def test(in_audio_list=None, audio_dir=None, ldc_annotations_list=None, lab_dir=None, results_dir=None, model_list=None,
model_file=None, feature_type='PLP_0', n_coeffs_per_frame=13, acc_frames=31,
samp_period=0.01, window_length=0.025, eval_script=None):
'''
Test Speech Activity Detection for a list of files given a specific model. Ideally, many
of the input arguments like samp_period, window_length, n_coeffs_per_frame, acc_frames
should be read from the model file.
Input:
in_audio_list : list of audio files (absolute paths)
audio_dir : directory where the audio files lie
ldc_annotations_list : list with the LDC annotation files
lab_dir : directory where the .lab transcription files lie
results_dir : directory where the results will be stored
model_list : file containing a list of the class names
model_file : an HTK formatted mmf file (containing the gmm models for the different classes
feature_type : an HTK-formatted string describing the feature_type, e.g., MFCC_0
n_coeffs_per_frame : number of features per frame
acc_frames : number of frames to accumulate features over
samp_period : the frame period (in seconds)
window_length : the frame duration (in seconds)
eval_script : the java DARPA evaluation script
Output:
conf_matrix : return the confusion matrix
'''
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Run the VAD to get the results
vad_gmm.vad_gmm_list(in_audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, results_dir, samp_period, window_length)
# The annotations are in .lab format, i.e., start_time end_time label per line
ref_annotations_list = os.path.join(results_dir,'ref_test_annotations.list')
hyp_annotations_list = os.path.join(results_dir,'hyp_test_annotations.list')
lists.create_corresponding_list_assert(in_audio_list, lab_dir, ref_annotations_list,'lab')
lists.create_corresponding_list_assert(in_audio_list, results_dir, hyp_annotations_list,'rec')
# Given the results and the reference annotations, evaluate by estimating a confusion matrix
conf_matrix = vad_gmm_evaluate.vad_gmm_evaluate_frames(ref_annotations_list, hyp_annotations_list,
samp_period, model_list)
msg = "{0} \n {1}".format(model_file, conf_matrix)
logging.info(msg)
# Estimate accuracy
n_instances = np.sum(conf_matrix)
n_correct = np.sum(conf_matrix.diagonal())
msg = "Accuracy: {0} / {1} = {2}".format(n_correct, n_instances, float(n_correct) / n_instances )
logging.info(msg)
if eval_script is not None and os.path.exists(eval_script):
lists.create_corresponding_list_assert(in_audio_list, lab_dir, ref_annotations_list,'lab.frames.txt')
lists.create_corresponding_list_assert(in_audio_list, results_dir, hyp_annotations_list,'rec.frames.txt')
vad_evaluate_darpa(testing_list=in_audio_list, ref_annotations_list=ldc_annotations_list,
hyp_annotations_list=hyp_annotations_list, eval_script=eval_script,
audio_dir=audio_dir, smp_period=samp_period, window_length=window_length,
results_dir=results_dir, task_id='{0}_{1}_{2}'.format(feature_type, str(n_coeffs_per_frame),
str(acc_frames)))
return conf_matrix
def test(in_audio_list=None,
lab_dir=None,
results_dir=None,
model_list=None,
model_file=None,
feature_type='PLP_0',
n_coeffs_per_frame=13,
acc_frames=31,
samp_period=0.01,
window_length=0.02):
'''
Test Speech Activity Detection for a list of files given a specific model. Ideally, many
of the input arguments like samp_period, window_length, n_coeffs_per_frame, acc_frames
should be read from the model file.
Input:
in_audio_list : list of audio files (absolute paths)
lab_dir : directory where the .lab transcription files lie
results_dir : directory where the results will be stored
model_list : file containing a list of the class names
model_file : an HTK formatted mmf file (containing the gmm models for the different classes
feature_type : an HTK-formatted string describing the feature_type, e.g., MFCC_0
n_coeffs_per_frame : number of features per frame
acc_frames : number of frames to accumulate features over
samp_period : the frame period (in seconds)
window_length : the frame duration (in seconds)
Output:
conf_matrix : return the confusion matrix
'''
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Classify using
estimate_channel_gmm(in_audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir,
results_dir)
# The annotations are in .lab format, i.e., start_time end_time label per line
ref_annotations_list = os.path.join(results_dir,
'ref_test_annotations.list')
hyp_annotations_list = os.path.join(results_dir,
'hyp_test_annotations.list')
lists.create_corresponding_list_assert(in_audio_list, lab_dir,
ref_annotations_list, 'lab')
lists.create_corresponding_list_assert(in_audio_list, results_dir,
hyp_annotations_list, 'rec')
# Given the results and the reference annotations, evaluate by estimating a confusion matrix
conf_matrix = vad_gmm_evaluate.vad_gmm_evaluate_frames(
ref_annotations_list,
hyp_annotations_list,
samp_period,
model_list,
mode='single')
msg = "{} \n {}".format(model_file, conf_matrix)
logging.info(msg)
return (conf_matrix)
def test(in_audio_list=None, lab_dir=None, results_dir=None, model_list=None,
model_file=None, feature_type='PLP_0', n_coeffs_per_frame=13, acc_frames=31,
samp_period=0.01, window_length=0.02):
'''
Test Speech Activity Detection for a list of files given a specific model. Ideally, many
of the input arguments like samp_period, window_length, n_coeffs_per_frame, acc_frames
should be read from the model file.
