# Copyright 2025 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. from tokenizers.decoders import DecodeStream from ...audio_utils import AudioInput, make_list_of_audio from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import auto_docstring, logging logger = logging.get_logger(__name__) class ParakeetProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "audio_kwargs": { "sampling_rate": 16000, "padding": "longest", "return_attention_mask": True, "subsampling_factor": 8, }, "text_kwargs": { "padding": True, "padding_side": "right", "add_special_tokens": False, }, "common_kwargs": {"return_tensors": "pt"}, } @auto_docstring class ParakeetProcessor(ProcessorMixin): def __init__(self, feature_extractor, tokenizer, blank_token=""): """ blank_token (`str`, *optional*, defaults to `""`): Blank token for TDT decoding. """ self.blank_token = blank_token self.blank_token_id = tokenizer.convert_tokens_to_ids(blank_token) super().__init__(feature_extractor, tokenizer) @auto_docstring def __call__( self, audio: AudioInput, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, sampling_rate: int | None = None, **kwargs: Unpack[ParakeetProcessorKwargs], ): r""" sampling_rate (`int`, *optional*): The sampling rate of the input audio in Hz. This should match the sampling rate expected by the feature extractor (defaults to 16000 Hz). If provided, it will be validated against the processor's expected sampling rate, and an error will be raised if they don't match. If not provided, a warning will be issued and the default sampling rate will be assumed. """ audio = make_list_of_audio(audio) output_kwargs = self._merge_kwargs( ParakeetProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if sampling_rate is None: logger.warning_once( f"You've provided audio without specifying the sampling rate. It will be assumed to be {output_kwargs['audio_kwargs']['sampling_rate']}, which can result in silent errors." ) elif sampling_rate != output_kwargs["audio_kwargs"]["sampling_rate"]: raise ValueError( f"The sampling rate of the audio ({sampling_rate}) does not match the sampling rate of the processor ({output_kwargs['audio_kwargs']['sampling_rate']}). Please provide resampled the audio to the expected sampling rate." ) if audio is not None: inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"]) if text is not None: encodings = self.tokenizer(text, **output_kwargs["text_kwargs"]) if text is None: return inputs else: inputs["labels"] = encodings["input_ids"] # Prepend blank token to labels to form decoder_input_ids. # The TDT decoder expects [blank, label_0, ..., label_{U-1}] as input, if isinstance(text, str): text = [text] decoder_text = [self.blank_token + t for t in text] decoder_encodings = self.tokenizer(decoder_text, **output_kwargs["text_kwargs"]) inputs["decoder_input_ids"] = decoder_encodings["input_ids"] return inputs @property def model_input_names(self): feature_extractor_input_names = self.feature_extractor.model_input_names return feature_extractor_input_names + ["labels", "decoder_input_ids"] def decode(self, *args, durations=None, **kwargs): """ Forward arguments to [`~PreTrainedTokenizer.decode`] and post-process the timestamps (if provided for TDT) as in the NeMo library. """ decoded = self.tokenizer.decode(*args, **kwargs) if durations is not None: token_ids = args[0] # Derive per-step frame indices from cumulative sum of durations. timestamps = durations.cumsum(dim=-1) - durations output_kwargs = self._merge_kwargs( ParakeetProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, ) frame_rate = ( self.feature_extractor.hop_length / self.feature_extractor.sampling_rate * output_kwargs["audio_kwargs"]["subsampling_factor"] ) # Filter padding/blank tokens and decode per sequence to keep track of token-level timestamps # See `compute_rnnt_timestamps` in NeMo: # https://github.com/NVIDIA-NeMo/NeMo/blob/1692a8fb97e1aadc883cfadd2a57c4e8a1b793aa/nemo/collections/asr/parts/submodules/rnnt_decoding.py#L993 skip_ids = {self.tokenizer.pad_token_id, self.blank_token_id} proc_timestamps = [] for batch_ids, batch_timestamps, batch_durations in zip(token_ids, timestamps, durations): stream = DecodeStream(skip_special_tokens=True) timestamp_dict = [] for i, token_id in enumerate(batch_ids): if int(token_id) in skip_ids: continue chunk = stream.step(self.tokenizer._tokenizer, int(token_id)) if chunk is not None: timestamp_dict.append( { "token": chunk, "start": int(batch_timestamps[i]), "end": int(batch_timestamps[i] + batch_durations[i]), } ) proc_timestamps.append(self._refine_timestamps_tdt(timestamp_dict, frame_rate)) return decoded, proc_timestamps return decoded def _refine_timestamps_tdt( self, char_offsets, frame_rate, supported_punctuation=["?", "'", "¡", "¿", "-", ":", ",", "%", "/", ".", "!"] ): for i, offset in enumerate(char_offsets): # Convert frame indices to seconds offset["start"] = offset["start"] * frame_rate offset["end"] = offset["end"] * frame_rate # If token is a punctuation mark, set its start and end offset as start and end of previous token if offset["token"] in supported_punctuation and i > 0: offset["start"] = char_offsets[i - 1]["end"] offset["end"] = offset["start"] return char_offsets __all__ = ["ParakeetProcessor"]