Time stamps for CTC models (#15687)

* [Wav2Vec2 Time Stamps]

* Add first version

* add word time stamps

* Fix

* save intermediate space

* improve

* [Finish CTC Tokenizer]

* remove @

* remove @

* push

* continue with phonemes

* up

* finish PR

* up

* add example

* rename

* finish

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* correct split

* finalize

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/wav2vec2.mdx

@@ -45,6 +45,8 @@ This model was contributed by [patrickvonplaten](https://huggingface.co/patrickv
 [[autodoc]] Wav2Vec2CTCTokenizer
     - __call__
     - save_vocabulary
+    - decode
+    - batch_decode
 
 ## Wav2Vec2FeatureExtractor
 

src/transformers/models/hubert/configuration_hubert.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ Hubert model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -248,3 +250,7 @@ class HubertConfig(PretrainedConfig):
         # ctc loss
         self.ctc_loss_reduction = ctc_loss_reduction
         self.ctc_zero_infinity = ctc_zero_infinity
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/sew/configuration_sew.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ SEW model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -243,3 +245,7 @@ class SEWConfig(PretrainedConfig):
         # sequence classification
         self.use_weighted_layer_sum = use_weighted_layer_sum
         self.classifier_proj_size = classifier_proj_size
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/sew_d/configuration_sew_d.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ SEW-D model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -279,3 +281,7 @@ class SEWDConfig(PretrainedConfig):
         # sequence classification
         self.use_weighted_layer_sum = use_weighted_layer_sum
         self.classifier_proj_size = classifier_proj_size
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/unispeech/configuration_unispeech.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ UniSpeech model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -289,3 +291,7 @@ class UniSpeechConfig(PretrainedConfig):
 
         # pretraining loss
         self.replace_prob = replace_prob
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/unispeech_sat/configuration_unispeech_sat.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ UniSpeechSat model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -306,3 +308,7 @@ class UniSpeechSatConfig(PretrainedConfig):
         self.tdnn_kernel = list(tdnn_kernel)
         self.tdnn_dilation = list(tdnn_dilation)
         self.xvector_output_dim = xvector_output_dim
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/wav2vec2/configuration_wav2vec2.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ Wav2Vec2 model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -329,3 +331,7 @@ class Wav2Vec2Config(PretrainedConfig):
         self.tdnn_kernel = list(tdnn_kernel)
         self.tdnn_dilation = list(tdnn_dilation)
         self.xvector_output_dim = xvector_output_dim
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

src/transformers/models/wav2vec2/tokenization_wav2vec2.py

@@ -18,12 +18,22 @@ import json
 import os
 import sys
 import warnings
+from dataclasses import dataclass
 from itertools import groupby
-from typing import Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
-from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
+from ...file_utils import (
+    ModelOutput,
+    PaddingStrategy,
+    TensorType,
+    add_end_docstrings,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+    to_py_obj,
+)
 from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list
 from ...tokenization_utils_base import AddedToken, BatchEncoding
 from ...utils import logging
@@ -32,6 +42,15 @@ from ...utils import logging
 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",
@@ -79,6 +98,28 @@ WAV2VEC2_KWARGS_DOCSTRING = r"""
 """
 
 
+@dataclass
+class Wav2Vec2CTCTokenizerOutput(ModelOutput):
+    """
+    Output type of [` Wav2Vec2CTCTokenizer`], with transcription.
+
+    Args:
+        text (list of `str` or `str`):
+            Decoded logits in text from. Usually the speech transcription.
+        char_offsets (`Dict[str, Union[int, str]]` or `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.
+        word_offsets (`Dict[str, Union[int, str]]` or `Dict[str, Union[int, str]]`):
+            Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets
+            can be used to compute time stamps for each word.
+    """
+
+    text: Union[List[str], str]
+    char_offsets: List[Dict[str, Union[float, str]]] = None
+    word_offsets: List[Dict[str, Union[float, str]]] = None
+
+
 class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
 
