Update test.py

test.py

@@ -1,262 +1,119 @@
-# coding=utf-8
-# Lint as: python3
-"""test set"""
-
-
-import csv
-import os
-import json
-
-import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
-
-
-_CITATION = """\
-@inproceedings{panayotov2015librispeech,
-  title={Librispeech: an ASR corpus based on public domain audio books},
-  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
-  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
-  pages={5206--5210},
-  year={2015},
-  organization={IEEE}
-}
-"""
-
-_DESCRIPTION = """\
-Lorem ipsum
-"""
-
-_BASE_URL = "https://huggingface.co/datasets/j-krzywdziak/test/tree/main"
-_AUDIO_URL = _BASE_URL + "dev.tar.gz"
-_TRANSCRIPT_URL = _BASE_URL + "dev.tsv"
-
-# _DL_URLS = {
-#     "clean": {
-#         "dev": _DL_URL + "dev-clean.tar.gz",
-#         "test": _DL_URL + "test-clean.tar.gz",
-#         "train.100": _DL_URL + "train-clean-100.tar.gz",
-#         "train.360": _DL_URL + "train-clean-360.tar.gz",
-#     },
-#     "other": {
-#         "test": _DL_URL + "test-other.tar.gz",
-#         "dev": _DL_URL + "dev-other.tar.gz",
-#         "train.500": _DL_URL + "train-other-500.tar.gz",
-#     },
-#     "all": {
-#         "dev.clean": _DL_URL + "dev-clean.tar.gz",
-#         "dev.other": _DL_URL + "dev-other.tar.gz",
-#         "test.clean": _DL_URL + "test-clean.tar.gz",
-#         "test.other": _DL_URL + "test-other.tar.gz",
-#         "train.clean.100": _DL_URL + "train-clean-100.tar.gz",
-#         "train.clean.360": _DL_URL + "train-clean-360.tar.gz",
-#         "train.other.500": _DL_URL + "train-other-500.tar.gz",
-#     },
-# }
-
-logger = datasets.logging.get_logger(__name__)
-
-
-class TestASRConfig(datasets.BuilderConfig):
-    """BuilderConfig for TestASR."""
-
-    def __init__(self, **kwargs):
-        """
-        Args:
-          data_dir: `string`, the path to the folder containing the files in the
-            downloaded .tar
-          citation: `string`, citation for the data set
-          url: `string`, url for information about the data set
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(TestASRConfig, self).__init__(**kwargs)
-
-
-class TestASR(datasets.GeneratorBasedBuilder):
-    """Test dataset."""
-    #
-    # DEFAULT_WRITER_BATCH_SIZE = 256
-    # DEFAULT_CONFIG_NAME = "all"
-    # BUILDER_CONFIGS = [
-    #     LibrispeechASRConfig(name="clean", description="'Clean' speech."),
-    #     LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
-    #     LibrispeechASRConfig(name="all", description="Combined clean and other dataset."),
-    # ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "audio_id": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                    "ngram": datasets.Value("string")
-                }
-            ),
-            supervised_keys=None,
-            homepage=_BASE_URL,
-            citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_AUDIO_URL)
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
-        meta_path = dl_manager.download(_TRANSCRIPT_URL)
-        return [datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "audio_id": meta_path,
-                "ngram": meta_path,
-                "audio": local_extracted_archive
-            }
-        )]
-        #
-        # if self.config.name == "clean":
-        #     train_splits = [
-        #         datasets.SplitGenerator(
-        #             name="train.100",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.100"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.100"]),
-        #             },
-        #         ),
-        #         datasets.SplitGenerator(
-        #             name="train.360",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.360"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.360"]),
-        #             },
-        #         ),
-        #     ]
-        #     dev_splits = [
-        #         datasets.SplitGenerator(
-        #             name=datasets.Split.VALIDATION,
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("dev"),
-        #                 "files": dl_manager.iter_archive(archive_path["dev"]),
-        #             },
-        #         )
-        #     ]
-        #     test_splits = [
-        #         datasets.SplitGenerator(
-        #             name=datasets.Split.TEST,
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("test"),
-        #                 "files": dl_manager.iter_archive(archive_path["test"]),
-        #             },
-        #         )
-        #     ]
-        # elif self.config.name == "other":
-        #     train_splits = [
-        #         datasets.SplitGenerator(
-        #             name="train.500",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.500"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.500"]),
-        #             },
-        #         )
-        #     ]
-        #     dev_splits = [
-        #         datasets.SplitGenerator(
-        #             name=datasets.Split.VALIDATION,
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("dev"),
-        #                 "files": dl_manager.iter_archive(archive_path["dev"]),
-        #             },
-        #         )
-        #     ]
-        #     test_splits = [
-        #         datasets.SplitGenerator(
-        #             name=datasets.Split.TEST,
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("test"),
-        #                 "files": dl_manager.iter_archive(archive_path["test"]),
-        #             },
-        #         )
-        #     ]
-        # elif self.config.name == "all":
-        #     train_splits = [
-        #         datasets.SplitGenerator(
-        #             name="train.clean.100",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.clean.100"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
-        #             },
-        #         ),
-        #         datasets.SplitGenerator(
-        #             name="train.clean.360",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
-        #             },
-        #         ),
-        #         datasets.SplitGenerator(
-        #             name="train.other.500",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("train.other.500"),
