dfjwfe

test.py

@@ -3,10 +3,13 @@
 """test set"""
 
 
+import csv
 import os
+import json
 
 import datasets
-from datasets.tasks import AutomaticSpeechRecognition
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
 
 
 _CITATION = """\
@@ -24,9 +27,9 @@ _DESCRIPTION = """\
 Lorem ipsum
 """
 
-_URL = "https://huggingface.co/datasets/j-krzywdziak/test/blob/main/dev_ngrams.tsv"
-_DL_URL = "https://huggingface.co/datasets/j-krzywdziak/test/blob/main/clips.tar.gz"
-
+_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": {
@@ -90,173 +93,169 @@ class TestASR(datasets.GeneratorBasedBuilder):
                     "ngram": datasets.Value("string")
                 }
             ),
-            supervised_keys=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+            supervised_keys=None,
+            homepage=_BASE_URL,
+            citation=_CITATION
         )
 
     def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_DL_URLS[self.config.name])
+        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={
+                "audios": local_extracted_archive,
+                "infos": meta_path
+            }
+        )]
+        #
+        # 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"]),
+        #             },
+        #         ),
+        #     ]
 
-        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
+        #return train_splits + dev_splits + test_splits
 
-    def _generate_examples(self, files, local_extracted_archive):
+    def _generate_examples(self, meta_path, local_extracted_archive):
         """Generate examples from a LibriSpeech archive_path."""
-        key = 0
-        audio_data = {}
-        transcripts = []
-        for path, f in files:
-            if path.endswith(".flac"):
-                id_ = path.split("/")[-1][: -len(".flac")]
-                audio_data[id_] = f.read()
-            elif path.endswith(".trans.txt"):
-                for line in f:
-                    if line:
-                        line = line.decode("utf-8").strip()
-                        id_, transcript = line.split(" ", 1)
-                        audio_file = f"{id_}.flac"
-                        speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
-                        audio_file = (
-                            os.path.join(local_extracted_archive, audio_file)
-                            if local_extracted_archive
-                            else audio_file
-                        )
-                        transcripts.append(
-                            {
-                                "id": id_,
-                                "speaker_id": speaker_id,
-                                "chapter_id": chapter_id,
-                                "file": audio_file,
-                                "text": transcript,
-                            }
-                        )
-            if audio_data and len(audio_data) == len(transcripts):
-                for transcript in transcripts:
-                    audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
-                    yield key, {"audio": audio, **transcript}
-                    key += 1
-                audio_data = {}
-                transcripts = []
+        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