     """
@@ -121,6 +162,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         unk_token="<unk>",
         pad_token="<pad>",
         word_delimiter_token="|",
+        replace_word_delimiter_char=" ",
         do_lower_case=False,
         **kwargs
     ):
@@ -131,12 +173,14 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
             pad_token=pad_token,
             do_lower_case=do_lower_case,
             word_delimiter_token=word_delimiter_token,
+            replace_word_delimiter_char=replace_word_delimiter_char,
             **kwargs,
         )
 
         self._word_delimiter_token = word_delimiter_token
 
         self.do_lower_case = do_lower_case
+        self.replace_word_delimiter_char = replace_word_delimiter_char
 
         with open(vocab_file, encoding="utf-8") as vocab_handle:
             self.encoder = json.load(vocab_handle)
@@ -204,31 +248,106 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         return result
 
     def convert_tokens_to_string(
-        self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False
+        self,
-    ) -> str:
+        tokens: List[str],
+        group_tokens: bool = True,
+        spaces_between_special_tokens: bool = False,
+        output_char_offsets: bool = False,
+        output_word_offsets: bool = False,
+    ) -> Dict[str, Union[str, float]]:
         """
         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:
-            tokens = [token_group[0] for token_group in groupby(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
-        filtered_tokens = list(filter(lambda token: token != self.pad_token, tokens))
+        processed_chars = list(filter(lambda char: char != self.pad_token, chars))
-
-        if spaces_between_special_tokens:
-            join_token = " "
-        else:
-            join_token = ""
 
         # replace delimiter token
-        string = join_token.join(
+        processed_chars = [
-            [" " if token == self.word_delimiter_token else token for token in filtered_tokens]
+            self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars
-        ).strip()
+        ]
+
+        # retrieve offsets
+        char_offsets = word_offsets = None
+        if output_char_offsets or output_word_offsets:
+            char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token)
+
+            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: "
+                    f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:"
+                    f" {len(processed_chars)}"
+                )
+
+            # set tokens to correct processed token
+            for i, char in enumerate(processed_chars):
+                char_offsets[i]["char"] = char
+
+            # retrieve word offsets from character offsets
+            word_offsets = None
+            if output_word_offsets:
+                word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char)
+
+        # join to string
+        join_char = " " if spaces_between_special_tokens else ""
+        string = join_char.join(processed_chars).strip()
 
         if self.do_lower_case:
             string = string.lower()
-        return string
+
+        return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets}
+
+    @staticmethod
+    def _compute_offsets(
+        char_repetitions: List[int], chars: List[str], ctc_token: int
+    ) -> 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))
+        return offsets
+
+    @staticmethod
+    def _get_word_offsets(
+        offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " "
+    ) -> Dict[str, Union[str, float]]:
+        word_offsets = []
+        final_offset_idx = len(offsets) - 1
+
+        for i, offset in enumerate(offsets):
+            # define previous, next and current char
+            char = offset["char"]
+            prev_char = offsets[i - 1]["char"] if i > 0 else None
+            next_char = offsets[i + 1]["char"] if i < final_offset_idx else None
+
+            # derive whether word begins, ends and whether current char is in word
+            word_begin = (i == 0 and char != word_delimiter_char) or (prev_char == word_delimiter_char)
+            word_end = (i == final_offset_idx and char != word_delimiter_char) or (next_char == word_delimiter_char)
+            char_is_in_word = char != word_delimiter_char
+
+            if word_begin:
+                word_offset = {"word": "", "start_offset": offset["start_offset"]}
+
+            if word_end:
+                word_offset["end_offset"] = offset["end_offset"]
+                word_offsets.append(word_offset)
+
+            if char_is_in_word:
+                word_offset["word"] += offset["char"]
+
+        return word_offsets
 
     def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
         if is_split_into_words:
@@ -242,6 +361,8 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         clean_up_tokenization_spaces: bool = True,
         group_tokens: bool = True,
         spaces_between_special_tokens: bool = False,
+        output_word_offsets: Optional[bool] = False,
+        output_char_offsets: Optional[bool] = False,
     ) -> str:
         """
         special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the
@@ -256,16 +377,210 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
                 continue
             result.append(token)
 
-        text = self.convert_tokens_to_string(
+        string_output = self.convert_tokens_to_string(
-            result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens
+            result,
+            group_tokens=group_tokens,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            output_word_offsets=output_word_offsets,
+            output_char_offsets=output_char_offsets,
         )
 
+        text = string_output["text"]
+
         if clean_up_tokenization_spaces:
-            clean_text = self.clean_up_tokenization(text)
+            text = self.clean_up_tokenization(text)
-            return clean_text
+
+        if output_word_offsets or output_char_offsets:
+            return Wav2Vec2CTCTokenizerOutput(
+                text=text,
+                char_offsets=string_output["char_offsets"],
+                word_offsets=string_output["word_offsets"],
+            )
         else:
             return text
 
