Delete sesge.py

sesge.py

@@ -1,120 +0,0 @@
-import csv
-import os
-import json
-
-import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
-
-from scipy.io.wavfile import read, write
-import io
-
-#from .release_stats import STATS
-
-_CITATION = """\
-@inproceedings{demint2024,
-  author = {Pérez-Ortiz, Juan Antonio and 
-    Esplà-Gomis, Miquel and 
-    Sánchez-Cartagena, Víctor M. and 
-    Sánchez-Martínez, Felipe and 
-    Chernysh, Roman and 
-    Mora-Rodríguez, Gabriel and 
-    Berezhnoy, Lev},
-  title = {{DeMINT}: Automated Language Debriefing for English Learners via {AI} 
-           Chatbot Analysis of Meeting Transcripts},
-  booktitle = {Proceedings of the 13th Workshop on NLP for Computer Assisted Language Learning},
-  month = october,
-  year = {2024},
-  url = {https://aclanthology.org/volumes/2024.nlp4call-1/},
-}
-"""
-
-class SesgeConfig(datasets.BuilderConfig):
-    def __init__(self, name, version, **kwargs):
-        self.language = kwargs.pop("language", None)
-        self.release_date = kwargs.pop("release_date", None)
-        """
-        description = (
-            f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
-            f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
-            f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
-            f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
-        )
-        """
-
-        super(SesgeConfig, self).__init__(
-            name=name,
-            **kwargs,
-        )
-
-class Sesge():
-
-    BUILDER_CONFIGS = [
-        SesgeConfig(
-            name="sesge",
-            version=1.0,
-            language='eng',
-            release_date="2024-10-8",
-        )
-    ]
-
-    def _info(self):
-        total_languages = 1
-        total_valid_hours = 1
-        description = (
-            "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
-            f"The dataset currently consists of {total_valid_hours} validated hours of speech "
-            f" in {total_languages} languages, but more voices and languages are always added."
-        )
-        features = datasets.Features(
-            {
-                "audio": datasets.features.Audio(sampling_rate=48_000),
-                "sentence": datasets.Value("string"),
-            }
-        )
-
-    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path, split):
-        archives = os.listdir(archives)
-        print(archives)
-        metadata = {}
-        with open(meta_path, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter=";", quoting=csv.QUOTE_NONE)
-            for row in tqdm(reader):
-                metadata[row["file_name"]] = row
-        #print(metadata)
-        for i, path in enumerate(archives):
-            #for path, file in audio_archive:
-            _, filename = os.path.split(path)
-            file = os.path.join("data", split, filename)
-            #print(filename)
-            if file in metadata:
-                result = dict(metadata[file])
-                print("Result: ", result)
-                with open(os.path.join(local_extracted_archive_paths, filename), 'rb') as wavfile:
-                    input_wav = wavfile.read()
-
-                rate, data = read(io.BytesIO(input_wav))
-
-                # data is a numpy ND array representing the audio data. Let's do some stuff with it
-                reversed_data = data[::-1] #reversing it
-
-                #then, let's save it to a BytesIO object, which is a buffer for bytes object
-                bytes_wav = bytes()
-                byte_io = io.BytesIO(bytes_wav)
-                write(byte_io, rate, reversed_data)
-
-                output_wav = byte_io.read()
-                # set the audio feature and the path to the extracted file
-                path = os.path.join(local_extracted_archive_paths[i], path)
-                result["audio"] = {"path": path, "bytes": data}
-                result["path"] = path
-                yield path, result
-            else:
-                print("No file found")
-                yield None, None
-
-if __name__ == '__main__':
-    data = Sesge()
-    gen = data._generate_examples("/Users/rafael/Desktop/TFM/Transformes/Demint/Base de datos/COnver/datos/", "/Users/rafael/Desktop/TFM/Transformes/Demint/Base de datos/COnver/datos/", "/Users/rafael/Desktop/TFM/Transformes/Demint/Base de datos/COnver/metadata.csv", "train")
-
-    print(next(gen))