updates to part 3 script and adding part 2 loading

imda-dataset.py

@@ -1,100 +1,54 @@
 import os
+import glob
 import datasets
-# import pandas as pd
+import pandas as pd
 from sklearn.model_selection import train_test_split
-import textgrids
-import io
-import soundfile as sf
-from urllib.request import urlopen
-import re
-import json
-import tempfile
-
-def cleanup_string(line):
-
-    words_to_remove = ['(ppo)','(ppc)', '(ppb)', '(ppl)', '<s/>','<c/>','<q/>', '<fil/>', '<sta/>', '<nps/>', '<spk/>', '<non/>', '<unk>', '<s>', '<z>', '<nen>']
-
-    formatted_line = re.sub(r'\s+', ' ', line).strip().lower()
-
-    #detect all word that matches words in the words_to_remove list
-    for word in words_to_remove:
-        if re.search(word,formatted_line):
-            # formatted_line = re.sub(word,'', formatted_line)
-            formatted_line = formatted_line.replace(word,'')
-            formatted_line = re.sub(r'\s+', ' ', formatted_line).strip().lower()
-            # print("*** removed words: " + formatted_line)
-
-    #detect '\[(.*?)\].' e.g. 'Okay [ah], why did I gamble?'
-    #remove [ ] and keep text within
-    if re.search('\[(.*?)\]', formatted_line):
-        formatted_line = re.sub('\[(.*?)\]', r'\1', formatted_line).strip()
-        #print("***: " + formatted_line)
-
-    #detect '\((.*?)\).' e.g. 'Okay (um), why did I gamble?'
-    #remove ( ) and keep text within
-    if re.search('\((.*?)\)', formatted_line):
-        formatted_line = re.sub('\((.*?)\)', r'\1', formatted_line).strip()
-        # print("***: " + formatted_line)
-
-    #detect '\'(.*?)\'' e.g. 'not 'hot' per se'
-    #remove ' ' and keep text within
-    if re.search('\'(.*?)\'', formatted_line):
-        formatted_line = re.sub('\'(.*?)\'', r'\1', formatted_line).strip()
-        #print("***: " + formatted_line)
-
-    #remove punctation '''!()-[]{};:'"\, <>./?@#$%^&*_~'''
-    punctuation = '''!–;"\,./?@#$%^&*~''' 
-    punctuation_list = str.maketrans("","",punctuation)
-    formatted_line = re.sub(r'-', ' ', formatted_line)
-    formatted_line = re.sub(r'_', ' ', formatted_line)
-    formatted_line = formatted_line.translate(punctuation_list)
-    formatted_line = re.sub(r'\s+', ' ', formatted_line).strip().lower()
-    #print("***: " + formatted_line)
-
-    return formatted_line
-
-
 
 _DESCRIPTION = """\
-The National Speech Corpus (NSC) is the first large-scale Singapore English corpus 
-spearheaded by the Info-communications and Media Development Authority (IMDA) of Singapore.
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
 """
 
 _CITATION = """\
 """
 _CHANNEL_CONFIGS = sorted([
-    "Audio Same CloseMic", "Audio Separate IVR", "Audio Separate StandingMic"
+    "CHANNEL0", "CHANNEL1", "CHANNEL2"
 ])
 
-_HOMEPAGE = "https://www.imda.gov.sg/how-we-can-help/national-speech-corpus"
+_GENDER_CONFIGS = sorted(["F", "M"])
 
-_LICENSE = ""
+_RACE_CONFIGS = sorted(["CHINESE", "MALAY", "INDIAN", "OTHERS"])
 
-_PATH_TO_DATA = './IMDA - National Speech Corpus/PART3'
-# _PATH_TO_DATA = './PART1/DATA'
+_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia"
 
-INTERVAL_MAX_LENGTH = 25
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+_PATH_TO_DATA = './IMDA - National Speech Corpus/PART1'
+# _PATH_TO_DATA = './PART1/DATA'
 
 class Minds14Config(datasets.BuilderConfig):
     """BuilderConfig for xtreme-s"""
 
     def __init__(
-        self, channel, description, homepage, path_to_data
+        self, channel, gender, race, description, homepage, path_to_data
     ):
         super(Minds14Config, self).__init__(
-            name=channel,
+            name=channel+gender+race,
             version=datasets.Version("1.0.0", ""),
             description=self.description,
         )
         self.channel = channel
+        self.gender = gender
+        self.race = race
         self.description = description
         self.homepage = homepage
         self.path_to_data = path_to_data
 
