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# coding=utf-8
# Copyright 2022 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.
"""
Speech processor class for M-CTC-T
"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class MCTCTProcessor(ProcessorMixin):
r"""
Constructs a MCTCT processor which wraps a MCTCT feature extractor and a MCTCT tokenizer into a single processor.
[`MCTCTProcessor`] offers all the functionalities of [`MCTCTFeatureExtractor`] and [`AutoTokenizer`]. See the
[`~MCTCTProcessor.__call__`] and [`~MCTCTProcessor.decode`] for more information.
Args:
feature_extractor (`MCTCTFeatureExtractor`):
An instance of [`MCTCTFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`AutoTokenizer`):
An instance of [`AutoTokenizer`]. The tokenizer is a required input.
"""
feature_extractor_class = "MCTCTFeatureExtractor"
tokenizer_class = "AutoTokenizer"
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.__call__`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to AutoTokenizer's
[`~AutoTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.pop("text", None)
if len(args) > 0:
audio = args[0]
args = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process.")
if audio is not None:
inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif audio is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to AutoTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def pad(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.pad`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*args, **kwargs)
input_features = kwargs.pop("input_features", None)
labels = kwargs.pop("labels", None)
if len(args) > 0:
input_features = args[0]
args = args[1:]
if input_features is not None:
input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
if labels is not None:
labels = self.tokenizer.pad(labels, **kwargs)
if labels is None:
return input_features
elif input_features is None:
return labels
else:
input_features["labels"] = labels["input_ids"]
return input_features
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to AutoTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning MCTCT.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call."
)
self._in_target_context_manager = True
self.current_processor = self.tokenizer
yield
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
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