Upload sesge.py

sesge.py

@@ -0,0 +1,97 @@
+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 = (
+            "A dataset containing English speech with grammatical errors, along with the corresponding transcriptions."
+            "Utterances are synthesized using a text-to-speech model, whereas the grammatically incorrect texts come from the C4_200M synthetic dataset."
+        )
+
+        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 = (
+            "A dataset containing English speech with grammatical errors, along with the corresponding transcriptions."
+            "Utterances are synthesized using a text-to-speech model, whereas the grammatically incorrect texts come from the C4_200M synthetic dataset."
+        )
+        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)
+        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
+
+        for i, path in enumerate(archives):
+            #for path, file in audio_archive:
+            _, filename = os.path.split(path)
+            file = os.path.join("data", split, 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))
+                
+                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