd_ref_files(util.processInpPath(args.ref))
hyp_path = util.processInpPath(args.hyp)
out_path = util.processInpPath(args.out, inpType='file')
diarcollar = proc_sd_collar(args.diarcollar)
return ref_path, hyp_path, out_path, diarcollar
def parse_arguments():
sctk_path = util.get_fs02sctk_path()
def_out_path = util.get_results_path()+'ASR_WER_Result_'+util.getDateTimeStrStamp()+'.txt'
desc='Wrapper File to generate WER Scores for FS02 Challenge ASR (track1 and track2) Task.' +\
'Scoring mechanism for both tracks is the same, but the system output '+\
'hypothesis file/folder expected from the user will be different. '+\
'(folder with json files for track-1, and a plain text file for track-2)'+\
'For more information regarding scoring input and hypothesis files, '+\
'refer below arguments description. Open-Source Software Credits: '+\
'This script uses compute-wer tool from the Kaldi Speech Recognition '+\
'Toolkit. for more info, refer: (http://kaldi-asr.org/doc/tools.html)'
ref_mp = 'egs/ref_gt/ASR/ASR_track'
hyp_mp = 'egs/sys_results/ASR/ASR_track'
ref_def = sctk_path+ref_mp
hyp_def = sctk_path+hyp_mp
ref_str = 'Reference (ground truth) Path. '+\
'(Directory Path for Track-1, and File Path for Track-2) '+\
'Directory Path for Track-1 must include only ASR ground truth files. '+\
'For ASR_track1: directory containing json format ground truth files required. '+\
'Please refer ./'+ref_mp+'1/ directory for examples. '+\
'For ASR_track2: kaldi "text" file. Refer: { https://kaldi-asr.org/doc/data_prep.html#data_prep_data }. '+\
' file contents of File Path for Track-2 must include only FS02_ASR_track2 '+\
'file-names followed by associated transcripts (like in Kaldi "text" format)'+\
'Please refer ./'+ref_mp+'2/ directory for examples.'
hyp_str = 'Hypothesis (system output) Directory/File Path. '+\
'(Directory Path for Track-1, and File Path for Track-2) '+\
'Directory Path for Track-1 must include only FS02-ASR system output files. '+\
'For ASR_track1: directory containing json format system output files required. '+\
'Please refer ./'+ref_mp+'1/ directory for examples and file format. '+\
'For ASR_track2: kaldi "text" file. Refer: { https://kaldi-asr.org/doc/data_prep.html#data_prep_data }. '+\
'file contents of File Path for Track-2 must include only FS02_ASR_track2 '+\
'file-names followed by associated transcripts (like in Kaldi "text" format)'+\
'Please refer ./'+ref_mp+'2/ directory for examples and file format.'
out_str = 'Output (overall system score) File Path. '+\
'Default: Result file will stored in '+util.get_results_path()+' directory. '+\
'Additional log files if generated will be stored in '+util.get_logs_path()
trk_str = 'Track number of the ASR Task to be evaluated. '+\
'Input Options: (as string) "1" or "2"'
kld_str = 'base path to the locally installed kaldi directory. '+\
'e.g. /home/crss/kaldi. This argument is required for'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-track', '--track', type=str, default='1', help=trk_str)
parser.add_argument('-kaldi','--kaldi', type=str, required=True, help=kld_str)
parser.add_argument('-ref', '--ref', type=str, default=ref_def, help=ref_str)
parser.add_argument('-hyp', '--hyp', type=str, default=hyp_def, help=hyp_str)
parser.add_argument('-out', '--out', type=str, default=def_out_path, help=out_str)
args = parser.parse_args()
track_num = proc_track_num(args.track)
kaldi_path = util.processInpPath(args.kaldi)
if args.ref == ref_def or args.hyp == hyp_def:
ad = str(track_num)+'/'
t2_name = 'FS01_ASR_track2_transcriptions_Dev'
print('ref or hyp paths are either not provided, or match default paths.')
print('Running Script on default example for Track-',str(track_num))
if track_num == 2:
args.ref = ref_def+ad+t2_name
args.hyp = hyp_def+ad+t2_name
else:
args.ref = ref_def+ad
args.hyp = hyp_def+ad
if track_num == 2:
ref_path = util.processInpPath(args.ref, inpType='file', checkExists=True)
hyp_path = util.processInpPath(args.hyp, inpType='file', checkExists=True)
else:
ref_path = util.processInpPath(args.ref)
hyp_path = util.processInpPath(args.hyp)
out_path = util.processInpPath(args.out, inpType='file')
return ref_path, hyp_path, out_path, track_num, kaldi_path
def parse_arguments():
sctk_path = util.get_fs02sctk_path()
def_out_path = util.get_results_path(
) + 'SAD_DCF_Result_' + util.getDateTimeStrStamp() + '.txt'
coll_inps_str = 'Allowed Inputs: 0, 0.25, 0.5, 1, 2'
desc='Wrapper File to generate DCF Scores for FS02 Challenge SAD Task.' +\
'For more information regarding scoring input and hypothesis files, '+\
'refer below arguments description. '+\
'Open-Source Software Credits: This script uses scoreFile_SAD.pl '+\
'developed by NIST. for more info, refer: (https://www.nist.gov/'+\
'itl/iad/mig/nist-open-speech-activity-detection-evaluation)'
ref_mp = 'egs/ref_gt/SAD/'
hyp_mp = 'egs/sys_results/SAD/'
ref_def = sctk_path + ref_mp
hyp_def = sctk_path + hyp_mp
ref_str = 'Reference (ground truth) Directory Path. '+\
'This directory must include only SAD ground truth files. '+\
'Please refer ./'+ref_mp+' directory for examples.'
hyp_str = 'Hypothesis (system output) Directory Path. '+\
'This directory must include only SAD system output files. '+\
'Please refer ./'+hyp_mp+' directory for examples and file format.'
out_str = 'Output (per file and overall system score) File Path. '+\
'Default: Result file will stored in '+util.get_results_path()+' directory. '+\
'Additional log files if generated will be stored in '+util.get_logs_path()
clr_str = 'Desired forgiveness Collar for SAD evaluation. '+coll_inps_str+\
' Default collar length: 0.5 secs.'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-ref',
'--ref',
type=str,
default=ref_def,
help=ref_str)
parser.add_argument('-hyp',
'--hyp',
type=str,
default=hyp_def,
help=hyp_str)
parser.add_argument('-out',
'--out',
type=str,
default=def_out_path,
help=out_str)
parser.add_argument('-sadcollar',
'--sadcollar',
type=float,
default=0.5,
help=clr_str)
args = parser.parse_args()
ref_path = util.processInpPath(args.ref)
hyp_path = util.processInpPath(args.hyp)
out_path = util.processInpPath(args.out, inpType='file')
sadcollar = proc_sad_collar(args.sadcollar)
return ref_path, hyp_path, out_path, sadcollar
