add train, validation, even and odd splits

libriheavy.py

@@ -24,6 +24,8 @@ _CITATION = """\
 }
 """
 
+PATH = "./medium_data"
+
 class LibriheavyConfig(datasets.BuilderConfig):
     """BuilderConfig for Libriheavy."""
 
@@ -69,46 +71,82 @@ class Libriheavy(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         # first, we load speaker_list.json
-        speaker_list = "medium_data/speaker_list.json"
+        speaker_list = f"{PATH}/speaker_list.json"
         speaker_list = dl_manager.download_and_extract(speaker_list)
         with open(speaker_list, "r") as f:
             speaker_list = json.load(f)
         # now we load the individual speaker metadata
         speaker_metadata = {}
         for speaker_id, metadata_path in speaker_list.items():
-            metadata_path = f"medium_data/{metadata_path}"
+            metadata_path = f"{PATH}/{speaker_id}/{metadata_path}"
             metadata_path = dl_manager.download_and_extract(metadata_path)
             with open(metadata_path, "r") as f:
                 speaker_metadata[speaker_id] = json.load(f)
         speaker_chunks = []
+        even_speaker_chunks = []
+        odd_speaker_chunks = []
         for speaker_id, metadata in speaker_metadata.items():
             for chunk_id, chunk in metadata["chunks"].items():
-                speaker_chunks.append(
-                    {
-                        "speaker_id": speaker_id,
-                        "id": f"{speaker_id}_{chunk_id}",
-                        "audio": dl_manager.download(f"medium_data/{chunk['npz'].replace('.gz', '')}"),
-                        "text": dl_manager.download(f"medium_data/{chunk['json']}"),
-                    }
-                )
+                chunk_dict = {
+                    "speaker_id": speaker_id,
+                    "id": f"{speaker_id}_{chunk_id}",
+                    "audio": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['npz'].replace('.gz', '')}"),
+                    "text": dl_manager.download(f"{PATH}/{speaker_id}/{chunk['json']}"),
+                }
+                speaker_chunks.append(chunk_dict)
+                if int(chunk_id) % 2 == 0:
+                    even_speaker_chunks.append(chunk_dict)
+                else:
+                    odd_speaker_chunks.append(chunk_dict)
         # shuffle the chunks
         np.random.seed(42)
         np.random.shuffle(speaker_chunks)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"speaker_chunks": speaker_chunks},
-            )
+                name="train",
+                gen_kwargs={"speaker_chunks": speaker_chunks, "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name="validation",
+                gen_kwargs={"speaker_chunks": speaker_chunks, "split": "validation"}
+            ),
+            datasets.SplitGenerator(
+                name="even",
+                gen_kwargs={"speaker_chunks": even_speaker_chunks, "split": "even"}
+            ),
+            datasets.SplitGenerator(
+                name="odd",
+                gen_kwargs={"speaker_chunks": odd_speaker_chunks, "split": "odd"}
+            ),
         ]
 
-    def _generate_examples(self, speaker_chunks):
+    def _generate_examples(self, speaker_chunks, split):
         """Yields examples."""
         for chunk in speaker_chunks:
             npz = dict(np.load(chunk["audio"], allow_pickle=True))
             utterances = npz.keys()
             with gzip.open(chunk["text"], "rt") as f:
                 text = json.load(f)
-            for utterance_id, utterance in text.items():
+            if split in ["train", "even", "odd"]:
+                for utterance_id, utterance in text.items():
+                    # skip the last utterance
+                    if utterance_id == sorted(list(text.keys()))[-1]:
+                        continue
+                    result =  {
+                        "id": chunk["speaker_id"] + "_" + utterance_id,
+                        "speaker_id": chunk["speaker_id"],
+                        "audio": chunk["audio"],
+                        "text": chunk["text"],
+                        "word_segments": [
+                            {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"]
+                        ],
+                        "mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0],
+                    }
+                    yield chunk["speaker_id"] + "_" + utterance_id, result
+            else:
+                # only use the last utterance
+                utterance_id = sorted(list(text.keys()))[-1]
+                utterance = text[utterance_id]
                 result =  {
                     "id": chunk["speaker_id"] + "_" + utterance_id,
                     "speaker_id": chunk["speaker_id"],