 
-def _build_config(channel):
+def _build_config(channel, gender, race):
     return Minds14Config(
         channel=channel,
+        gender=gender,
+        race=race,
         description=_DESCRIPTION,
         homepage=_HOMEPAGE,
         path_to_data=_PATH_TO_DATA,
@@ -119,21 +73,25 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = []
     for channel in _CHANNEL_CONFIGS + ["all"]:
-        BUILDER_CONFIGS.append(_build_config(channel))
+        for gender in _GENDER_CONFIGS + ["all"]:
+            for race in _RACE_CONFIGS + ["all"]:
+                BUILDER_CONFIGS.append(_build_config(channel, gender, race))
     # BUILDER_CONFIGS = [_build_config(name) for name in _CHANNEL_CONFIGS + ["all"]]
 
-    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "allallall"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
         task_templates = None
+        # mics = _CHANNEL_CONFIGS
         features = datasets.Features(
             {
-                "audio": datasets.features.Audio(),
+                "audio": datasets.features.Audio(sampling_rate=16000),
                 "transcript": datasets.Value("string"),
                 "mic": datasets.Value("string"),
                 "audio_name": datasets.Value("string"),
-                "interval": datasets.Value("string")
+                "gender": datasets.Value("string"),
+                "race": datasets.Value("string"),
             }
         )
         
@@ -163,51 +121,31 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             else [self.config.channel]
         )
 
-        json_path = dl_manager.download(os.path.join(self.config.path_to_data, "directory_list.json"))
-        print(f"json_path: {json_path}")
-        with open(json_path, "r") as f:
-            directory_dict = json.load(f)
-        print(f"directory_dict: {directory_dict}")
-        
-        train_audio_list = []
-        test_audio_list = []
-        for mic in mics:
-            audio_list = []
-            if mic == "Audio Same CloseMic":
-                audio_list = [x for x in directory_dict[mic] if (x[-5] == "1") ]
-                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
-                for path in train:
-                    train_audio_list.append(os.path.join(self.config.path_to_data, mic, path))
-                    s = list(path)
-                    s[-5] = "2"
-                    train_audio_list.append(os.path.join(self.config.path_to_data, mic, "".join(s)))
-                for path in test:
-                    test_audio_list.append(os.path.join(self.config.path_to_data, mic, path))
-                    s = list(path)
-                    s[-5] = "2"
-                    test_audio_list.append(os.path.join(self.config.path_to_data, mic, "".join(s)))
-            elif mic == "Audio Separate IVR":
-                audio_list = [x.split("\\")[0] for x in directory_dict[mic]]
-                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
-                for folder in train:
-                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x.split("\\")[0]==folder)]
-                    train_audio_list.extend(audios)
-                for folder in test:
-                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x.split("\\")[0]==folder)]
-                    test_audio_list.extend(audios)
-            elif mic == "Audio Separate StandingMic":
-                audio_list = [x[:14] for x in directory_dict[mic]]
-                audio_list = list(set(audio_list))
-                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
-                for folder in train:
-                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x[:14]==folder)]
-                    train_audio_list.extend(audios)
-                for folder in test:
-                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x[:14]==folder)]
-                    test_audio_list.extend(audios)
-                            
-        print(f"train_audio_list: { train_audio_list}")
-        print(f"test_audio_list: { test_audio_list}")
+        gender = (
+            _GENDER_CONFIGS
+            if self.config.gender == "all"
+            else [self.config.gender]
+        )
+
+        race = (
+            _RACE_CONFIGS
+            if self.config.race == "all"
+            else [self.config.race]
+        )
+
+        # augment speaker ids directly here
+        # read the speaker information
+        train_speaker_ids = []
+        test_speaker_ids = []
+        # path_to_speaker = os.path.join(self.config.path_to_data, "DOC", "Speaker Information (Part 1).XLSX")
+        path_to_speaker = dl_manager.download(os.path.join(self.config.path_to_data, "DOC", "Speaker Information (Part 1).XLSX"))
+        speaker_df = pd.read_excel(path_to_speaker, dtype={'SCD/PART1': object})
+        for g in gender:
+            for r in race:
+                X = speaker_df[(speaker_df["ACC"]==r) & (speaker_df["SEX"]==g)]
+                X_train, X_test = train_test_split(X, test_size=0.3, random_state=42, shuffle=True)
+                train_speaker_ids.extend(X_train["SCD/PART1"])
+                test_speaker_ids.extend(X_test["SCD/PART1"])
 
