tokenization_wav2vec2_phoneme.py

# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for Wav2Vec2Phoneme."""

import json
import os
import sys
from dataclasses import dataclass
from itertools import groupby
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import numpy as np

from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list
from ...tokenization_utils_base import AddedToken
from ...utils import (
    ModelOutput,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    logging,
    requires_backends,
    to_py_obj,
)


logger = logging.get_logger(__name__)


if TYPE_CHECKING:
    if is_torch_available():
        import torch
    if is_tf_available():
        import tensorflow as tf
    if is_flax_available():
        import jax.numpy as jnp  # noqa: F401


VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/wav2vec2-lv-60-espeak-cv-ft": (
            "https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/wav2vec2-lv-60-espeak-cv-ft": (
            "https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft/resolve/main/tokenizer_config.json"
        ),
    },
}

# Wav2Vec2Phoneme has no max input length
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-lv-60-espeak-cv-ft": sys.maxsize}


ListOfDict = List[Dict[str, Union[int, str]]]


@dataclass
class Wav2Vec2PhonemeCTCTokenizerOutput(ModelOutput):
    """
    Output type of [` Wav2Vec2PhonemeCTCTokenizer`], with transcription.

    Args:
        text (list of `str` or `str`):
            Decoded logits in text from. Usually the speech transcription.
        char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`):
            Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char
            offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with
            produced text.
    """

    text: Union[List[str], str]
    char_offsets: Union[List[ListOfDict], ListOfDict] = None


class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):

    """
    Constructs a Wav2Vec2PhonemeCTC tokenizer.

    This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
    the superclass for more information regarding such methods.

    Args:
        vocab_file (`str`):
            File containing the vocabulary.
        bos_token (`str`, *optional*, defaults to `"<s>"`):
            The beginning of sentence token.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sentence token.
        unk_token (`str`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        do_phonemize (`bool`, *optional*, defaults to `True`):
            Whether the tokenizer should phonetize the input or not. Only if a sequence of phonemes is passed to the
            tokenizer, `do_phonemize` should be set to `False`.
        phonemizer_lang (`str`, *optional*, defaults to `"en-us"`):
            The language of the phoneme set to which the tokenizer should phonetize the input text to.
        phonemizer_backend (`str`, *optional*. defaults to `"espeak"`):
            The backend phonetization library that shall be used by the phonemizer library. Defaults to `espeak-ng`.
            See the [phonemizer package](https://github.com/bootphon/phonemizer#readme). for more information.

        **kwargs
            Additional keyword arguments passed along to [`PreTrainedTokenizer`]
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        phone_delimiter_token=" ",
        word_delimiter_token=None,
        do_phonemize=True,
        phonemizer_lang="en-us",
        phonemizer_backend="espeak",
        **kwargs,
    ):
        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            word_delimiter_token=word_delimiter_token,
            phone_delimiter_token=phone_delimiter_token,
            do_phonemize=do_phonemize,
            phonemizer_lang=phonemizer_lang,
            phonemizer_backend=phonemizer_backend,
            **kwargs,
        )

        self._word_delimiter_token = word_delimiter_token
        self._phone_delimiter_token = phone_delimiter_token
        self.do_phonemize = do_phonemize
        self.phonemizer_lang = phonemizer_lang
        self.phonemizer_backend = phonemizer_backend

        if do_phonemize:
            self.init_backend(self.phonemizer_lang)

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}

    @property
    def vocab_size(self) -> int:
        return len(self.decoder)

    def get_vocab(self) -> Dict:
        return dict(self.encoder, **self.added_tokens_encoder)

    def init_backend(self, phonemizer_lang: str):
        """
        Initializes the backend.

        Args:
            phonemizer_lang (`str`): The language to be used.
        """
        requires_backends(self, "phonemizer")
        from phonemizer.backend import BACKENDS

        self.backend = BACKENDS[self.phonemizer_backend](phonemizer_lang, language_switch="remove-flags")

    def prepare_for_tokenization(
        self,
        text: str,
        is_split_into_words: bool = False,
        phonemizer_lang: Optional[str] = None,
        do_phonemize: Optional[bool] = None,
    ) -> Tuple[str, Dict[str, Any]]:
        """
        Performs any necessary transformations before tokenization.

        This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
        `kwargs` at the end of the encoding process to be sure all the arguments have been used.

        Args:
            text (`str`):
                The text to prepare.
            is_split_into_words (`bool`, *optional*, defaults to `False`):
                Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
                tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
                which it will tokenize. This is useful for NER or token classification.
            phonemizer_lang (`str`, *optional*):
                The language of the phoneme set to which the tokenizer should phonetize the input text to.
            do_phonemize (`bool`, *optional*):
                Whether the tokenizer should phonetize the input text or not. Only if a sequence of phonemes is passed
                to the tokenizer, `do_phonemize` should be set to `False`.