+    # 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 = True,
+        output_char_offsets: bool = False,
+        output_word_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*, defaults to `True`):
+                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.tokenization_wav2vec2.batch_decode`] works the same way with batched output.
+
+                </Tip>
+
+            output_word_offsets (`bool`, *optional*, defaults to `False`):
+                Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
+                and model downsampling rate to compute the time-stamps of transcribed words.
+
+                <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.tokenization_wav2vec2.batch_decode`] works the same way with 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.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
+            sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
+            `output_char_offsets == True` or `output_word_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,
+                output_word_offsets=output_word_offsets,
+                **kwargs,
+            )
+            for seq in sequences
+        ]
+        if output_char_offsets or output_word_offsets:
+            # transform list of dicts to dict of lists
+            return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]})
+
+        return batch_decoded
+
+    # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets`
+    # and `output_word_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 = True,
+        output_char_offsets: bool = False,
+        output_word_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*, defaults to `True`):
+                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`.
+
+                </Tip>
+
+            output_word_offsets (`bool`, *optional*, defaults to `False`):
+                Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
+                and model downsampling rate to compute the time-stamps of transcribed words.
+
+                <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`.
+
+                </Tip>
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific decode method.
+
+        Returns:
+            `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
+            sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
+            `output_char_offsets == True` or `output_word_offsets == True`.
+
+        Example:
+
+        ```python
+        >>> # Let's see how to retrieve time steps for a model
+        >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
+        >>> from datasets import load_dataset
+        >>> import datasets
+        >>> import torch
+
+        >>> # import model, feature extractor, tokenizer
+        >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+
+        >>> # load first sample of English common_voice
+        >>> dataset = load_dataset("common_voice", "en", split="train", streaming=True)
+        >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
+        >>> dataset_iter = iter(dataset)
+        >>> sample = next(dataset_iter)
+
+        >>> # forward sample through model to get greedily predicted transcription ids
+        >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
+        >>> logits = model(input_values).logits[0]
+        >>> pred_ids = torch.argmax(logits, axis=-1)
+
+        >>> # retrieve word stamps (analogous commands for `output_char_offsets`)
+        >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True)
+        >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate
+        >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate
+
+        >>> word_offsets = [
+        ...     {
+        ...         "word": d["word"],
+        ...         "start_time": d["start_offset"] * time_offset,
+        ...         "end_time": d["end_offset"] * time_offset,
+        ...     }
+        ...     for d in outputs.word_offsets
+        ... ]
+        >>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer:
+        >>> # https://huggingface.co/datasets/common_voice/viewer/en/train
+        >>> word_offset
+        >>> # [{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES',
+        >>> # 'start_time': 1.64, 'end_time': 1.90}, {'word': 'MILISANDRA',
+        >>> # 'start_time': 2.26, 'end_time': 2.9}, {'word': 'LOOK', 'start_time': 3.0, 'end_time': 3.16}, ...
+        ```"""
+        # 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,
+            output_word_offsets=output_word_offsets,
+            **kwargs,
+        )
+
     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")
@@ -294,7 +609,7 @@ class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
         Returns:
             `int`: The number of tokens actually added to the vocabulary.
 
-        Examples:
+        Example:
 
         ```python
         # Let's see how to increase the vocabulary of Bert model and tokenizer
@@ -551,6 +866,7 @@ class Wav2Vec2Tokenizer(PreTrainedTokenizer):
 
         if self.do_lower_case:
             string = string.lower()
+
         return string
 
     def _decode(

src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py

@@ -17,10 +17,20 @@
 import json
 import os
 import sys
+from dataclasses import dataclass
 from itertools import groupby
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
-from ...file_utils import requires_backends
+import numpy as np
+
+from ...file_utils import (
+    ModelOutput,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+    requires_backends,
+    to_py_obj,
+)
 from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list
 from ...tokenization_utils_base import AddedToken
 from ...utils import logging
@@ -29,6 +39,15 @@ from ...utils import logging
 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",
@@ -47,6 +66,24 @@ PRETRAINED_VOCAB_FILES_MAP = {
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-lv-60-espeak-cv-ft": sys.maxsize}
 