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
@@ -216,17 +154,23 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
             name=datasets.Split.TRAIN,
             gen_kwargs={
-                # "path_to_data": os.path.join(self.config.path_to_data, "Audio Same CloseMic"),
-                "audio_list": train_audio_list,
-                "dl_manager":dl_manager,
+                "path_to_data": self.config.path_to_data,
+                "speaker_metadata":speaker_df,
+                # "speaker_ids": train_speaker_ids,
+                "speaker_ids":["0001"],
+                "mics": mics,
+                "dl_manager": dl_manager
               },
           ),
           datasets.SplitGenerator(
             name=datasets.Split.TEST,
             gen_kwargs={
-                # "path_to_data": os.path.join(self.config.path_to_data, "Audio Same CloseMic"),
-                "audio_list": test_audio_list,
-                "dl_manager":dl_manager,
+                "path_to_data": self.config.path_to_data,
+                "speaker_metadata":speaker_df,
+                # "speaker_ids": test_speaker_ids,
+                "speaker_ids": ["0003"],
+                "mics": mics,
+                "dl_manager": dl_manager
             },
         ),
         ]
@@ -234,112 +178,55 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(
             self,
-            audio_list,
-            dl_manager,
+            path_to_data,
+            speaker_metadata,
+            speaker_ids,
+            mics,
+            dl_manager
         ):
         id_ = 0
-        for audio_path in audio_list:
-            file = os.path.split(audio_path)[-1]
-            folder = os.path.split(os.path.split(audio_path)[0])[-1]
-            # get script_path
-            if file.split("_")[0] == "conf":
-                # mic == "Audio Separate IVR"
-                s = list(file)
-                s[14] = "_"
-                file = ''.join(s)
-                script_path = os.path.join(self.config.path_to_data, "Scripts Separate", file[:-4]+".TextGrid")
-            elif folder.split()[1] == "Same":
-                # mic == "Audio Same CloseMic IVR"
-                script_path = os.path.join(self.config.path_to_data, "Scripts Same", file[:-4]+".TextGrid")
-            elif folder.split()[1] == "Separate":
-                # mic == "Audio Separate StandingMic":
-                script_path = os.path.join(self.config.path_to_data, "Scripts Separate", file[:-4]+".TextGrid")
-            
-
-            # LOAD TRANSCRIPT
-            # script_path = os.path.join(self.config.path_to_data, 'Scripts Same', '3000-1.TextGrid')
-            # check that the textgrid file can be read
-            script_path = dl_manager.download(script_path)            
-            try:
-                # tg = textgrid.TextGrid.fromFile(script_path)
-                with open(script_path, "rb") as f:
-                    tg = f.read()
-                    tg_dict = textgrids.TextGrid() 
-                    tg_dict.parse(tg)
-                for key in tg_dict.keys():
-                    tg = tg_dict[key]
-            except UnicodeDecodeError:
-                with open(script_path, "rb") as f:
-                    tg = f.read()
-                    decoded = tg.decode('utf-16')
-                    encoded = decoded.encode('utf-8')
-                    tg_dict = textgrids.TextGrid() 
-                    tg_dict.parse(encoded)
-                for key in tg_dict.keys():
-                    tg = tg_dict[key]
-            except TypeError:
-                with open(script_path, "rb") as f:
-                    tg = f.read()
-                    decoded = tg.decode('utf-8-sig')
-                    encoded = decoded.encode('utf-8')
-                    tg_dict = textgrids.TextGrid() 
-                    tg_dict.parse(encoded)
-                for key in tg_dict.keys():
-                    tg = tg_dict[key]
-
-            except Exception as e:
-                print(f"error reading textgrid file, {script_path}, {str(e)}")
-                continue
-            # LOAD AUDIO
-            # check that archive path exists, else will not open the archive
-            audio_path = dl_manager.download(audio_path)
-            if os.path.exists(audio_path):
-                with open(audio_path, 'rb') as f:
-                    data, sr = sf.read(f)
-                # data, sr = sf.read(audio_path)
