finish

ami-ihm-kaldi-chunked.py

@@ -280,10 +280,6 @@ class AMIConfig(datasets.BuilderConfig):
     def __init__(self, name, *args, **kwargs):
         """BuilderConfig for AMI"""
         super().__init__(name=name, *args, **kwargs)
-        if name not in {"dev", "test"}:
-            self.subsets_to_download = _SUBSETS[: _SUBSETS.index(name) + 1]
-        else:
-            self.subsets_to_download = (name,)
 
 
 class AMI(datasets.GeneratorBasedBuilder):
@@ -331,9 +327,9 @@ class AMI(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        train_audio_files = [_AUDIO_ARCHIVE_URL.format(subset=self.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS]
-        dev_audio_files = [_AUDIO_ARCHIVE_URL.format(subset=self.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS]
-        eval_audio_files = [_AUDIO_ARCHIVE_URL.format(subset=self.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS]
+        train_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS}
+        dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS}
+        eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS}
 
         train_audio_archives = dl_manager.download_and_extract(train_audio_files)
         dev_audio_archives = dl_manager.download_and_extract(dev_audio_files)
@@ -343,61 +339,44 @@ class AMI(datasets.GeneratorBasedBuilder):
         dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev"))
         eval_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="eval"))
 
-        import ipdb; ipdb.set_trace()
-
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation},
+                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation, "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation},
+                gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation, "split": "dev"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation},
+                gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation, "split": "eval"},
             ),
         ]
 
-    def _generate_examples(self, audio, annotation):
-        import ipdb; ipdb.set_trace()
-#        assert len(audio_archives_iterators) == len(meta_paths)
-#        if local_audio_archives_paths:
-#            assert len(audio_archives_iterators) == len(local_audio_archives_paths)
-#
-#        for i, (meta_path, audio_archive_iterator) in enumerate(
-#            zip(meta_paths, audio_archives_iterators)
-#        ):
-#            meta_dict = dict()
-#            with open(meta_path) as csvfile:
-#                meta_csv = csv.DictReader(csvfile)
-#                for line in meta_csv:
-#                    meta_dict[line["sid"]] = line
-#
-#            for audio_path_in_archive, audio_file in audio_archive_iterator:
-                # `audio_path_in_archive` is like "dev_chunks_0000/YOU1000000029_S0000095.wav"
-#                audio_filename = os.path.split(audio_path_in_archive)[1]
-#                audio_id = audio_filename.split(".wav")[0]
-#                audio_meta = meta_dict[audio_id]
-#                audio_meta["segment_id"] = audio_meta.pop("sid")
-#                audio_meta["original_full_path"] = audio_meta.pop("path")
-#                audio_meta["text"] = audio_meta.pop("text_tn")
-#                audio_meta["audio_id"] = audio_meta.pop("aid")
-#                if not audio_meta["category"]:
-#                    audio_meta["category"] = "N/A"
-#
-#                path = (
-#                    os.path.join(local_audio_archives_paths[i], audio_path_in_archive)
-#                    if local_audio_archives_paths
-#                    else audio_path_in_archive
-#                )
+    def _generate_examples(self, audio, annotation, split):
+        # open annotation file
+        with open(annotation, "r", encoding="utf-8") as f:
+            transcriptions = {}
+            for line in f.readlines():
+                line_items = line.strip().split()
+                _id = line_items[0]
+                text = " ".join(line_items[1:])
+                _, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
+
+                transcriptions[_id] = {
+                    "audio_id": _id,
+                    "segment_id": segment_id,
+                    "text": text,
+                    "begin_time": int(begin_time) / 100,
+                    "end_time": int(end_time) / 100,
+                    "microphone_id": microphone_id,
+                    "speaker_id": speaker_id,
+                }
 
-#                yield audio_id, {
-#                    "audio": {"path": path, "bytes": audio_file.read()},
-#                    **{
-#                        feature: value
-#                        for feature, value in audio_meta.items()
-#                        if feature in self.info.features
-#                    },
-#                }
+        for _audio_id, (transcription_id, result) in enumerate(transcriptions.items()):
+            folder_id = result["segment_id"]
+            file_name = "_".join([split, transcription_id.lower()]) + ".wav"
+            audio_file = os.path.join(audio[folder_id], folder_id, file_name)
+            result["audio"] = audio_file
+            yield _audio_id, result