fix bug in writing output txt file

main.py

@@ -38,6 +38,6 @@ for filename in os.listdir(audio_dir):
         filename = open(filepath, "w")
 
         ### transcribe ###
-        transcriptor = Transcriptor(audiofile, filepath, language, modelSize, access_token, voicefolder, quantization)
+        transcriptor = Transcriptor(audiofile, output_dir, language, modelSize, access_token, voicefolder, quantization)
         res = transcriptor.faster_whisper()
         

main2.py

@@ -51,7 +51,7 @@ if st.button("Transcribe"):
             #filename = open(filepath, "w")
 
             ### transcribe ###
-            transcriptor = Transcriptor(path, filepath, language, modelSize, access_token, voicefolder, quantization)
+            transcriptor = Transcriptor(path, outputfolder, language, modelSize, access_token, voicefolder, quantization)
             res = transcriptor.faster_whisper()
             print(f"Content has been written to {filepath}")
             st.success(f"Transcribe successful!")

main3.py

@@ -47,7 +47,7 @@ if st.button("Transcribe"):
             #filename = open(filepath, "w")
 
             ### transcribe ###
-            transcriptor = Transcriptor(path, filepath, language, modelSize, access_token, voicefolder, quantization)
+            transcriptor = Transcriptor(path, outputfolder2, filepath=filepath, language, modelSize, access_token, voicefolder, quantization)
             res = transcriptor.faster_whisper()
             print(f"Content has been written to {filepath}")
             st.success(f"Transcribe successful!")

output/Mie ayam rindu malam.MP3.txt

@@ -0,0 +1,18 @@
+DWI (0.3 : 0.5) :  Terima kasih. 
+DWI (5.7 : 8.0) :  Assalamualaikum 
+DWI (9.1 : 16.5) :  siang pak oke siang maaf pak lagi mengganggu sebentar saya dari papan grup 
+DWI (17.6 : 37.7) :  produknya mie, udang keju, sumai udang. Maaf pak kalau boleh tahu kebutuhannya setiap harinya berapa kilo kira-kira pak. 
+unknown (35.0 : 35.5) :  Terima kasih. 
+DWI (39.5 : 42.0) :  Ya, ga, looking back. 
+DWI (60.3 : 95.0) :  Ini saya bawa, ini saya bawa sampel sih pak, mie-nya Mapan Group ini harganya relatif murah sih pak, Rp18,5 itu yang JS, ekonomi JS pak. Pack-packannya ada isi 10, isi suntur 100 gramnya. Kalau bapak biasanya pakai yang 80 gram, muka yang 100 gram pak? 
+DWI (95.9 : 104.8) :  Dikasih tester aja dulu ya pak ya Saya tinggalin nomor telepon saya pak ya 
+DWI (105.8 : 109.0) :  Oke Pak, 0821. 
+DWI (109.7 : 112.9) :  409 
+DWI (113.9 : 115.3) :  208 
+DWI (116.0 : 117.1) :  3, 4, Pak 
+DWI (117.9 : 118.6) :  Uhhh. 
+DWI (119.8 : 127.5) :  Iya, jangan makan siang ya Pak. 
+DWI (130.2 : 131.1) :  Siang bu! 
+DWI (133.4 : 135.3) :  Maaf ya bu, ya nggak ganggu bu. 
+DWI (138.0 : 139.8) :  rame bu ya kayak gini nih ya 
+DWI (141.9 : 146.8) :  Makasih ya bu Assalamualaikum 

speechlib/core_analysis.py

@@ -15,7 +15,7 @@ import subprocess
 
 # by default use google speech-to-text API
 # if False, then use whisper finetuned version for sinhala
-def core_analysis(file_name, voices_folder, log_folder, language, modelSize, ACCESS_TOKEN, model_type, quantization=False, custom_model_path=None, hf_model_id=None, aai_api_key=None):
+def core_analysis(file_name, voices_folder, log_folder, logfile, language, modelSize, ACCESS_TOKEN, model_type, quantization=False, custom_model_path=None, hf_model_id=None, aai_api_key=None):
 
     # <-------------------PreProcessing file-------------------------->
     # convert compressed wav
@@ -138,7 +138,7 @@ def core_analysis(file_name, voices_folder, log_folder, language, modelSize, ACC
                         common_segments.append([start, end, segment[2], speaker])
 
     # writing log file
-    write_log_file(common_segments, log_folder, file_name, language)  
+    write_log_file(common_segments, log_folder, logfile, file_name, language)  
 
     ## TODO cleaning segments and temp folder
 

speechlib/speechlib.py

@@ -5,7 +5,7 @@ from .convert_to_wav import (convert_to_wav)
 
 class Transcriptor:
 
-    def __init__(self, file, log_folder, language, modelSize, ACCESS_TOKEN, voices_folder=None, quantization=False):
+    def __init__(self, file, log_folder, logfile, language, modelSize, ACCESS_TOKEN, voices_folder=None, quantization=False):
         '''
         transcribe a wav file 
         
@@ -234,28 +234,29 @@ class Transcriptor:
         self.voices_folder = voices_folder
         self.language = language
         self.log_folder = log_folder
+        self.logfile = logfile
         self.modelSize = modelSize
         self.quantization = quantization
         self.ACCESS_TOKEN = ACCESS_TOKEN
 
     def whisper(self):
-        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.language, self.modelSize, self.ACCESS_TOKEN, "whisper", self.quantization)
+        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.logfile, self.language, self.modelSize, self.ACCESS_TOKEN, "whisper", self.quantization)
         return res
     
     def faster_whisper(self):
-        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.language, self.modelSize, self.ACCESS_TOKEN, "faster-whisper", self.quantization)
+        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.logfile, self.language, self.modelSize, self.ACCESS_TOKEN, "faster-whisper", self.quantization)
         return res
 
     def custom_whisper(self, custom_model_path):
-        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.language, self.modelSize, self.ACCESS_TOKEN, "custom", self.quantization, custom_model_path)
+        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.logfile, self.language, self.modelSize, self.ACCESS_TOKEN, "custom", self.quantization, custom_model_path)
         return res
     
     def huggingface_model(self, hf_model_id):
-        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.language, self.modelSize, self.ACCESS_TOKEN, "huggingface", self.quantization, None, hf_model_id)
+        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.logfile, self.language, self.modelSize, self.ACCESS_TOKEN, "huggingface", self.quantization, None, hf_model_id)
         return res
     
     def assemby_ai_model(self, aai_api_key):
-        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.language, self.modelSize, self.ACCESS_TOKEN, "assemblyAI", self.quantization, None, None, aai_api_key)
+        res = core_analysis(self.file, self.voices_folder, self.log_folder, self.logfile, self.language, self.modelSize, self.ACCESS_TOKEN, "assemblyAI", self.quantization, None, None, aai_api_key)
         return res
 
 class PreProcessor:

speechlib/write_log_file.py

@@ -1,7 +1,7 @@
 import os
 from datetime import datetime
 
-def write_log_file(common_segments, log_folder, file_name, language):
+def write_log_file(common_segments, log_folder, logfile, file_name, language):
 
     if not os.path.exists(log_folder):
         os.makedirs(log_folder)
@@ -12,8 +12,8 @@ def write_log_file(common_segments, log_folder, file_name, language):
 
     file_name = os.path.splitext(os.path.basename(file_name))[0]
 
-    #log_file = log_folder + "/" + file_name + "_" + current_time + "_" + language + ".txt"
-    log_file = log_folder
+    log_file = logfile
+    # log_file = log_folder
     
     lf=open(log_file,"wb")