-                result = {}
-                i = 0
-                intervalLength = 0
-                intervalStart = 0
-                transcript_list = []
-                # filepath = os.path.join(self.config.path_to_data, 'tmp_clip.wav')
-                # filepath = dl_manager.download(filepath)
-                tempWavFile = tempfile.mktemp('.wav')
-                while i < (len(tg)-1):
-                    transcript = cleanup_string(tg[i].text)
-                    if intervalLength == 0 and len(transcript) == 0:
-                        intervalStart = tg[i].xmax
-                        i+=1
-                        continue
-                    intervalLength += tg[i].xmax-tg[i].xmin
-                    if intervalLength > INTERVAL_MAX_LENGTH:
-                        print(f"INTERVAL LONGER THAN {intervalLength}")
-                        result["transcript"] = transcript
-                        result["interval"] = "start:"+str(tg[i].xmin)+", end:"+str(tg[i].xmax)
-                        result["audio"] = {"path": audio_path, "bytes": data[int(tg[i].xmin*sr):int(tg[i].xmax*sr)], "sampling_rate":sr}
-                        yield id_, result
-                        id_+= 1
-                        intervalLength = 0
-                    else:
-                        if (intervalLength + tg[i+1].xmax-tg[i+1].xmin) < INTERVAL_MAX_LENGTH:
-                            if len(transcript) != 0:
-                                transcript_list.append(transcript)
-                            i+=1
-                            continue
-                        if len(transcript) == 0:
-                            spliced_audio = data[int(intervalStart*sr):int(tg[i].xmin*sr)]
-                        else:
-                            transcript_list.append(transcript)
-                            spliced_audio = data[int(intervalStart*sr):int(tg[i].xmax*sr)]
+        for mic in mics:
+            for speaker in speaker_ids:
+                # TRANSCRIPT: in the case of error, if no file found then dictionary will b empty
+                d = {}
+                counter = 0
+                while counter < 10:
+                    data = dl_manager.download(os.path.join(path_to_data, "DATA", mic, "SCRIPT", mic[-1]+speaker+str(counter)+'.TXT'))
+                    try:
+                        line_num = 0
+                        with open(data, encoding='utf-8-sig') as f:
+                            for line in f:
+                                if line_num == 0:
+                                    key = line.split("\t")[0]
+                                    line_num += 1
+                                elif line_num == 1:
+                                    d[key] = line.strip()
+                                    line_num -= 1
+                    except:
+                        print(f"{counter}")
+                        break
+                    counter+=1
+                # AUDIO: in the case of error it will skip the speaker
+                # archive_path = os.path.join(path_to_data, "DATA", mic, "WAVE", "SPEAKER"+speaker+'.zip')
+                archive_path = dl_manager.download(os.path.join(path_to_data, "DATA", mic, "WAVE", "SPEAKER"+speaker+'.zip'))
+                # check that archive path exists, else will not open the archive
+                if os.path.exists(archive_path):
+                    audio_files = dl_manager.iter_archive(archive_path)
+                    for path, f in audio_files:
+                        # bug catching if any error?
+                        result = {}
+                        full_path = os.path.join(archive_path, path) if archive_path else path # bug catching here
+                        result["audio"] = {"path": full_path, "bytes": f.read()}
+                        result["audio_name"] = path
+                        result["mic"] = mic
+                        metadata_row = speaker_metadata.loc[speaker_metadata["SCD/PART1"]==speaker].iloc[0]
+                        result["gender"]=metadata_row["SEX"]
+                        result["race"]=metadata_row["ACC"]
+                        try:
+                            result["transcript"] = d[f.name[-13:-4]]
+                            yield id_, result
+                            id_ += 1
+                        except:
+                            print(f"unable to find transcript")
                         
