nlp_transcribe

dbm_lib/dbm_features/raw_features/nlp/transcribe.py

@@ -0,0 +1,82 @@
+"""
+file_name: transcribe
+project_name: DBM
+created: 2020-10-11
+"""
+
+import pandas as pd
+import numpy as np
+import librosa
+import glob
+from os.path import join
+import logging
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+from dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+formant_dir = 'nlp/transcribe'
+csv_ext = '_transcribe.csv'
+error_txt = 'error: length less than 0.1'
+
+def calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path):
+    """
+    Preparing Formant freq matrix
+    Args:
+        audio_file: (.wav) parsed audio file; fl_name: input file name
+        out_loc: (str) Output directory; r_config: raw variable config
+    """
+    
+    text = n_util.process_deepspeech(audio_file, deep_path)
+    df_formant = pd.DataFrame([text], columns=[r_config.nlp_transcribe])
+    
+    df_formant.replace('', np.nan, regex=True,inplace=True)
+    df_formant[r_config.err_reason] = 'Pass'# will replace with threshold in future release
+    df_formant['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
+    
+def empty_transcribe(video_uri, out_loc, fl_name, r_config):
+    
+    """
+    Preparing empty formant frequency matrix if something fails
+    """
+    cols = [r_config.nlp_transcribe, r_config.err_reason]
+    out_val = [[np.nan, error_txt]]
+    df_fm = pd.DataFrame(out_val, columns = cols)
+    df_fm['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
+
+def run_transcribe(video_uri, out_dir, r_config, deep_path):
+    
+    """
+    Processing all patient's for fetching Formant freq
+    ---------------
+    ---------------
+    Args:
+        video_uri: video path; r_config: raw variable config object
+        out_dir: (str) Output directory for processed output; deep_path: deepspeech build path
+    """
+    try:
+        
+        input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
+        aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
+        if len(aud_filter)>0:
+
+            audio_file = aud_filter[0]
+            aud_dur = librosa.get_duration(filename=audio_file)
+
+            if float(aud_dur) < 0.1:
+                logger.info('Output file {} size is less than 0.1 sec'.format(audio_file))
+
+                empty_transcribe(video_uri, out_loc, fl_name, r_config)
+                return
+
+            calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path)
+    except Exception as e:
+        logger.error('Failed to process audio file')