updated script

README.md

@@ -7,12 +7,19 @@ license:
 - mit
 multilinguality:
 - monolingual
-configs:
+dataset_info:
   - config_name: default
-    datafiles:
-      - split: train
-        path: transcript/train.tsv
-
+    features:
+      - name: path
+        dtype: string
+      - name: audio
+        dtype:
+          audio:
+            sampling_rate: 16000
+      - name: sentence
+        dtype: string
+      - name: speaker_id
+        dtype: string
 ---
 
 

test-user.py

@@ -0,0 +1,126 @@
+# coding=utf-8
+# Lint as: python3
+
+
+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/gcjavi/dataviewer-test"
+_DATA_URL = "data/train.zip"
+_PROMPTS_URLS = {"train": "transcript/train.tsv"}
+
+logger = datasets.logging.get_logger(__name__)
+
+class TestConfig(datasets.BuilderConfig):
+
+    def __init__(self, name, **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(TestConfig, self).__init__(
+            name=name,
+            description=description,
+            **kwargs,
+        )
+
+class TestASR(datasets.GeneratorBasedBuilder):
+    """Lorem ipsum."""
+
+
+    BUILDER_CONFIGS = [
+        TestConfig(
+            name="dataviewer-test",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage=_BASE_URL,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        audio_path = dl_manager.download(_DATA_URL)
+        local_extracted_archive = dl_manager.extract(audio_path) if not dl_manager.is_streaming else None
+        meta_path = dl_manager.download(_PROMPTS_URLS)
+        return [datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                "meta_path": meta_path["train"],
+                "audio_files": dl_manager.iter_archive(audio_path),
+                "local_extracted_archive": local_extracted_archive,
+            }
+        )]
+
+    def _generate_examples(self, meta_path, audio_files, local_extracted_archive):
+        """Lorem ipsum."""
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            next(f)
+            for row in f:
+                print(row)
+                r = row.split("\t")
+                print(r)
+                audio_id = r[0]
+                sentence = r[1]
+                speaker_id = r[2]
+                metadata[audio_id] = {"path": audio_id,
+                                      "sentence": sentence,
+                                      "speaker_id": speaker_id}
+
+        id_ = 0
+        for path, f in audio_files:
+            print(path, f)
+            _, audio_name = os.path.split(path)
+            if audio_name in metadata:
+                result = dict(metadata[audio_name])
+                path = os.path.join(local_extracted_archive, "train", path) if local_extracted_archive else path
+                result["audio"] = {"path": path, "bytes":f.read()}
+                yield id_, result
+                id_ +=1