-                        sf.write(tempWavFile,spliced_audio, sr )
-                        # sf.write(filepath, spliced_audio, sr)
-                        result["interval"] = "start:"+str(intervalStart)+", end:"+str(tg[i].xmax)
-                        result["audio"] = {"path": tempWavFile, "bytes": spliced_audio, "sampling_rate":sr}
-                        result["transcript"] = ' '.join(transcript_list)
-                        yield id_, result
-                        id_+= 1
-                        intervalLength=0
-                        intervalStart=tg[i].xmax
-                        transcript_list = []
-                    i+=1
+                        

imda_nsc_part3.py

@@ -0,0 +1,348 @@
+import os
+import datasets
+# import pandas as pd
+from sklearn.model_selection import train_test_split
+import textgrids
+import io
+import soundfile as sf
+from urllib.request import urlopen
+import re
+import json
+import tempfile
+
+def cleanup_string(line):
+
+    words_to_remove = ['(ppo)','(ppc)', '(ppb)', '(ppl)', '<s/>','<c/>','<q/>', '<fil/>', '<sta/>', '<nps/>', '<spk/>', '<non/>', '<unk>', '<s>', '<z>', '<nen>']
+
+    formatted_line = re.sub(r'\s+', ' ', line).strip().lower()
+
+    #detect all word that matches words in the words_to_remove list
+    for word in words_to_remove:
+        if re.search(word,formatted_line):
+            # formatted_line = re.sub(word,'', formatted_line)
+            formatted_line = formatted_line.replace(word,'')
+            formatted_line = re.sub(r'\s+', ' ', formatted_line).strip().lower()
+            # print("*** removed words: " + formatted_line)
+
+    #detect '\[(.*?)\].' e.g. 'Okay [ah], why did I gamble?'
+    #remove [ ] and keep text within
+    if re.search('\[(.*?)\]', formatted_line):
+        formatted_line = re.sub('\[(.*?)\]', r'\1', formatted_line).strip()
+        #print("***: " + formatted_line)
+
+    #detect '\((.*?)\).' e.g. 'Okay (um), why did I gamble?'
+    #remove ( ) and keep text within
+    if re.search('\((.*?)\)', formatted_line):
+        formatted_line = re.sub('\((.*?)\)', r'\1', formatted_line).strip()
+        # print("***: " + formatted_line)
+
+    #detect '\'(.*?)\'' e.g. 'not 'hot' per se'
+    #remove ' ' and keep text within
+    if re.search('\'(.*?)\'', formatted_line):
+        formatted_line = re.sub('\'(.*?)\'', r'\1', formatted_line).strip()
+        #print("***: " + formatted_line)
+
+    #remove punctation '''!()-[]{};:'"\, <>./?@#$%^&*_~'''
+    punctuation = '''!–;"\,./?@#$%^&*~''' 
+    punctuation_list = str.maketrans("","",punctuation)
+    formatted_line = re.sub(r'-', ' ', formatted_line)
+    formatted_line = re.sub(r'_', ' ', formatted_line)
+    formatted_line = formatted_line.translate(punctuation_list)
+    formatted_line = re.sub(r'\s+', ' ', formatted_line).strip().lower()
+    #print("***: " + formatted_line)
+
+    return formatted_line
+
+
+
+_DESCRIPTION = """\
+The National Speech Corpus (NSC) is the first large-scale Singapore English corpus 
+spearheaded by the Info-communications and Media Development Authority (IMDA) of Singapore.
+"""
+
+_CITATION = """\
+"""
+_CHANNEL_CONFIGS = sorted([
+    "Audio Same CloseMic", "Audio Separate IVR", "Audio Separate StandingMic"
+])
+
+_HOMEPAGE = "https://www.imda.gov.sg/how-we-can-help/national-speech-corpus"
+
+_LICENSE = ""
+
+# _PATH_TO_DATA = './IMDA - National Speech Corpus/PART3'
+_PATH_TO_DATA = './PART3'
+
+INTERVAL_MAX_LENGTH = 25
+
+class Minds14Config(datasets.BuilderConfig):
+    """BuilderConfig for xtreme-s"""
+
+    def __init__(
+        self, channel, description, homepage, path_to_data
+    ):
+        super(Minds14Config, self).__init__(
+            name=channel,
+            version=datasets.Version("1.0.0", ""),
+            description=self.description,
+        )
+        self.channel = channel
+        self.description = description
+        self.homepage = homepage
+        self.path_to_data = path_to_data
+
+
+def _build_config(channel):
+    return Minds14Config(
+        channel=channel,
+        description=_DESCRIPTION,
+        homepage=_HOMEPAGE,
+        path_to_data=_PATH_TO_DATA,
+    )
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = []
+    for channel in _CHANNEL_CONFIGS + ["all"]:
+        BUILDER_CONFIGS.append(_build_config(channel))
+    # BUILDER_CONFIGS = [_build_config(name) for name in _CHANNEL_CONFIGS + ["all"]]
+
+    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        task_templates = None
+        features = datasets.Features(
+            {
+                "audio": datasets.features.Audio(),
+                "transcript": datasets.Value("string"),
+                "audio_name": datasets.Value("string"),
