fix failed

nb_samtale.py

@@ -17,11 +17,15 @@
 
 
 from collections import defaultdict
+from email.mime import audio
 from email.policy import default
 from importlib import metadata
+import io
 import json
 import os
 from re import split
+import tarfile
+from typing import List
 
 from huggingface_hub import hf_hub_url
 import datasets
@@ -61,6 +65,16 @@ _DATA_URL= "https://huggingface.co/datasets/Sprakbanken/nb_samtale/resolve/main/
 #    "test": ["test_bm_1.tar.gz", "test_nn_1.tar.gz"],
 #}
 
+
+def normalize_transcription(transcription: str, config="annotations"):
+    """Normalize transcriptions according to orthographic standards, or verbatim."""
+    # TODO: Implement normalization
+    if config == "orthographic":
+        return transcription
+    elif config == "verbatim":
+        return transcription
+    return transcription
+
 class NBSamtaleConfig(datasets.BuilderConfig):
     """BuilderConfig for NBSamtale"""
 
@@ -134,72 +148,56 @@ class NBSamtale(datasets.GeneratorBasedBuilder):
         )
 
 
-    def _split_generators(self, dl_manager) -> datasets.SplitGenerator:
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         """Download data and extract to datasets.Splits"""
         dl_manager.download_config.ignore_url_params = True
         audio_path = {}
-        local_extracted_archive = {}
-        metadata_path = {}
         split_type = {
             "train": datasets.Split.TRAIN,
             "test": datasets.Split.TEST,
             "dev": datasets.Split.VALIDATION,
             }
         for split in split_type:
-            audio_path[split] = dl_manager.download([f"{_DATA_URL}/{split}_{lang}_1.tar.gz" for lang in ["bm", "nn"]])
-            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
-            metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/{split}_metadata.jsonl")
+            #audio_path[split] = dl_manager.download([f"data/{split}_{lang}_1.tar.gz" for lang in ["bm", "nn"]])
+            audio_path[split] = dl_manager.download(f"data/{split}_bm_1.tar.gz")
 
         return [
             datasets.SplitGenerator(
-            name=dsplit,
+            name=split_type[split],
             gen_kwargs={
-                "local_extracted_archive": local_extracted_archive[split],
-                "audio_files": dl_manager.iter_archive(audio_path[split]),
-                "metadata": metadata_path[split],
+                "local_extracted_archive": dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None,
+                "audio_files": dl_manager.iter_archive(audio_path[split]),#[dl_manager.iter_archive(archive) for archive in audio_path[split]],
+                "metadata": dl_manager.download_and_extract(f"data/{split}_metadata.jsonl"),
                 }
-            ) for split, dsplit in split_type.items()
+            ) for split in split_type
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, local_extracted_archive, audio_files, metadata):
         """Loads the data files and extract the features."""
-        data_fields = list(self._info().features.keys())
+        #data_fields = list(self._info().features.keys())
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         meta = {}
         with open(metadata, encoding="utf-8") as mf:
-            for row in mf.read().splitlines():
+            datalines = mf.read().splitlines()
+            for row in datalines:
                 data = json.loads(row)
                 audio_path = data["file_name"]
-                # if data is incomplete, fill with empty values
                 data["transcription"] = normalize_transcription(data["transcription"], config=self.config.name)
-                for field in data_fields:
-                    if field not in data:
-                        data[field] = ""
-
                 meta[audio_path] = data
 
         id_ = 0
-        for archive in audio_files:
-            for path, audiofile in archive:
-                if path in meta:
-                    result = dict(meta[path])
-                    # set the audio feature and the path to the extracted file
-                    path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
-                    result["audio"] = {"path": path, "bytes": audiofile.read()}
-                    result["path"] = path
-                    yield id_, result
-                    id_ += 1
-                else:
-                    print(f"Missing metadata for {path}")
-
 
-def normalize_transcription(transcription: str, config="annotations"):
-    """Normalize transcriptions according to orthographic standards, or verbatim."""
-    # TODO: Implement normalization
-    if config == "orthographic":
-        return transcription
-    elif config == "verbatim":
-        return transcription
-    return transcription
+        #for archive in audio_files:
+        for path, audio_file in audio_files:
+            if not path in meta:
+                print(f"{path} not in metadata")
+            else:
+                result = dict(meta[path])
+                # set the audio feature and the path to the extracted file
+                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
+                result["audio"] = {"path": path, "bytes": audio_file.read()}
+                result["path"] = path
+                yield id_, result
+                id_ += 1