Input:
in_audio_list : list of audio files (absolute paths)
lab_dir : directory where the .lab transcription files lie
results_dir : directory where the results will be stored
model_list : file containing a list of the class names
model_file : an HTK formatted mmf file (containing the gmm models for the different classes
feature_type : an HTK-formatted string describing the feature_type, e.g., MFCC_0
n_coeffs_per_frame : number of features per frame
acc_frames : number of frames to accumulate features over
samp_period : the frame period (in seconds)
window_length : the frame duration (in seconds)
Output:
conf_matrix : return the confusion matrix
'''
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Classify using
estimate_channel_gmm(in_audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, results_dir)
# The annotations are in .lab format, i.e., start_time end_time label per line
ref_annotations_list = os.path.join(results_dir,'ref_test_annotations.list')
hyp_annotations_list = os.path.join(results_dir,'hyp_test_annotations.list')
lists.create_corresponding_list_assert(in_audio_list, lab_dir, ref_annotations_list,'lab')
lists.create_corresponding_list_assert(in_audio_list, results_dir, hyp_annotations_list,'rec')
# Given the results and the reference annotations, evaluate by estimating a confusion matrix
conf_matrix = vad_gmm_evaluate.vad_gmm_evaluate_frames(ref_annotations_list, hyp_annotations_list,
samp_period, model_list, mode='single')
msg = "{} \n {}".format(model_file, conf_matrix)
logging.info(msg)
return(conf_matrix)
def test(in_audio_list=None, audio_dir=None, ldc_annotations_list=None, lab_dir=None, results_dir=None, model_list=None,
model_file=None, feature_type='PLP_0', n_coeffs_per_frame=13, acc_frames=31,
samp_period=0.01, window_length=0.025, eval_script=None):
'''
Test Speech Activity Detection for a list of files given a specific model. Ideally, many
of the input arguments like samp_period, window_length, n_coeffs_per_frame, acc_frames
should be read from the model file.
Input:
in_audio_list : list of audio files (absolute paths)
audio_dir : directory where the audio files lie
ldc_annotations_list : list with the LDC annotation files
lab_dir : directory where the .lab transcription files lie
results_dir : directory where the results will be stored
model_list : file containing a list of the class names
model_file : an HTK formatted mmf file (containing the gmm models for the different classes
feature_type : an HTK-formatted string describing the feature_type, e.g., MFCC_0
n_coeffs_per_frame : number of features per frame
acc_frames : number of frames to accumulate features over
samp_period : the frame period (in seconds)
window_length : the frame duration (in seconds)
eval_script : the java DARPA evaluation script
Output:
conf_matrix : return the confusion matrix
'''
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Run the VAD to get the results
vad_gmm.vad_gmm_list(in_audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, results_dir, samp_period, window_length)
# The annotations are in .lab format, i.e., start_time end_time label per line
ref_annotations_list = os.path.join(results_dir, 'ref_test_annotations.list')
hyp_annotations_list = os.path.join(results_dir, 'hyp_test_annotations.list')
lists.create_corresponding_list_assert(in_audio_list, lab_dir, ref_annotations_list,'lab')
lists.create_corresponding_list_assert(in_audio_list, results_dir, hyp_annotations_list,'rec')
# Given the results and the reference annotations, evaluate by estimating a confusion matrix
conf_matrix = vad_gmm_evaluate.vad_gmm_evaluate_frames(ref_annotations_list, hyp_annotations_list,
samp_period, model_list)
msg = "{} \n {}".format(model_file, conf_matrix)
logging.info(msg)
# Estimate accuracy
n_instances = np.sum(conf_matrix)
n_correct = np.sum(conf_matrix.diagonal())
msg = "Accuracy: {} / {} = {}".format(n_correct, n_instances, float(n_correct) / n_instances )
logging.info(msg)
if eval_script is not None and os.path.exists(eval_script):
lists.create_corresponding_list_assert(in_audio_list, lab_dir, ref_annotations_list,'lab.frames.txt')
lists.create_corresponding_list_assert(in_audio_list, results_dir, hyp_annotations_list,'rec.frames.txt')
vad_evaluate_darpa(testing_list=in_audio_list, ref_annotations_list=ldc_annotations_list,
hyp_annotations_list=hyp_annotations_list, eval_script=eval_script,
audio_dir=audio_dir, smp_period=samp_period, window_length=window_length,
results_dir=results_dir, task_id='{}_{}_{}'.format(feature_type, str(n_coeffs_per_frame),
str(acc_frames)))
return(conf_matrix)