        Returns:
            `Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
        """
        if is_split_into_words:
            text = " " + text

        # set whether tokenizer should phonemize or not
        if do_phonemize is not None:
            self.do_phonemize = do_phonemize

        # set the correct phonemizer language
        if phonemizer_lang is not None:
            self.phonemizer_lang = phonemizer_lang
            self.init_backend(phonemizer_lang)

        return (text, {})

    def _tokenize(self, text, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer.
        """

        # make sure whitespace is stripped to prevent <unk>
        text = text.strip()

        # phonemize
        if self.do_phonemize:
            text = text.lower()

            # create list of phonemes
            text = self.phonemize(text, self.phonemizer_lang)

        # make sure ' ' is between phonemes
        tokens = text.split(" ")

        tokens = list(filter(lambda p: p.strip() != "", tokens))
        return tokens

    def phonemize(self, text: str, phonemizer_lang: Optional[str] = None) -> str:
        from phonemizer.separator import Separator

        word_delimiter = self.word_delimiter_token + " " if self.word_delimiter_token is not None else ""
        if phonemizer_lang is not None and phonemizer_lang != self.phonemizer_lang:
            self.init_backend(phonemizer_lang)
        else:
            phonemizer_lang = self.phonemizer_lang

        separator = Separator(phone=self.phone_delimiter_token, word=word_delimiter, syllable="")
        phonemes = self.backend.phonemize(
            [text],
            separator=separator,
        )
        phonemes = phonemes[0].strip()

        return phonemes

    @property
    def word_delimiter_token(self) -> str:
        """
        `str`: Word delimiter token. Log an error if used while not having been set.
        """
        if self._word_delimiter_token is None and self.verbose:
            return None
        return str(self._word_delimiter_token)

    @property
    def word_delimiter_token_id(self) -> Optional[int]:
        """
        `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been
        set.
        """
        if self._word_delimiter_token is None:
            return None
        return self.convert_tokens_to_ids(self.word_delimiter_token)

    @word_delimiter_token.setter
    def word_delimiter_token(self, value):
        self._word_delimiter_token = value

    @word_delimiter_token_id.setter
    def word_delimiter_token_id(self, value):
        self._word_delimiter_token = self.convert_tokens_to_ids(value)

    @property
    def phone_delimiter_token(self) -> str:
        """
        `str`: Word delimiter token. Log an error if used while not having been set.
        """
        if self._phone_delimiter_token is None and self.verbose:
            logger.error("Using phone_delimiter_token, but it is not set yet.")
            return None
        return str(self._phone_delimiter_token)

    @property
    def phone_delimiter_token_id(self) -> Optional[int]:
        """
        `Optional[int]`: Id of the phone_delimiter_token in the vocabulary. Returns `None` if the token has not been
        set.
        """
        if self._phone_delimiter_token is None:
            return None
        return self.convert_tokens_to_ids(self.phone_delimiter_token)

    @phone_delimiter_token.setter
    def phone_delimiter_token(self, value):
        self._phone_delimiter_token = value

    @phone_delimiter_token_id.setter
    def phone_delimiter_token_id(self, value):
        self._phone_delimiter_token = self.convert_tokens_to_ids(value)

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (str) in an index (integer) using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the vocab."""
        result = self.decoder.get(index, self.unk_token)
        return result

    def convert_tokens_to_string(
        self,
        tokens: List[str],
        group_tokens: bool = True,
        spaces_between_special_tokens: bool = False,
        filter_word_delimiter_token: bool = True,
        output_char_offsets: bool = False,
    ) -> str:
        """
        Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
        """
        # group same tokens into non-repeating tokens in CTC style decoding
        if group_tokens:
            chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens)))
        else:
            chars = tokens
            char_repetitions = len(tokens) * [1]

        # filter self.pad_token which is used as CTC-blank token
        processed_chars = list(filter(lambda char: char != self.pad_token, chars))

        # also filter self.word_delimiter_token if not not
        if filter_word_delimiter_token and self.word_delimiter_token is not None:
            processed_chars = list(filter(lambda token: token != self.word_delimiter_token, processed_chars))

        # retrieve offsets
        char_offsets = None
        if output_char_offsets:
            word_delimiter_token_for_offsets = (
                self.word_delimiter_token if filter_word_delimiter_token is True else None
            )
            char_offsets = self._compute_offsets(
                char_repetitions, chars, self.pad_token, word_delimiter_token=word_delimiter_token_for_offsets
            )

            if len(char_offsets) != len(processed_chars):
                raise ValueError(
                    f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}"
                    " have to be of the same length, but are: `len(offsets)`: "
                    f"{len(char_offsets)} and `len(processed_tokens)`: {len(processed_chars)}"
                )

            # set tokens to correct processed token
            for i, char in enumerate(processed_chars):
                char_offsets[i]["char"] = char

        string = " ".join(processed_chars).strip()

        return {"text": string, "char_offsets": char_offsets}

    @staticmethod
    def _compute_offsets(
        char_repetitions: List[int], chars: List[str], ctc_token: int, word_delimiter_token: Optional[int] = None
    ) -> List[Dict[str, Union[str, int]]]:
        end_indices = np.asarray(char_repetitions).cumsum()
        start_indices = np.concatenate(([0], end_indices[:-1]))

        offsets = [
            {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices)
        ]