 
+@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 (`Dict[str, Union[int, str]]` or `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: List[Dict[str, Union[float, str]]] = None
+
+
 class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
 
     """
@@ -284,24 +321,69 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
         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:
-            tokens = [token_group[0] for token_group in groupby(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
-        filtered_tokens = list(filter(lambda token: token != self.pad_token, tokens))
+        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:
-            filtered_tokens = list(filter(lambda token: token != self.word_delimiter_token, filtered_tokens))
+            processed_chars = list(filter(lambda token: token != self.word_delimiter_token, processed_chars))
 
-        string = " ".join(filtered_tokens).strip()
+        # 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
+            )
 
-        return string
+            if len(char_offsets) != len(processed_chars):
+                raise ValueError(
+                    f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}"
+                    f" 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,
@@ -311,6 +393,7 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
         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
@@ -325,19 +408,137 @@ class Wav2Vec2PhonemeCTCTokenizer(PreTrainedTokenizer):
                 continue
             result.append(token)
 
-        text = self.convert_tokens_to_string(
+        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"]
+
         if clean_up_tokenization_spaces:
-            clean_text = self.clean_up_tokenization(text)
+            text = self.clean_up_tokenization(text)
-            return clean_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 = True,
+        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*, defaults to `True`):
+                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 = True,
+        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*, defaults to `True`):
+                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")

src/transformers/models/wavlm/configuration_wavlm.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """ WavLM model configuration"""
 
+import math
+
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -330,3 +332,7 @@ class WavLMConfig(PretrainedConfig):
         self.tdnn_kernel = list(tdnn_kernel)
         self.tdnn_dilation = list(tdnn_dilation)
         self.xvector_output_dim = xvector_output_dim
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)

tests/test_tokenization_wav2vec2.py

@@ -29,7 +29,7 @@ from transformers import (
     Wav2Vec2CTCTokenizer,
     Wav2Vec2Tokenizer,
 )
-from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
+from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
 from transformers.testing_utils import require_torch, slow
 
 from .test_tokenization_common import TokenizerTesterMixin
@@ -422,27 +422,16 @@ class Wav2Vec2CTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
     def test_tokenizer_decode_special(self):
         tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
 
+        # fmt: off
         sample_ids = [
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
             [24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
         ]
         sample_ids_2 = [
             [11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
-            [
+            [24, 22, 5, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.word_delimiter_token_id],
-                24,
-                22,
-                5,
-                tokenizer.pad_token_id,
-                tokenizer.pad_token_id,
-                tokenizer.pad_token_id,
-                tokenizer.word_delimiter_token_id,
-                24,
-                22,
-                5,
-                77,
-                tokenizer.word_delimiter_token_id,
-            ],
         ]
+        # fmt: on
 
         batch_tokens = tokenizer.batch_decode(sample_ids)
         batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
@@ -454,27 +443,12 @@ class Wav2Vec2CTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizer.add_tokens(["!", "?"])
         tokenizer.add_special_tokens({"cls_token": "$$$"})
 
+        # fmt: off
         sample_ids = [
-            [
+            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 32, 32, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
-                11,
-                5,
-                15,
-                tokenizer.pad_token_id,
-                15,
-                8,
-                98,
-                32,
-                32,
-                33,
-                tokenizer.word_delimiter_token_id,
-                32,
-                32,
-                33,
-                34,
-                34,
-            ],
             [24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34],
         ]
+        # fmt: on
         batch_tokens = tokenizer.batch_decode(sample_ids)
 
         self.assertEqual(batch_tokens, ["HELLO<unk>!?!?$$$", "BYE BYE<unk>$$$"])
@@ -499,6 +473,187 @@ class Wav2Vec2CTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
         expected_sent = tokenizer.decode(tokenizer(sent).input_ids, spaces_between_special_tokens=True)
         self.assertEqual(sent, expected_sent)
 
