Add monkey patched fairseq package to run on python 3.11 (what is needed for our use of RVC at least)

modules/voice_conversion/fairseq/data/subsample_dataset.py

@@ -0,0 +1,72 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+import numpy as np
+
+from . import BaseWrapperDataset
+
+
+logger = logging.getLogger(__name__)
+
+
+class SubsampleDataset(BaseWrapperDataset):
+    """Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples
+
+    Args:
+        dataset (~torch.utils.data.Dataset): dataset to subsample
+        size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive)
+    """
+
+    def __init__(self, dataset, size_ratio, shuffle=False):
+        super().__init__(dataset)
+        assert size_ratio < 1
+        self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int)
+        self.indices = np.random.choice(
+            list(range(len(self.dataset))), self.actual_size, replace=False
+        )
+        self.shuffle = shuffle
+        logger.info(
+            "subsampled dataset from {} to {} (ratio={})".format(
+                len(self.dataset), self.actual_size, size_ratio
+            )
+        )
+
+    def __getitem__(self, index):
+        return self.dataset[self.indices[index]]
+
+    def __len__(self):
+        return self.actual_size
+
+    def collater(self, samples):
+        return self.dataset.collater(samples)
+
+    @property
+    def sizes(self):
+        return self.dataset.sizes[self.indices]
+
+    @property
+    def name(self):
+        return self.dataset.name
+
+    def num_tokens(self, index):
+        return self.dataset.num_tokens(self.indices[index])
+
+    def size(self, index):
+        return self.dataset.size(self.indices[index])
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.shuffle:
+            order = [np.random.permutation(len(self))]
+        else:
+            order = [np.arange(len(self))]
+        order.append(self.sizes)
+        return np.lexsort(order)
+
+    def prefetch(self, indices):
+        self.dataset.prefetch(self.indices[indices])