-        #                 "files": dl_manager.iter_archive(archive_path["train.other.500"]),
-        #             },
-        #         ),
-        #     ]
-        #     dev_splits = [
-        #         datasets.SplitGenerator(
-        #             name="validation.clean",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("dev.clean"),
-        #                 "files": dl_manager.iter_archive(archive_path["dev.clean"]),
-        #             },
-        #         ),
-        #         datasets.SplitGenerator(
-        #             name="validation.other",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("dev.other"),
-        #                 "files": dl_manager.iter_archive(archive_path["dev.other"]),
-        #             },
-        #         ),
-        #     ]
-        #     test_splits = [
-        #         datasets.SplitGenerator(
-        #             name="test.clean",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("test.clean"),
-        #                 "files": dl_manager.iter_archive(archive_path["test.clean"]),
-        #             },
-        #         ),
-        #         datasets.SplitGenerator(
-        #             name="test.other",
-        #             gen_kwargs={
-        #                 "local_extracted_archive": local_extracted_archive.get("test.other"),
-        #                 "files": dl_manager.iter_archive(archive_path["test.other"]),
-        #             },
-        #         ),
-        #     ]
-
-        #return train_splits + dev_splits + test_splits
-
-    def _generate_examples(self, meta_path, local_extracted_archive):
-        """Generate examples from a LibriSpeech archive_path."""
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        with open(meta_path, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            for row in tqdm(reader, desc="Reading metadata..."):
-                if not row["audio_id"].endswith(".mp3"):
-                    row["audio_id"] += ".mp3"
-                for field in data_fields:
-                    if field not in row:
-                        row[field] = ""
-                metadata[row["path"]] = row
-
-        for filename, file in local_extracted_archive:
-            _, filename = os.path.split(filename)
-            if filename in metadata:
-                result = dict(metadata[filename])
-                # set the audio feature and the path to the extracted file
-                path = os.path.join(local_extracted_archive,
-                                    filename) if local_extracted_archive else filename
-                result["audio"] = {"path": path, "bytes": file.read()}
-                # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
-                result["path"] = path if local_extracted_archive else filename
-
-                yield path, result
+# coding=utf-8
+# Lint as: python3
+"""test set"""
+
+
+import csv
+import os
+import json
+
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+_CITATION = """\
+@inproceedings{panayotov2015librispeech,
+  title={Librispeech: an ASR corpus based on public domain audio books},
+  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
+  pages={5206--5210},
+  year={2015},
+  organization={IEEE}
+}
+"""
+
+_DESCRIPTION = """\
+Lorem ipsum
+"""
+
+_BASE_URL = "https://huggingface.co/datasets/j-krzywdziak/test/tree/main"
+_DATA_URL = _BASE_URL + "dev.tar.gz"
+_PROMPTS_URLS = _BASE_URL + "dev.tsv"
+
+logger = datasets.logging.get_logger(__name__)
+
+class TestConfig(datasets.BuilderConfig):
+    """Lorem impsum."""
+
+    def __init__(self, name, version, **kwargs):
+        # self.language = kwargs.pop("language", None)
+        # self.release_date = kwargs.pop("release_date", None)
+        # self.num_clips = kwargs.pop("num_clips", None)
+        # self.num_speakers = kwargs.pop("num_speakers", None)
+        # self.validated_hr = kwargs.pop("validated_hr", None)
+        # self.total_hr = kwargs.pop("total_hr", None)
+        # self.size_bytes = kwargs.pop("size_bytes", None)
+        # self.size_human = size_str(self.size_bytes)
+        description = (
+            f"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor "
+            f"incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud "
+            f"exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure "
+            f"dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. "
+            f"Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt "
+            f"mollit anim id est laborum."
+        )
+        super(TestConfigConfig, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+class TestASR(datasets.GeneratorBasedBuilder):
+    """Lorem ipsum."""
+    DEFAULT_CONFIG_NAME = "all"
+
+    BUILDER_CONFIGS = [
+        TestConfig(
+            name="Test Dataset",
+            version="0.0.0",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "audio_id": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "ngram": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage=_BASE_URL,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DATA_URL)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+        meta_path = dl_manager.download(_PROMPTS_URLS)
+        return [datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                "meta_file": meta_path,
+                "audio_files": dl_manager.iter_archive(local_extracted_archive)
+            }
+        )]
+
+    def _generate_examples(self, meta_path, audio_files):
+        """Lorem ipsum."""
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            for row in f:
+                audio_id = row.splt("\t")[0]
+                ngram = row.split("\t")[1]
+                metadata[audio_id] = {"audio_id": audio_id,
+                                      "ngram": ngram}
+
+        inside_clips_dir = True
+        id_ = 0
+        for path, f in audio_files:
+            _, audio_name = os.path.split(path)
+            if audio_name in metadata:
+                audio = {"bytes": f.read()}
+                yield id_, {**metadata[audio_id], "audio": audio}
+                id_ +=1