+    @staticmethod
+    def get_from_offsets(offsets, key):
+        retrieved_list = [d[key] for d in offsets]
+        return retrieved_list
+
+    def test_offsets(self):
+        tokenizer = self.get_tokenizer()
+
+        # fmt: off
+        # HEEEEE||LLL<pad>LO<unk> => HE LLO<unk>
+        # 1H + 5E + 2| + 3L + 1<pad> + 1L + 1O + 1<unk>
+        sample_ids = [11, 5, 5, 5, 5, 5, 4, 4, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98]
+        # fmt: on
+
+        outputs_char = tokenizer.decode(sample_ids, output_char_offsets=True)
+        # check Wav2Vec2CTCTokenizerOutput keys for char
+        self.assertTrue(len(outputs_char.keys()), 2)
+        self.assertTrue("text" in outputs_char)
+        self.assertTrue("char_offsets" in outputs_char)
+        self.assertTrue(isinstance(outputs_char, Wav2Vec2CTCTokenizerOutput))
+
+        outputs_word = tokenizer.decode(sample_ids, output_word_offsets=True)
+        # check Wav2Vec2CTCTokenizerOutput keys for word
+        self.assertTrue(len(outputs_word.keys()), 2)
+        self.assertTrue("text" in outputs_word)
+        self.assertTrue("word_offsets" in outputs_word)
+        self.assertTrue(isinstance(outputs_word, Wav2Vec2CTCTokenizerOutput))
+
+        outputs = tokenizer.decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
+        # check Wav2Vec2CTCTokenizerOutput keys for both
+        self.assertTrue(len(outputs.keys()), 3)
+        self.assertTrue("text" in outputs)
+        self.assertTrue("char_offsets" in outputs)
+        self.assertTrue("word_offsets" in outputs)
+        self.assertTrue(isinstance(outputs, Wav2Vec2CTCTokenizerOutput))
+
+        # check that order of chars is correct and identical for both outputs
+        self.assertEqual("".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
+        self.assertEqual(
+            self.get_from_offsets(outputs["char_offsets"], "char"), ["H", "E", " ", "L", "L", "O", "<unk>"]
+        )
+        self.assertListEqual(
+            self.get_from_offsets(outputs["char_offsets"], "char"),
+            self.get_from_offsets(outputs_char["char_offsets"], "char"),
+        )
+
+        # check that order of words is correct and identical to both outputs
+        self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"], "word")), outputs.text)
+        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "word"), ["HE", "LLO<unk>"])
+        self.assertListEqual(
+            self.get_from_offsets(outputs["word_offsets"], "word"),
+            self.get_from_offsets(outputs_word["word_offsets"], "word"),
+        )
+
+        # check that offsets are actually correct for char
+        # 0 is H, 1 is E, 6 is | (" "),  8 is 1st L,  12 is 2nd L, 13 is O, 14 is <unk>
+        self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 6, 8, 12, 13, 14])
+        # 1 is H, 6 is E, 8 is | (" "),  11 is 1st L (note due to <pad>
+        # different begin of 2nd L), 13 is 2nd L, 14 is O, 15 is <unk>
+        self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 6, 8, 11, 13, 14, 15])
+
+        # check that offsets are actually correct for word
+        # H is at 1st position of first word, first L is at 8th position of second word
+        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "start_offset"), [0, 8])
+        # last E is at 6th position of first word, first L is at last (15th) position of second word
+        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "end_offset"), [6, 15])
+
+    def test_offsets_batch(self):
+        tokenizer = self.get_tokenizer()
+
+        def check_list_tuples_equal(outputs_batch, outputs_list):
+            self.assertTrue(isinstance(outputs_batch, Wav2Vec2CTCTokenizerOutput))
+            self.assertTrue(isinstance(outputs_list[0], Wav2Vec2CTCTokenizerOutput))
+
+            # transform list to ModelOutput
+            outputs_batch_2 = Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in outputs_list] for k in outputs_list[0]})