+                "interval": datasets.Value("string")
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            supervised_keys=("audio", "transcript"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+            task_templates=task_templates,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        mics = (
+            _CHANNEL_CONFIGS
+            if self.config.channel == "all"
+            else [self.config.channel]
+        )
+
+        json_path = dl_manager.download(os.path.join(self.config.path_to_data, "directory_list_part3.json"))
+        print(f"json_path: {json_path}")
+        with open(json_path, "r") as f:
+            directory_dict = json.load(f)
+        print(f"directory_dict: {directory_dict}")
+        
+        train_audio_list = []
+        test_audio_list = []
+        for mic in mics:
+            audio_list = []
+            if mic == "Audio Same CloseMic":
+                audio_list = [x for x in directory_dict[mic] if (x[-5] == "1") ]
+                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
+                for path in train:
+                    train_audio_list.append(os.path.join(self.config.path_to_data, mic, path))
+                    s = list(path)
+                    s[-5] = "2"
+                    train_audio_list.append(os.path.join(self.config.path_to_data, mic, "".join(s)))
+                for path in test:
+                    test_audio_list.append(os.path.join(self.config.path_to_data, mic, path))
+                    s = list(path)
+                    s[-5] = "2"
+                    test_audio_list.append(os.path.join(self.config.path_to_data, mic, "".join(s)))
+            elif mic == "Audio Separate IVR":
+                audio_list = [x.split("\\")[0] for x in directory_dict[mic]]
+                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
+                for folder in train:
+                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x.split("\\")[0]==folder)]
+                    train_audio_list.extend(audios)
+                for folder in test:
+                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x.split("\\")[0]==folder)]
+                    test_audio_list.extend(audios)
+            elif mic == "Audio Separate StandingMic":
+                audio_list = [x[:14] for x in directory_dict[mic]]
+                audio_list = list(set(audio_list))
+                train, test = train_test_split(audio_list, test_size=0.3, random_state=42, shuffle=True)
+                for folder in train:
+                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x[:14]==folder)]
+                    train_audio_list.extend(audios)
+                for folder in test:
+                    audios = [os.path.join(self.config.path_to_data, mic, x) for x in directory_dict[mic] if (x[:14]==folder)]
+                    test_audio_list.extend(audios)
+                            
+        print(f"train_audio_list: { train_audio_list}")
+        print(f"test_audio_list: { test_audio_list}")
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        return [
+            datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                # "path_to_data": os.path.join(self.config.path_to_data, "Audio Same CloseMic"),
+                "audio_list": train_audio_list,
+                "dl_manager":dl_manager,
+              },
+          ),
+          datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                # "path_to_data": os.path.join(self.config.path_to_data, "Audio Same CloseMic"),
+                "audio_list": test_audio_list,
+                "dl_manager":dl_manager,
+            },
+        ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(
+            self,
+            audio_list,
+            dl_manager,
+        ):
+        id_ = 0
+        for audio_path in audio_list:
+            file = os.path.split(audio_path)[-1]
+            folder = os.path.split(os.path.split(audio_path)[0])[-1]
+            # get script_path
+            if file.split("_")[0] == "conf":
+                # mic == "Audio Separate IVR"
+                s = list(file)
+                s[14] = "_"
+                file = ''.join(s)
+                script_path = os.path.join(self.config.path_to_data, "Scripts Separate", file[:-4]+".TextGrid")
+            elif folder.split()[1] == "Same":
+                # mic == "Audio Same CloseMic IVR"
+                script_path = os.path.join(self.config.path_to_data, "Scripts Same", file[:-4]+".TextGrid")
+            elif folder.split()[1] == "Separate":
+                # mic == "Audio Separate StandingMic":
+                script_path = os.path.join(self.config.path_to_data, "Scripts Separate", file[:-4]+".TextGrid")
+            
