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processing_whisper.py
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processing_whisper.py
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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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 Whisper
"""
from ...processing_utils import ProcessorMixin
class WhisperProcessor(ProcessorMixin):
r"""
Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
processor.
[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
the [`~WhisperProcessor.__call__`] and [`~WhisperProcessor.decode`] for more information.
Args:
feature_extractor (`WhisperFeatureExtractor`):
An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`WhisperTokenizer`):
An instance of [`WhisperTokenizer`]. The tokenizer is a required input.
"""
feature_extractor_class = "WhisperFeatureExtractor"
tokenizer_class = "WhisperTokenizer"
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True, **kwargs):
forced_decoder_tokens = "<|startoftranscript|>"
if language is not None:
if language not in self.tokenizer.additional_special_tokens:
raise ValueError(
f"{language} is not supported. The language should be one of the following: '<|en|>',"
" '<|zh|>', '<|de|>', '<|es|>', '<|ru|>', '<|ko|>', '<|fr|>', '<|ja|>', '<|pt|>', '<|tr|>',"
" '<|pl|>', '<|ca|>', '<|nl|>', '<|ar|>', '<|sv|>', '<|it|>', '<|id|>', '<|hi|>', '<|fi|>',"
" '<|vi|>', '<|iw|>', '<|uk|>', '<|el|>', '<|ms|>', '<|cs|>', '<|ro|>', '<|da|>', '<|hu|>',"
" '<|ta|>', '<|no|>', '<|th|>', '<|ur|>', '<|hr|>', '<|bg|>', '<|lt|>', '<|la|>', '<|mi|>',"
" '<|ml|>', '<|cy|>', '<|sk|>', '<|te|>', '<|fa|>', '<|lv|>', '<|bn|>', '<|sr|>', '<|az|>',"
" '<|sl|>', '<|kn|>', '<|et|>', '<|mk|>', '<|br|>', '<|eu|>', '<|is|>', '<|hy|>', '<|ne|>',"
" '<|mn|>', '<|bs|>', '<|kk|>', '<|sq|>', '<|sw|>', '<|gl|>', '<|mr|>', '<|pa|>', '<|si|>',"
" '<|km|>', '<|sn|>', '<|yo|>', '<|so|>', '<|af|>', '<|oc|>', '<|ka|>', '<|be|>', '<|tg|>',"
" '<|sd|>', '<|gu|>', '<|am|>', '<|yi|>', '<|lo|>', '<|uz|>', '<|fo|>', '<|ht|>', '<|ps|>',"
" '<|tk|>', '<|nn|>', '<|mt|>', '<|sa|>', '<|lb|>', '<|my|>', '<|bo|>', '<|tl|>', '<|mg|>',"
" '<|as|>', '<|tt|>', '<|haw|>', '<|ln|>', '<|ha|>', '<|ba|>', '<|jw|>', '<|su|>'"
)
forced_decoder_tokens += f"<|{language}|>"
if task is not None:
if task not in self.tokenizer.additional_special_tokens:
raise ValueError(f"{task} is not supported. The language should be in : \{'transcribe', 'translate'}")
forced_decoder_tokens += f"<|{task}|>"
forced_decoder_tokens += "<|notimestamps|>" if no_timestamps else None
forced_decoder_ids = self.tokenizer.encode(forced_decoder_tokens, **kwargs)
return forced_decoder_ids
def __call__(self, *args, language=None, task=None, no_timestamps=True, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to WhisperFeatureExtractor's
[`~WhisperFeatureExtractor.__call__`] and returns its output. If used in the context
[`~WhisperProcessor.as_target_processor`] this method forwards all its arguments to WhisperTokenizer's
[`~WhisperTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
Args:
- language
- task
- no_timestamp
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
audio = kwargs.pop("audio", 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, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
forced_decoder_ids = self._get_decoder_prompt_ids(task, language, no_timestamps, **kwargs)
if text is None:
if forced_decoder_ids is not None:
return inputs, forced_decoder_ids
return inputs
elif audio is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
inputs["forced_decoder_ids"] = forced_decoder_ids
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)