Init

X-LANCE · Oct 7, 2023 · 73537de · 73537de
1 parent df6b2f4
commit 73537de
Show file tree

Hide file tree

Showing 22 changed files with 27,782 additions and 1 deletion.
diff --git a/.gitignore b/.gitignore
@@ -149,4 +149,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
diff --git a/data_collate.py b/data_collate.py
@@ -0,0 +1,196 @@
+import os.path
+import random
+import numpy as np
+import torch
+import re
+import torch.utils.data
+
+import kaldiio
+from tqdm import tqdm
+
+
+class BaseCollate:
+    def __init__(self, n_frames_per_step=1):
+        self.n_frames_per_step = n_frames_per_step
+
+    def collate_text_mel(self, batch: [dict]):
+        """
+        :param batch: list of dicts
+        This function sorts batch elements by its length and concatenate all batch elements into pytorch tensors
+        """
+        contains_noise = (batch[0]['noise'] is not None)
+        utt = list(map(lambda x: x['utt'], batch))
+        input_lengths, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([len(x['phn_ids']) for x in batch]),
+            dim=0, descending=True)
+        max_input_len = input_lengths[0]
+
+        text_padded = torch.LongTensor(len(batch), max_input_len)
+        text_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            text = batch[ids_sorted_decreasing[i]]['phn_ids']
+            text_padded[i, :text.size(0)] = text
+
+        # Right zero-pad mel-spec
+        num_mels = batch[0]['mel'].size(0)
+        max_target_len = max([x['mel'].size(1) for x in batch])
+        if max_target_len % self.n_frames_per_step != 0:
+            max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
+            assert max_target_len % self.n_frames_per_step == 0
+
+        # include mel padded
+        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
+        mel_padded.zero_()
+        noise_padded = torch.zeros_like(mel_padded)
+        output_lengths = torch.LongTensor(len(batch))
+        for i in range(len(ids_sorted_decreasing)):
+            mel = batch[ids_sorted_decreasing[i]]['mel']
+            mel_padded[i, :, :mel.size(1)] = mel
+
+            output_lengths[i] = mel.size(1)
+            if contains_noise:
+                noise = batch[ids_sorted_decreasing[i]]['noise']
+                noise_padded[i, :, :mel.size(1)] = noise
+
+        dur_padded = torch.LongTensor(len(batch), max_input_len)
+        dur_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            dur = batch[ids_sorted_decreasing[i]]['dur']
+            dur_padded[i, :dur.size(0)] = dur
+
+        utt_name = np.array(utt)[ids_sorted_decreasing].tolist()
+        if isinstance(utt_name, str):
+            utt_name = [utt_name]
+
+        res = {
+            "utt": utt_name,
+            "text_padded": text_padded,
+            "input_lengths": input_lengths,
+            "mel_padded": mel_padded,
+            "noise_padded": noise_padded if contains_noise else None,
+            "output_lengths": output_lengths,
+            "dur_padded": dur_padded
+        }
+        return res, ids_sorted_decreasing
+
+
+class SpkIDCollate(BaseCollate):
+    def __call__(self, batch, *args, **kwargs):
+        base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
+        spk_ids = torch.LongTensor(list(map(lambda x: x["spk_ids"], batch)))
+        spk_ids = spk_ids[ids_sorted_decreasing]
+        base_data.update({
+            "spk_ids": spk_ids
+        })
+        return base_data
+
+
+class XvectorCollate(BaseCollate):
+    def __call__(self, batch, *args, **kwargs):
+        base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
+        xvectors = torch.cat(list(map(lambda x: x["xvector"].unsqueeze(0), batch)), dim=0)
+        xvectors = xvectors[ids_sorted_decreasing]
+        base_data.update({
+            "xvector": xvectors
+        })
+        return base_data
+
+
+class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
+    """
+    Maintain similar input lengths in a batch.
+    Length groups are specified by boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
+
+    It removes samples which are not included in the boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
+    """
+
+    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+        self.lengths = dataset.lengths
+        self.batch_size = batch_size
+        self.boundaries = boundaries
+
+        self.buckets, self.num_samples_per_bucket = self._create_buckets()
+        self.total_size = sum(self.num_samples_per_bucket)
+        self.num_samples = self.total_size // self.num_replicas
+
+    def _create_buckets(self):
+        buckets = [[] for _ in range(len(self.boundaries) - 1)]
+        for i in range(len(self.lengths)):
+            length = self.lengths[i]
+            idx_bucket = self._bisect(length)
+            if idx_bucket != -1:
+                buckets[idx_bucket].append(i)
+
+        for i in range(len(buckets) - 1, 0, -1):
+            if len(buckets[i]) == 0:
+                buckets.pop(i)
+                self.boundaries.pop(i + 1)
+
+        num_samples_per_bucket = []
+        for i in range(len(buckets)):
+            len_bucket = len(buckets[i])
+            total_batch_size = self.num_replicas * self.batch_size
+            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
+            num_samples_per_bucket.append(len_bucket + rem)
+        return buckets, num_samples_per_bucket
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+
+        indices = []
+        if self.shuffle:
+            for bucket in self.buckets:
+                indices.append(torch.randperm(len(bucket), generator=g).tolist())
+        else:
+            for bucket in self.buckets:
+                indices.append(list(range(len(bucket))))
+
+        batches = []
+        for i in range(len(self.buckets)):
+            bucket = self.buckets[i]
+            len_bucket = len(bucket)
+            ids_bucket = indices[i]
+            num_samples_bucket = self.num_samples_per_bucket[i]
+
+            # add extra samples to make it evenly divisible
+            rem = num_samples_bucket - len_bucket
+            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
+
+            # subsample
+            ids_bucket = ids_bucket[self.rank::self.num_replicas]
+
+            # batching
+            for j in range(len(ids_bucket) // self.batch_size):
+                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
+                batches.append(batch)
+
+        if self.shuffle:
+            batch_ids = torch.randperm(len(batches), generator=g).tolist()
+            batches = [batches[i] for i in batch_ids]
+        self.batches = batches
+
+        assert len(self.batches) * self.batch_size == self.num_samples
+        return iter(self.batches)
+
+    def _bisect(self, x, lo=0, hi=None):
+        if hi is None:
+            hi = len(self.boundaries) - 1
+
+        if hi > lo:
+            mid = (hi + lo) // 2
+            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
+                return mid
+            elif x <= self.boundaries[mid]:
+                return self._bisect(x, lo, mid)
+            else:
+                return self._bisect(x, mid + 1, hi)
+        else:
+            return -1
+
+    def __len__(self):
+        return self.num_samples // self.batch_size