+
+            # LOAD TRANSCRIPT
+            # script_path = os.path.join(self.config.path_to_data, 'Scripts Same', '3000-1.TextGrid')
+            # check that the textgrid file can be read
+            script_path = dl_manager.download(script_path)            
+            try:
+                # tg = textgrid.TextGrid.fromFile(script_path)
+                with open(script_path, "rb") as f:
+                    tg = f.read()
+                    tg_dict = textgrids.TextGrid() 
+                    tg_dict.parse(tg)
+                for key in tg_dict.keys():
+                    tg = tg_dict[key]
+            except UnicodeDecodeError:
+                with open(script_path, "rb") as f:
+                    tg = f.read()
+                    decoded = tg.decode('utf-16')
+                    encoded = decoded.encode('utf-8')
+                    tg_dict = textgrids.TextGrid() 
+                    tg_dict.parse(encoded)
+                for key in tg_dict.keys():
+                    tg = tg_dict[key]
+            except TypeError:
+                with open(script_path, "rb") as f:
+                    tg = f.read()
+                    decoded = tg.decode('utf-8-sig')
+                    encoded = decoded.encode('utf-8')
+                    tg_dict = textgrids.TextGrid() 
+                    tg_dict.parse(encoded)
+                for key in tg_dict.keys():
+                    tg = tg_dict[key]
+
+            except Exception as e:
+                print(f"error reading textgrid file, {script_path}, {str(e)}")
+                continue
+            # LOAD AUDIO
+            # check that archive path exists, else will not open the archive
+            audio_path = dl_manager.download(audio_path)
+            if os.path.exists(audio_path):
+                with open(audio_path, 'rb') as f:
+                    data, sr = sf.read(f)
+                # data, sr = sf.read(audio_path)
+                result = {}
+                i = 0
+                intervalLength = 0
+                intervalStart = 0
+                transcript_list = []
+                # filepath = os.path.join(self.config.path_to_data, 'tmp_clip.wav')
+                # filepath = dl_manager.download(filepath)
+                tempWavFile = tempfile.mktemp('.wav')
+                while i < (len(tg)-1):
+                    transcript = cleanup_string(tg[i].text)
+                    if intervalLength == 0 and len(transcript) == 0:
+                        intervalStart = tg[i].xmax
+                        i+=1
+                        continue
+                    intervalLength += tg[i].xmax-tg[i].xmin
+                    if intervalLength > INTERVAL_MAX_LENGTH:
+                        print(f"INTERVAL LONGER THAN {intervalLength}")
+                        spliced_audio = data[int(tg[i].xmin*sr):int(tg[i].xmax*sr)]
+                        sf.write(tempWavFile,spliced_audio, sr)
+                        result["transcript"] = transcript
+                        result["interval"] = "start:"+str(tg[i].xmin)+", end:"+str(tg[i].xmax)
+                        result["audio"] = {"path": tempWavFile, "bytes": spliced_audio, "sampling_rate":sr}
+                        result["audio_name"] = audio_path
+                        yield id_, result
+                        id_+= 1
+                        intervalLength = 0
+                    else:
+                        if (intervalLength + tg[i+1].xmax-tg[i+1].xmin) < INTERVAL_MAX_LENGTH:
+                            if len(transcript) != 0:
+                                transcript_list.append(transcript)
+                            i+=1
+                            continue
+                        if len(transcript) == 0:
+                            spliced_audio = data[int(intervalStart*sr):int(tg[i].xmin*sr)]
+                        else:
+                            transcript_list.append(transcript)
+                            spliced_audio = data[int(intervalStart*sr):int(tg[i].xmax*sr)]
+                        
+                        sf.write(tempWavFile,spliced_audio, sr )
+                        # sf.write(filepath, spliced_audio, sr)
+                        result["interval"] = "start:"+str(intervalStart)+", end:"+str(tg[i].xmax)
+                        result["audio"] = {"path": tempWavFile, "bytes": spliced_audio, "sampling_rate":sr}
+                        result["transcript"] = ' '.join(transcript_list)
+                        result["audio_name"] = audio_path
+                        yield id_, result
+                        id_+= 1
+                        intervalLength=0
+                        intervalStart=tg[i].xmax
+                        transcript_list = []
+                    i+=1