        # filter out CTC token
        offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets))

        # filter out word delimiter token if necessary
        if word_delimiter_token is not None:
            offsets = list(filter(lambda offsets: offsets["char"] != word_delimiter_token, offsets))

        return offsets

    def _decode(
        self,
        token_ids: List[int],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        group_tokens: bool = True,
        filter_word_delimiter_token: bool = True,
        spaces_between_special_tokens: bool = False,
        output_char_offsets: bool = False,
    ) -> str:
        """
        special _decode function is needed for Wav2Vec2PhonemeTokenizer because added tokens should be treated exactly
        the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be
        called on the whole token list and not individually on added tokens
        """
        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)

        result = []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            result.append(token)

        string_output = self.convert_tokens_to_string(
            result,
            group_tokens=group_tokens,
            spaces_between_special_tokens=spaces_between_special_tokens,
            filter_word_delimiter_token=filter_word_delimiter_token,
            output_char_offsets=output_char_offsets,
        )

        text = string_output["text"]

        clean_up_tokenization_spaces = (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
            text = self.clean_up_tokenization(text)

        if output_char_offsets:
            return Wav2Vec2PhonemeCTCTokenizerOutput(text=text, char_offsets=string_output["char_offsets"])
        else:
            return text

    # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` here
    def decode(
        self,
        token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        output_char_offsets: bool = False,
        **kwargs,
    ) -> str:
        """
        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
        tokens and clean up tokenization spaces.

        Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.

        Args:
            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*):
                Whether or not to clean up the tokenization spaces.
            output_char_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output character offsets. Character offsets can be used in combination with the
                sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.

                <Tip>

                Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
                understand how to make use of `output_word_offsets`.
                [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works the same way with
                phonemes.

                </Tip>

            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `str` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded
            sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]
            when `output_char_offsets == True`.
        """
        # Convert inputs to python lists
        token_ids = to_py_obj(token_ids)

        return self._decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            output_char_offsets=output_char_offsets,
            **kwargs,
        )

    # overwritten from `tokenization_utils_base.py` because tokenizer can output
    # `ModelOutput` which should not be a list for batched output and because
    # we need docs for `output_char_offsets` here
    def batch_decode(
        self,
        sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        output_char_offsets: bool = False,
        **kwargs,
    ) -> List[str]:
        """
        Convert a list of lists of token ids into a list of strings by calling decode.

        Args:
            sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*):
                Whether or not to clean up the tokenization spaces.
            output_char_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output character offsets. Character offsets can be used in combination with the
                sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.

                <Tip>

                Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
                understand how to make use of `output_word_offsets`.
                [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works analogous with phonemes
                and batched output.

                </Tip>

            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The
            decoded sentence. Will be a
            [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`] when
            `output_char_offsets == True`.
        """
        batch_decoded = [
            self.decode(
                seq,
                skip_special_tokens=skip_special_tokens,
                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                output_char_offsets=output_char_offsets,
                **kwargs,
            )
            for seq in sequences
        ]
        if output_char_offsets:
            # transform list of dicts to dict of lists
            return Wav2Vec2PhonemeCTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]})

        return batch_decoded

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")

        return (vocab_file,)

    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
        """
        Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
        it with indices starting from length of the current vocabulary.

        Args:
            new_tokens (`List[str]`or `List[tokenizers.AddedToken]`):
                Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by
                checking if the tokenizer assign the index of the `unk_token` to them).
            special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the tokens should be added as special tokens.

        Returns:
            `int`: The number of tokens actually added to the vocabulary.

        Examples:

        ```python
        # Let's see how to increase the vocabulary of Bert model and tokenizer
        tokenizer = Wav2Vec2PhonemeCTCTokenizer.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
        model = Wav2Vec2PhonemeForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")

        num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"])
        print("We have added", num_added_toks, "tokens")
        # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
        model.resize_token_embeddings(len(tokenizer))
        ```"""
        new_tokens = [str(tok) for tok in new_tokens]

        tokens_to_add = []
        for token in new_tokens:
            if not isinstance(token, str):
                raise ValueError(f"Token {token} has to be of type string, but is of type {type(token)}.")
            assert isinstance(token, str)
            if (
                token != self.unk_token
                and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
                and token not in tokens_to_add
            ):
                tokens_to_add.append(token)
                if self.verbose:
                    logger.info(f"Adding {token} to the vocabulary")

        added_tok_encoder = {tok: len(self) + i for i, tok in enumerate(tokens_to_add)}
        added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
        self.added_tokens_encoder.update(added_tok_encoder)
        self.added_tokens_decoder.update(added_tok_decoder)

        # Make sure we don't split on any special tokens (even they were already in the vocab before)
        for token in tokens_to_add:
            if len(token) > 1:
                self._additional_special_tokens.append(AddedToken(token))
                _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token)

        self._create_trie(self.unique_no_split_tokens)

        return len(tokens_to_add)