Update app.py

app.py

@@ -7,22 +7,42 @@ import datasets
 
 
 _DESCRIPTION = """
-A large-scale speech corpus for representation learning, semi-supervised learning and interpretation.
+A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
 """
 
 _CITATION = """
-@inproceedings{}
+@inproceedings{wang-etal-2021-voxpopuli,
+    title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, 
+    Semi-Supervised Learning and Interpretation",
+    author = "Wang, Changhan  and
+      Riviere, Morgane  and
+      Lee, Ann  and
+      Wu, Anne  and
+      Talnikar, Chaitanya  and
+      Haziza, Daniel  and
+      Williamson, Mary  and
+      Pino, Juan  and
+      Dupoux, Emmanuel",
+    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics 
+    and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
+    month = aug,
+    year = "2021",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.acl-long.80",
+    doi = "10.18653/v1/2021.acl-long.80",
+    pages = "993--1003",
+}
 """
 
-_HOMEPAGE = ""
+_HOMEPAGE = "https://github.com/facebookresearch/voxpopuli"
 
-_LICENSE = ""
+_LICENSE = "CC0, also see https://www.europarl.europa.eu/legal-notice/en/"
 
 _ASR_LANGUAGES = [
     "hy"
 ]
 _ASR_ACCENTED_LANGUAGES = [
-    ""
+    "en_accented"
 ]
 
 _LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES
@@ -67,38 +87,84 @@ class HySpeech(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Define paths to your train, dev, and test data
-        data_dir = "data/"
-        train_data_dir = os.path.join(data_dir, "train")
-        dev_data_dir = os.path.join(data_dir, "dev")
-        test_data_dir = os.path.join(data_dir, "test")
-
-        # Load metadata files for train, dev, and test
-        train_metadata_path = os.path.join(data_dir, "train.tsv")
-        dev_metadata_path = os.path.join(data_dir, "dev.tsv")
-        test_metadata_path = os.path.join(data_dir, "test.tsv")
-
-        # Yield split generators for train, dev, and test
+        n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)
+        with open(n_shards_path) as f:
+            n_shards = json.load(f)
+
+        splits = ["train", "dev", "test"]
+
+        audio_urls = defaultdict(dict)
+        for split in splits:
+            audio_urls[split] = [_AUDIO_ARCHIVE_PATH.format(split=split)]
+
+        meta_urls = defaultdict(dict)
+        for split in splits:
+            meta_urls[split][lang] = _METADATA_PATH.format(split=split)
+
+        # dl_manager.download_config.num_proc = len(urls)
+
+        meta_paths = dl_manager.download_and_extract(meta_urls)
+        audio_paths = dl_manager.download(audio_urls)
+
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split: {lang: [None] * len(audio_paths[split]) for lang in self.config.languages} for split in splits
+            }
+        )
+
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": train_data_dir, "metadata_path": train_metadata_path}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": dev_data_dir, "metadata_path": dev_metadata_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_dir": test_data_dir, "metadata_path": test_metadata_path}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "audio_archives": {
+                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
+                        for lang_archives in audio_paths["train"].items()
+                    },
+                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
+                    "metadata_paths": meta_paths["train"],
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "audio_archives": {
+                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
+                        for lang_archives in audio_paths["dev"].items()
+                    },
+                    "local_extracted_archives_paths": local_extracted_audio_paths["dev"],
+                    "metadata_paths": meta_paths["dev"],
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "audio_archives": {
+                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
+                        for lang_archives in audio_paths["test"].items()
+                    },
+                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
+                    "metadata_paths": meta_paths["test"],
+                }
+            ),
         ]
 
-    def _generate_examples(self, data_dir, metadata_path):
-        # Load metadata from TSV file
-        with open(metadata_path, "r") as f:
-            metadata = csv.DictReader(f, delimiter="\t")
-
-            # Iterate over metadata to yield examples
-            for row in metadata:
-                audio_id = row["audio_id"]
-                audio_path = os.path.join(data_dir, row["audio_path"])  # Adjust column name accordingly
-                # Load audio file and yield example
-                with open(audio_path, "rb") as audio_file:
-                    yield audio_id, {
-                        "audio_id": audio_id,
-                        "language": row["language"],  # Adjust column name accordingly
-                        "audio": {"path": audio_path, "bytes": audio_file.read()},
-                        # Add other metadata fields as needed
-                    }
+    
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+        features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"]
+
+        meta_path = metadata_paths[lang]
+        with open(meta_path) as f:
+            metadata = {x["id"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives[lang], local_extracted_archives_paths[lang]):
+            for audio_filename, audio_file in audio_archive:
+                audio_id = audio_filename.split(os.sep)[-1].split(".wav")[0]
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+
+                yield audio_id, {
+                    "audio_id": audio_id,
+                    **{feature: metadata[audio_id][feature] for feature in features},
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                }