Create tokenizer_base.py

tokenizer_base.py

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+from abc import ABC, abstractmethod
+from itertools import groupby
+from typing import List, Optional, Tuple
+
+import torch
+from torch import Tensor
+from torch.nn.utils.rnn import pad_sequence
+
+
+class CharsetAdapter:
+    """Transforms labels according to the target charset."""
+
+    def __init__(self, target_charset) -> None:
+        super().__init__()
+        self.charset = target_charset
+        self.lowercase_only = target_charset == target_charset.lower()
+        self.uppercase_only = target_charset == target_charset.upper()
+
+    def __call__(self, label):
+        if self.lowercase_only:
+            label = label.lower()
+        elif self.uppercase_only:
+            label = label.upper()
+        return label
+
+
+class BaseTokenizer(ABC):
+
+    def __init__(
+        self, charset: str, specials_first: tuple = (), specials_last: tuple = ()
+    ) -> None:
+        self._itos = specials_first + tuple(charset + "[UNK]") + specials_last
+        self._stoi = {s: i for i, s in enumerate(self._itos)}
+
+    def __len__(self):
+        return len(self._itos)
+
+    def _tok2ids(self, tokens: str) -> List[int]:
+        return [self._stoi[s] for s in tokens]
+
+    def _ids2tok(self, token_ids: List[int], join: bool = True) -> str:
+        tokens = [self._itos[i] for i in token_ids]
+        return "".join(tokens) if join else tokens
+
+    @abstractmethod
+    def encode(
+        self, labels: List[str], device: Optional[torch.device] = None
+    ) -> Tensor:
+        """Encode a batch of labels to a representation suitable for the model.
+
+        Args:
+            labels: List of labels. Each can be of arbitrary length.
+            device: Create tensor on this device.
+
+        Returns:
+            Batched tensor representation padded to the max label length. Shape: N, L
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
+        """Internal method which performs the necessary filtering prior to decoding."""
+        raise NotImplementedError
+
+    def decode(
+        self, token_dists: Tensor, raw: bool = False
+    ) -> Tuple[List[str], List[Tensor]]:
+        """Decode a batch of token distributions.
+
+        Args:
+            token_dists: softmax probabilities over the token distribution. Shape: N, L, C
+            raw: return unprocessed labels (will return list of list of strings)
+
+        Returns:
+            list of string labels (arbitrary length) and
+            their corresponding sequence probabilities as a list of Tensors
+        """
+        batch_tokens = []
+        batch_probs = []
+        for dist in token_dists:
+            probs, ids = dist.max(-1)
+            if not raw:
+                probs, ids = self._filter(probs, ids)
+            tokens = self._ids2tok(ids, not raw)
+            batch_tokens.append(tokens)
+            batch_probs.append(probs)
+        return batch_tokens, batch_probs
+
+
+class Tokenizer(BaseTokenizer):
+    BOS = "[B]"
+    EOS = "[E]"
+    PAD = "[P]"
+
+    def __init__(self, charset: str) -> None:
+        specials_first = (self.EOS,)
+        specials_last = (self.BOS, self.PAD)
+        super().__init__(charset, specials_first, specials_last)
+        self.eos_id, self.bos_id, self.pad_id = [
+            self._stoi[s] for s in specials_first + specials_last
+        ]
+
+    def encode(
+        self, labels: List[str], device: Optional[torch.device] = None
+    ) -> Tensor:
+        batch = [
+            torch.as_tensor(
+                [self.bos_id] + self._tok2ids(y) + [self.eos_id],
+                dtype=torch.long,
+                device=device,
+            )
+            for y in labels
+        ]
+        return pad_sequence(batch, batch_first=True, padding_value=self.pad_id)
+
+    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
+        ids = ids.tolist()
+        try:
+            eos_idx = ids.index(self.eos_id)
+        except ValueError:
+            eos_idx = len(ids)
+        ids = ids[:eos_idx]
+        probs = probs[: eos_idx + 1]
+        return probs, ids
+
+
+class CTCTokenizer(BaseTokenizer):
+    BLANK = "[B]"
+
+    def __init__(self, charset: str) -> None:
+        super().__init__(charset, specials_first=(self.BLANK,))
+        self.blank_id = self._stoi[self.BLANK]
+
+    def encode(
+        self, labels: List[str], device: Optional[torch.device] = None
+    ) -> Tensor:
+        batch = [
+            torch.as_tensor(self._tok2ids(y), dtype=torch.long, device=device)
+            for y in labels
+        ]
+        return pad_sequence(batch, batch_first=True, padding_value=self.blank_id)
+
+    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
+        ids = list(zip(*groupby(ids.tolist())))[0]
+        ids = [x for x in ids if x != self.blank_id]
+        return probs, ids