+
+            self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
+
+            def recursive_check(list_or_dict_1, list_or_dict_2):
+                if isinstance(list_or_dict_1, list):
+                    [recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
+                self.assertEqual(list_or_dict_1, list_or_dict_2)
+
+            if "char_offsets" in outputs_batch:
+                recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
+
+            if "word_offsets" in outputs_batch:
+                recursive_check(outputs_batch["word_offsets"], outputs_batch_2["word_offsets"])
+
+        # fmt: off
+        sample_ids = [
+            [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
+            [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
+        ]
+        # fmt: on
+
+        # We assume that `decode` works as expected. All we will check now is
+        # the output type is correct and the output is identical to `decode`
+
+        # char
+        outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
+        outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
+        check_list_tuples_equal(outputs_char_batch, outputs_char)
+
+        # word
+        outputs_word_batch = tokenizer.batch_decode(sample_ids, output_word_offsets=True)
+        outputs_word = [tokenizer.decode(ids, output_word_offsets=True) for ids in sample_ids]
+        check_list_tuples_equal(outputs_word_batch, outputs_word)
+
+        # both
+        outputs_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
+        outputs = [tokenizer.decode(ids, output_word_offsets=True, output_char_offsets=True) for ids in sample_ids]
+        check_list_tuples_equal(outputs_batch, outputs)
+
+    def test_offsets_integration(self):
+        tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
+        # pred_ids correspond to the following code
+        # ```
+        #        from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
+        #        from datasets import load_dataset
+        #        import datasets
+        #        import torch
+        #        model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        #        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+        #
+        #        ds = load_dataset("common_voice", "en", split="train", streaming=True)
+        #        ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
+        #        ds_iter = iter(ds)
+        #        sample = next(ds_iter)
+        #
+        #        input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
+        #        logits = model(input_values).logits
+        #        pred_ids = torch.argmax(logits, axis=-1).cpu().tolist()
+        # ```
+        # fmt: off
+        pred_ids = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 11, 0, 0, 0, 22, 0, 0, 4, 4, 4, 14, 0, 0, 0, 0, 0, 8, 8, 0, 5, 5, 0, 12, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 0, 10, 0, 0, 0, 15, 0, 0, 10, 0, 0, 0, 12, 0, 0, 0, 0, 0, 7, 0, 9, 0, 0, 14, 0, 0, 0, 13, 0, 7, 0, 0, 4, 4, 0, 15, 8, 8, 0, 0, 8, 0, 26, 0, 0, 4, 4, 0, 0, 15, 0, 0, 0, 0, 0, 0, 10, 0, 26, 5, 5, 0, 4, 4, 0, 0, 12, 11, 0, 0, 5, 4, 4, 4, 0, 18, 0, 0, 0, 7, 9, 9, 0, 6, 0, 12, 12, 4, 4, 0, 6, 0, 0, 8, 0, 4, 4, 4, 0, 19, 0, 0, 8, 9, 9, 0, 0, 0, 0, 12, 12, 0, 0, 0, 0, 0, 0, 0, 16, 16, 0, 0, 17, 5, 5, 5, 0, 4, 4, 4, 0, 0, 29, 29, 0, 0, 0, 0, 8, 11, 0, 9, 9, 0, 0, 0, 4, 4, 0, 12, 12, 0, 0, 0, 9, 0, 0, 0, 0, 0, 8, 18, 0, 0, 0, 4, 4, 0, 0, 8, 9, 0, 4, 4, 0, 6, 11, 5, 0, 4, 4, 0, 13, 13, 0, 0, 0, 10, 0, 0, 25, 0, 0, 6, 0, 4, 4, 0, 0, 0, 0, 7, 0, 0, 23, 0, 0, 4, 4, 0, 0, 0, 6, 11, 0, 5, 4, 4, 18, 0, 0, 0, 0, 0, 0, 7, 15, 0, 0, 0, 15, 15, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+
+        # wav2vec2-base downsamples input audio by a factor of 320
+        # sampling rate for wav2vec2-base is 16_000
+        time_offset_wav2vec2_base = 320 / 16_000
+
