Create text_speech_codes_v2.py

text_speech_codes_v2.py

@@ -0,0 +1,98 @@
+# Lint as: python3
+"""semantic and acoustic codes dataset with text.
+"""
+
+
+import glob
+import os
+
+import datasets
+import torch
+
+
+class TextSpeechCodesDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for Text-SpeechCodes dataset."""
+
+    def __init__(self, **kwargs):
+        super(TextSpeechCodesDatasetConfig, self).__init__(**kwargs)
+
+
+class TextSpeechCodesDataset(datasets.GeneratorBasedBuilder):
+    """Codes dataset."""
+
+    BUILDER_CONFIGS = [
+        TextSpeechCodesDatasetConfig(name="all", description="TextSpeechCodes dataset"),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return (
+            "Codes should be computed before using this dataset. "
+            "`datasets.load_dataset('/path/to/this/script', name=all, data_dir='path/to/folder/folder_name/of/codes')`"
+        )
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "length": datasets.Value("int32"),
+                "transcription": datasets.Value("string"),
+                "acoustic_tokens": datasets.Array2D(shape=(None, 12), dtype="int16"),
+                "semantic_tokens": datasets.Array2D(shape=(None, 1), dtype="int16"),
+                "transcription_bytes": datasets.Sequence(datasets.Value("uint8")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager):
+        base_data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        if not os.path.exists(base_data_dir):
+            raise FileNotFoundError(
+                f"{base_data_dir} does not exist. Make sure you insert a manual dir via "
+                f"`datasets.load_dataset('/this/script', data_dir=...)` "
+                f"that includes code files .pt files "
+                f"dataset. Manual download instructions: {self.manual_download_instructions}"
+            )
+
+        train_data_dirs = glob.glob(os.path.join(base_data_dir, "**", "*.pt"), recursive=True)
+        print(f"Found {len(train_data_dirs)} files")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dirs": train_data_dirs},
+            ),
+        ]
+
+    def _generate_examples(self, data_dirs):
+        for key, path in enumerate(data_dirs):
+            id_ = path.split("/")[-1].replace(".pt", "")
+
+            data = torch.load(path, map_location="cpu", weights_only=False)
+            for i, (k, v) in enumerate(data.items()):
+                acoustic_tokens = v["acoustic_codes"]
+                semantic_tokens = v["semantic_codes"]
+
+                if acoustic_tokens.ndim == 3:
+                    acoustic_tokens = acoustic_tokens.squeeze(0).transpose(0, 1)
+                else:
+                    acoustic_tokens = acoustic_tokens.transpose(0, 1)
+                if semantic_tokens.ndim == 2:
+                    semantic_tokens = semantic_tokens.transpose(0, 1)
+                else:
+                    semantic_tokens = semantic_tokens.unsqueeze(1)
+
+                transcription = v["transcription"]
+                transcription_bytes = list(transcription.encode("utf-8"))
+
+                yield f"{id_}_{i}", {
+                    "id": str(k),
+                    "length": semantic_tokens.shape[0] + len(transcription_bytes),
+                    "transcription": transcription,
+                    "transcription_bytes": transcription_bytes,
+                    "acoustic_tokens": acoustic_tokens,
+                    "semantic_tokens": semantic_tokens,
+                }