test new folder structure

.gitattributes

@@ -54,3 +54,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 2015-other.tar filter=lfs diff=lfs merge=lfs -text
+*.tsv filter=lfs diff=lfs merge=lfs -text

data/2015-other.tar → audio/clean/test/clean_test.tar

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a3cadbf50b6f4f588a2a07c895ba5d0ce0d778cf01263b0cf5aa3ee4fdd47bf7
-size 8640757760
+oid sha256:8ccbb447d99bb45a93a4b3aef70379bffaf88d9dd36248353c656883449556c9
+size 2715852800

audio/clean/train/clean_train.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c5d6ff4daaee62df0f071b671f7ce02c8c7cc8a5039ced70144ffc477e687bc
+size 7456389120

data/clean/test/clean_test.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:775e5335d84cfadda12e4e91c0c23d65d375f1a77b706ef93dea52e20d7d44cb
+size 1856141

data/clean/train/clean_train.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeaac895cf84c48497176d49aac9b7f8d44df1389222274235e21bd1ce89df82
+size 7898749

data/transcript.tsv

@@ -1,2 +0,0 @@
-path	sentence
-DSPG_137_23122015_9873.69_9888.03.wav	THIS IS A SAMPLE SENTENCE

my_dataset.py

@@ -1,132 +0,0 @@
-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"),
-                    "path": 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

nos-parlaspeech.py

@@ -0,0 +1,94 @@
+# coding=utf-8
+
+import pandas as pd
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = """\
+
+"""
+
+_DESCRIPTION = """\
+
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/5541827"
+
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_INDEX_REPO = "https://huggingface.co/datasets/proxectonos/Nos_Parlaspeech-GL/tree/main/"
+#_INDEX_REPO = "https://huggingface.co/datasets/proxectonos/Nos_Parlaspeech-GL/resolve/main/"
+
+_URLS = {
+    "index": _INDEX_REPO + "data/{config}/{split}/{config}_{split}.tsv",
+    "audio": "audio/{config}/{split}/{config}_{split}.tar",
+    #"audio": "audio/{config}/{split}/{config}_{split}.tar?download=1",
+}
+_SPLITS = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"}
+
+
+class ParlaSpeech(datasets.GeneratorBasedBuilder):
+    """Nos-ParlaSpeech."""
+
+    VERSION = datasets.Version("1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="clean", version=VERSION, description="XX hours of clean quality segments."),
+        datasets.BuilderConfig(name="other", version=VERSION, description="XX hours of other quality segments."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "path": datasets.Value("string"), #path to wav file
+                    "audio": datasets.features.Audio(), 
+                    "speaker_id": datasets.Value("int64"),
+                    "sentence": datasets.Value("string"),
+                    "gender": datasets.ClassLabel(names=["F", "M"]),
+                    "duration": datasets.Value("float64"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                AutomaticSpeechRecognition(transcription_column="sentence")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {
+            split: {key: url.format(config=self.config.name, split=_SPLITS[split]) for key, url in _URLS.items()}
+            for split in _SPLITS
+        }
+        dl_dir = dl_manager.download(urls)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "index_path": dl_dir[split]["index"],
+                    "audio_files": dl_manager.iter_archive(dl_dir[split]["audio"]),
+                },
+            )
+            for split in _SPLITS
+        ]
+
+    def _generate_examples(self, index_path, audio_files):
+        with open(index_path, encoding="utf-8") as index_file:
+            index = pd.read_csv(index_file, delimiter="\t", index_col="path").to_dict(orient="index")
+        # clean: 83568 = 79269 + 2155 + 2144 ; other: 146669 = 142813 + 1957 + 1899
+        for key, (path, file) in enumerate(audio_files):
+            if path.endswith(".wav"):
+                data = index.pop(path)
+                audio = {"path": path, "bytes": file.read()}
+                yield key, {"path": path, "audio": audio, **data}
+            else:
+                path = path + ".wav"
+                data = index.pop(path)
+                audio = {"path": path, "bytes": file.read()}
+                yield key, {"path": path, "audio": audio, **data}