Update README.md

README.md

@@ -32,6 +32,8 @@ model-index:
 Note: This model is trained with 5 Turkish movies additional to common voice dataset.
 Although WER is high (50%) per common voice test dataset,  performance from "other sources " seems pretty good.
 
+Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogues as they tend to do better in clean sounds with less background noise.
+
 Dataset building from csv and merging code can be found on below of this Readme.
 
 Please try speech yourself on the right side to see its performance.
@@ -122,7 +124,7 @@ model.to("cuda")
 
 #Note: Not ignoring "'"  on this one 
 #Note: Not ignoring "'"  on this one 
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\#\>\<\_\’\[\]\{\}]'
+chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\#\\>\\<\\_\\’\\[\\]\\{\\}]'
 
 
 #resampler = torchaudio.transforms.Resample(48_000, 16_000)
@@ -153,11 +155,11 @@ def audio_resampler(batch, new_sample_rate = 16000):
 def remove_special_characters(batch):
 
     ##this one comes from subtitles if additional timestamps not processed  -> 00:01:01   00:01:01,33
-    batch["sentence"] = re.sub('\b\d{2}:\d{2}:\d{2}(,+\d{2})?\b', ' ', batch["sentence"])
+    batch["sentence"] = re.sub('\\b\\d{2}:\\d{2}:\\d{2}(,+\\d{2})?\\b', ' ', batch["sentence"])
     ##remove all caps in text [AÇIKLAMA] etc, do it before..
-    batch["sentence"] = re.sub('\[(\b[A-Z]+\])', '', batch["sentence"]) 
+    batch["sentence"] = re.sub('\\[(\\b[A-Z]+\\])', '', batch["sentence"]) 
     ##replace three dots (that are inside string with single)
-    batch["sentence"] = re.sub("([a-zA-Z]+)\.\.\.", r"\1.", batch["sentence"])
+    batch["sentence"] = re.sub("([a-zA-Z]+)\\.\\.\\.", r"\\1.", batch["sentence"])
     #standart ignore list
     batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
     
@@ -220,7 +222,7 @@ from datasets import Dataset
 import csv
 
 #Walk all subdirectories of base_set_path and find csv files
-base_set_path = r'C:\dataset_extracts'
+base_set_path = r'C:\\dataset_extracts'
 csv_files = []
 for path, subdirs, files in os.walk(base_set_path):
     for name in files:
@@ -230,7 +232,7 @@ for path, subdirs, files in os.walk(base_set_path):
 
 def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']):
   path = Path(csvfilename)
-  csv_delimiter="\t"  ##tab seperated, change if something else
+  csv_delimiter="\\t"  ##tab seperated, change if something else
   
   ##Pandas has bug reading non-ascii file names, make sure use open with encoding
   df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8')
@@ -248,7 +250,7 @@ from datasets import concatenate_datasets, load_dataset
 from datasets import load_from_disk
 
 # Merge datasets together (from csv files)
-dataset_file_path = ".\dataset_file"
+dataset_file_path = ".\\dataset_file"
 custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] )
 
 #save this one to disk