Upload cv17_es_other_automatically_verified.py

cv17_es_other_automatically_verified.py

@@ -0,0 +1,139 @@
+from collections import defaultdict
+import os
+import json
+import csv
+csv.field_size_limit(100000000)
+
+import datasets
+
+_NAME="cv17_es_other_automatically_verified"
+_VERSION="1.0.0"
+_AUDIO_EXTENSIONS=".mp3"
+
+_DESCRIPTION = """
+Split called -other- of the Spanish Common Voice v17.0 that was automatically verified
+using various ASR system.
+"""
+
+_CITATION = """
+@misc{carlosmena2024cv17autoveri,
+      title={Spanish Common Voice v17.0 Split Other Automatically Verified}, 
+      author={Mena, Carlos},
+      publisher={Barcelona Supercomputing Center}
+      year={2024},
+      url={https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified},
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified"
+
+_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
+
+_BASE_DATA_DIR = "corpus/"
+
+_METADATA_OTHER   = os.path.join(_BASE_DATA_DIR,"files","other.tsv")
+
+_TARS_REPO = os.path.join(_BASE_DATA_DIR,"files","tars_repo.paths")
+
+class CV17EsOtherAutomaticallyVerifiedConfig(datasets.BuilderConfig):
+    """BuilderConfig for The Spanish Common Voice v17.0 Split Other Automatically Verified"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class CV17EsOtherAutomaticallyVerified(datasets.GeneratorBasedBuilder):
+    """Spanish Common Voice v17.0 Split Other Automatically Verified"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        CV17EsOtherAutomaticallyVerifiedConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio": datasets.Audio(sampling_rate=16000),             
+                "client_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "sentence_id": datasets.Value("string"),
+                "sentence": datasets.Value("string"),
+                "sentence_domain": datasets.Value("string"),
+                "up_votes": datasets.Value("int32"),
+                "down_votes": datasets.Value("int32"),
+                "age": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "accents": datasets.Value("string"),
+                "variant": datasets.Value("string"),
+                "locale": datasets.Value("string"),
+                "segment": datasets.Value("string"),               
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        metadata_other=dl_manager.download_and_extract(_METADATA_OTHER)
+
+        tars_repo=dl_manager.download_and_extract(_TARS_REPO)
+       
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars_repo,'r') as f:
+            hash_tar_files['other']=[path.replace('\n','') for path in f]
+                
+        hash_meta_paths={"other":metadata_other}
+
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["other"]
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split:[None] * len(audio_paths[split]) for split in splits
+            }
+        )
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
+        return [
+            datasets.SplitGenerator(
+                name="other",
+                gen_kwargs={
+                    "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["other"]],
+                    "local_extracted_archives_paths": local_extracted_audio_paths["other"],
+                    "metadata_paths": hash_meta_paths["other"],
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+
+        features = ["client_id","sentence_id","sentence","sentence_domain","up_votes",
+                    "down_votes","age","gender", "accents","variant","locale","segment"]
+        
+        with open(metadata_paths) as f:
+            metadata = {x["path"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in audio_archive:
+                audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
+                audio_id=audio_id+_AUDIO_EXTENSIONS
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+                
+                try:                        
+                        yield audio_id, {
+                            "path": audio_id,
+                            **{feature: metadata[audio_id][feature] for feature in features},
+                            "audio": {"path": path, "bytes": audio_file.read()},
+                }
+                except:
+                        continue
+