+        expected_char_time_stamps_text = ['W', 'H', 'Y', ' ', 'D', 'O', 'E', 'S', ' ', 'M', 'I', 'L', 'I', 'S', 'A', 'N', 'D', 'R', 'A', ' ', 'L', 'O', 'O', 'K', ' ', 'L', 'I', 'K', 'E', ' ', 'S', 'H', 'E', ' ', 'W', 'A', 'N', 'T', 'S', ' ', 'T', 'O', ' ', 'C', 'O', 'N', 'S', 'U', 'M', 'E', ' ', 'J', 'O', 'H', 'N', ' ', 'S', 'N', 'O', 'W', ' ', 'O', 'N', ' ', 'T', 'H', 'E', ' ', 'R', 'I', 'V', 'T', ' ', 'A', 'P', ' ', 'T', 'H', 'E', ' ', 'W', 'A', 'L', 'L', ' ']
+        expected_char_time_stamps_start = [1.42, 1.44, 1.52, 1.58, 1.64, 1.76, 1.82, 1.88, 1.92, 2.26, 2.32, 2.4, 2.46, 2.54, 2.66, 2.7, 2.76, 2.84, 2.88, 2.94, 3.0, 3.02, 3.1, 3.14, 3.2, 3.28, 3.42, 3.46, 3.48, 3.54, 3.62, 3.64, 3.7, 3.72, 3.8, 3.88, 3.9, 3.96, 4.0, 4.04, 4.1, 4.16, 4.2, 4.28, 4.34, 4.36, 4.48, 4.66, 4.74, 4.76, 4.84, 4.94, 5.06, 5.08, 5.12, 5.22, 5.28, 5.38, 5.5, 5.52, 5.6, 5.68, 5.7, 5.74, 5.8, 5.82, 5.84, 5.88, 5.94, 6.04, 6.1, 6.16, 6.2, 6.32, 6.38, 6.44, 6.54, 6.56, 6.6, 6.62, 6.66, 6.8, 6.82, 6.9, 6.96]
+        expected_char_time_stamps_end = [1.44, 1.46, 1.54, 1.64, 1.66, 1.8, 1.86, 1.9, 2.06, 2.28, 2.34, 2.42, 2.48, 2.56, 2.68, 2.72, 2.78, 2.86, 2.9, 2.98, 3.02, 3.06, 3.12, 3.16, 3.24, 3.3, 3.44, 3.48, 3.52, 3.58, 3.64, 3.66, 3.72, 3.78, 3.82, 3.9, 3.94, 3.98, 4.04, 4.08, 4.12, 4.18, 4.26, 4.3, 4.36, 4.4, 4.52, 4.7, 4.76, 4.82, 4.9, 4.98, 5.08, 5.1, 5.16, 5.26, 5.32, 5.4, 5.52, 5.54, 5.64, 5.7, 5.72, 5.78, 5.82, 5.84, 5.86, 5.92, 5.98, 6.06, 6.12, 6.18, 6.24, 6.34, 6.4, 6.48, 6.56, 6.58, 6.62, 6.66, 6.68, 6.82, 6.84, 6.94, 7.02]
+
+        expected_word_time_stamps_text = ['WHY', 'DOES', 'MILISANDRA', 'LOOK', 'LIKE', 'SHE', 'WANTS', 'TO', 'CONSUME', 'JOHN', 'SNOW', 'ON', 'THE', 'RIVT', 'AP', 'THE', 'WALL']
+        expected_word_time_stamps_start = [1.42, 1.64, 2.26, 3.0, 3.28, 3.62, 3.8, 4.1, 4.28, 4.94, 5.28, 5.68, 5.8, 5.94, 6.32, 6.54, 6.66]
+        expected_word_time_stamps_end = [1.54, 1.9, 2.9, 3.16, 3.52, 3.72, 4.04, 4.18, 4.82, 5.16, 5.54, 5.72, 5.86, 6.18, 6.4, 6.62, 6.94]
+        # fmt: on
+
+        output = tokenizer.batch_decode(pred_ids, output_char_offsets=True, output_word_offsets=True)
+
+        char_offsets_text = self.get_from_offsets(output["char_offsets"][0], "char")
+        char_offsets_start = self.get_from_offsets(output["char_offsets"][0], "start_offset")
+        char_offsets_end = self.get_from_offsets(output["char_offsets"][0], "end_offset")
+
+        word_offsets_text = self.get_from_offsets(output["word_offsets"][0], "word")
+        word_offsets_start = self.get_from_offsets(output["word_offsets"][0], "start_offset")
+        word_offsets_end = self.get_from_offsets(output["word_offsets"][0], "end_offset")
+
+        # let's transform offsets to time stamps in seconds
+        char_time_stamps_start = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_start]
+        char_time_stamps_end = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_end]
+
+        word_time_stamps_start = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_start]
+        word_time_stamps_end = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_end]
+
+        # NOTE: you can verify the above results by checking out the dataset viewer
+        # on https://huggingface.co/datasets/common_voice/viewer/en/train and
+        # downloading / playing the sample `common_voice_en_100038.mp3`. As
+        # you can hear the time-stamps match more or less
+
+        self.assertListEqual(expected_char_time_stamps_text, char_offsets_text)
+        self.assertListEqual(expected_char_time_stamps_start, char_time_stamps_start)
+        self.assertListEqual(expected_char_time_stamps_end, char_time_stamps_end)
+
+        self.assertListEqual(expected_word_time_stamps_text, word_offsets_text)
+        self.assertListEqual(expected_word_time_stamps_start, word_time_stamps_start)
+        self.assertListEqual(expected_word_time_stamps_end, word_time_stamps_end)
+
     def test_pretrained_model_lists(self):
         # Wav2Vec2Model has no max model length => no testing
         pass

