add script

my_dataset.py

@@ -0,0 +1,132 @@
+import LANGUAGES as LANGUAGES
+import STATS as STATS
+import datasets as datasets
+from datasets.utils.py_utils import size_str
+
+_HOMEPAGE = "homepage-info"
+_CITATION = "citation-info"
+_LICENSE = "license-info"
+_DESCRIPTION = "description-info"
+
+_PROMPTS_URLS = "....."
+_DATA_URL = "...."
+
+
+
+"""Configuration class, allows to have multiple configurations if needed"""
+class ParlaSpeechDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for ParlaSpeech"""
+
+    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 = ( ##Update Description in the final version
+            f"ParlaSpeech is a dataset in {self.language} released on {self.release_date}. "
+        )
+        super(ParlaSpeechDatasetConfig, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+
+class ParlaSpeechDataset(datasets.GeneratroBasedBuilder):
+
+    """"
+    ### NO TENGO CLARO SI HACE FALTA ESTO ###
+    DEFAULT_CONFIG_NAME = "all"
+
+    BUILDER_CONFIGS = [
+        ParlaSpeechDatasetConfig(
+            name=lang,
+            version=STATS["version"],
+            language=LANGUAGES[lang],
+            release_date=STATS["date"],
+            num_clips=lang_stats["clips"],
+            num_speakers=lang_stats["users"],
+            total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
+            size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
+        )
+        for lang, lang_stats in STATS["locales"].items()
+    ]
+    """
+
+    """ When the dataset is loaded and .info is called, the info defined here is displayed."""
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    #"speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version = self.config.version,
+        )
+
+    " Used to organize the audio files and sentence prompts in each split, once downloaded the dataset."
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators"""
+        prompts_paths = dl_manager.download(_PROMPTS_URLS)
+        archive = dl_manager.download(_DATA_URL)
+        ## local_extracted_archives = dl_manager.extract(archive)
+        train_dir = "vivos/train"
+        test_dir = "vivos/test"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "prompts_path": prompts_paths["train"],
+                    "path_to_clips": train_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "prompts_path": prompts_paths["test"],
+                    "path_to_clips": test_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
+        """Yields examples as (key, example) tuples."""
+        examples = {}
+        with open(prompts_path, encoding="utf-8") as f: ##prompts_path -> transcript.tsv
+            for row in f:
+                data = row.strip().split(" ", 1)
+                #speaker_id = data[0].split("_")[0]
+                #audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
+                audio_path = "/".join([path_to_clips, "DSPG_137_23122015_9873.69_9888.03.wav"])
+                examples[audio_path] = {
+                    #"speaker_id": speaker_id,
+                    "path": audio_path,
+                    "sentence": data[1],
+                }
+        inside_clips_dir = False
+        id_ = 0
+        for path, f in audio_files:
+            if path.startswith(path_to_clips):
+                inside_clips_dir = True
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield id_, {**examples[path], "audio": audio}
+                    id_ += 1
+            elif inside_clips_dir:
+                break