tests/test_tokenization_wav2vec2_phoneme.py

@@ -20,6 +20,7 @@ from typing import Tuple
 
 from transformers import Wav2Vec2PhonemeCTCTokenizer
 from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
+from transformers.models.wav2vec2_phoneme.tokenization_wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizerOutput
 from transformers.testing_utils import require_phonemizer
 
 from .test_tokenization_common import TokenizerTesterMixin
@@ -248,23 +249,94 @@ class Wav2Vec2PhonemeCTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
         batch_tokens = tokenizer.batch_decode(sample_ids)
         self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])
 
-    # overwrite common test
+    @staticmethod
+    def get_from_offsets(offsets, key):
+        retrieved_list = [d[key] for d in offsets]
+        return retrieved_list
+
+    def test_offsets(self):
+        tokenizer = self.get_tokenizer(word_delimiter_token="|")
+        tokenizer.add_tokens("|")
+
+        # fmt: off
+        # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
+        sample_ids = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
+        # fmt: on
+
+        outputs = tokenizer.decode(sample_ids, output_char_offsets=True, filter_word_delimiter_token=False)
+        # check Wav2Vec2CTCTokenizerOutput keys for char
+        self.assertTrue(len(outputs.keys()), 2)
+        self.assertTrue("text" in outputs)
+        self.assertTrue("char_offsets" in outputs)
+        self.assertTrue(isinstance(outputs, Wav2Vec2PhonemeCTCTokenizerOutput))
+
+        # check that order of chars is correct and identical for both outputs
+        self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
+        self.assertListEqual(
+            self.get_from_offsets(outputs["char_offsets"], "char"), ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]
+        )
+
+        # check that offsets are actually correct for char
+        # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
+        # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
+        self.assertListEqual(
+            self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 4, 7, 9, 11, 12, 15, 16]
+        )
+        self.assertListEqual(
+            self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 4, 6, 9, 10, 12, 15, 16, 17]
+        )
+
+    def test_offsets_batch(self):
+        tokenizer = self.get_tokenizer(word_delimiter_token="|")
+
+        def check_list_tuples_equal(outputs_batch, outputs_list):
+            self.assertTrue(isinstance(outputs_batch, Wav2Vec2PhonemeCTCTokenizerOutput))
+            self.assertTrue(isinstance(outputs_list[0], Wav2Vec2PhonemeCTCTokenizerOutput))
+
+            # transform list to ModelOutput
+            outputs_batch_2 = Wav2Vec2PhonemeCTCTokenizerOutput(
+                {k: [d[k] for d in outputs_list] for k in outputs_list[0]}
+            )
+
+            self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
+
+            def recursive_check(list_or_dict_1, list_or_dict_2):
+                if isinstance(list_or_dict_1, list):
+                    [recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
+                self.assertEqual(list_or_dict_1, list_or_dict_2)
+
+            if "char_offsets" in outputs_batch:
+                recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
+
+        # fmt: off
+        sample_ids = [
+            [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
+            [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
+        ]
+        # fmt: on
+
+        # We assume that `decode` works as expected. All we will check now is
+        # the output type is correct and the output is identical to `decode`
+
+        # char
+        outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
+        outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
+        check_list_tuples_equal(outputs_char_batch, outputs_char)
+
+    @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes")
     def test_added_tokens_do_lower_case(self):
-        # Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes
         pass
 
-    # overwrite common test
+    @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes")
     def test_encode_decode_with_spaces(self):
-        # Wav2Vec2PhonemeTokenizer always puts spaces between phonemes
         pass
 
-    # overwrite common test
+    @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency")
     def test_internal_consistency(self):
-        # encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency
         pass
 
+    @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing")
     def test_pretrained_model_lists(self):
-        # Wav2Vec2PhonemeModel has no max model length => no testing
         pass
 
     # overwrite common