from typing import Optional, Union import numpy as np import torch import transformers class UltravoxProcessor(transformers.ProcessorMixin): """ Constructs an Ultravox processor which wraps an audio processor and a tokenizer into a single processor. Args: audio_processor: The audio processor for the audio encoder. tokenizer: The tokenizer for the language model. """ attributes = ["audio_processor", "tokenizer"] audio_processor_class = ( "Wav2Vec2Processor", "SeamlessM4TFeatureExtractor", "WhisperProcessor", ) tokenizer_class = ( "PreTrainedTokenizer", "PreTrainedTokenizerFast", ) tokenizer: transformers.PreTrainedTokenizerBase audio_processor: transformers.ProcessorMixin def __init__( self, audio_processor=None, tokenizer=None, audio_padding: str = "longest", encoder_ds_factor: int = 320, stack_factor: int = 8, audio_placeholder: str = "<|audio|>", ): """ Args: audio_processor: The audio processor for the audio encoder. tokenizer: The tokenizer for the language model. audio_padding: The padding strategy for the audio encoder. encoder_ds_factor: The downsample factor of the audio encoder. stack_factor: The factor by which the audio encoder output is stacked in the multimodal projector. audio_placeholder: The placeholder for the audio in the text. """ self.audio_padding = audio_padding self.encoder_ds_factor = encoder_ds_factor self.stack_factor = stack_factor self.audio_placeholder = audio_placeholder self.audio_token_replacement = tokenizer.eos_token assert ( self.audio_token_replacement is not None ), "The tokenizer has no EOS token. Cannot recover." super().__init__(audio_processor=audio_processor, tokenizer=tokenizer) def __call__( self, text: Optional[str] = None, audio: Optional[Union[np.ndarray, torch.Tensor]] = None, sampling_rate: Optional[int] = None, return_tensors: Optional[ Union[str, transformers.TensorType] ] = transformers.TensorType.PYTORCH, **kwargs, ) -> transformers.BatchFeature: """ Main method to prepare for the model one text sequence and audio. This method forwards the `text` and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the audio(s), this method forwards the `audio`, `sampling_rate` and `kwargs` arguments to audio processor's [`~Wav2Vec2Processor.__call__`] if `audio` is not `None`. Please refer to the docstring of the above two methods for more information. Args: text (`str`, `List[str]`): The sequence to be encoded. Sequence can be a string or (pretokenized string). audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The audio to be prepared. Audio can be NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T the sample length of the audio. sampling_rate (`int`, *optional*, defaults to 16000): Sampling rate of the input audio. We expect 16kHz audio. Don't change this value unless you know what you are doing. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **audio_values** -- Processed audio values to be fed to a model. Returned when `audio` is not `None`. - **audio_token_len** -- Predicted number of audio frames: this value is guaranteed to be a close upper bound. Returned when `audio` is not `None`. - **audio_token_start_idx** -- The index in the tokenized text where the audio starts. Returned when `audio` is not `None`. """ # TODO: Add support for multiple audio and text inputs. data = {} audio_embed_frames = 0 if audio is not None and len(audio) > 0: if self.audio_padding == "max_length": # 30 seconds is the expected length for Whisper assert sampling_rate is not None, "Sampling rate must be provided." audio_len = 30 * sampling_rate else: audio_len = audio.shape[-1] # It's guaranteed that the number of frames is less than or equal to this amount. # For Whisper this is exact AFAICT, but for Wav2Vec2 it's an upper bound. # Currently, StackAudioFrames makes sure an over-estimation won't cause issues by padding the audio embeddings. nb_encoder_frames = int(round(audio_len / self.encoder_ds_factor + 1e-4)) audio_embed_frames = int(np.ceil(nb_encoder_frames / self.stack_factor)) data["audio_token_len"] = [audio_embed_frames] # Main audio processing. The processor is model-specific. x = self.audio_processor( audio, sampling_rate=sampling_rate, padding="longest", max_length=audio_len, **kwargs, ) if "input_features" in x: data["audio_values"] = x.input_features else: data["audio_values"] = x.input_values if text is not None: assert isinstance( text, str ), "Text must be a string. Batch mode not supported yet." if self.audio_placeholder in text: if "audio_token_len" not in data: raise ValueError( f"audio must be provided when using audio placeholder ({self.audio_placeholder}) in text." ) start_idx = len( self.tokenizer.encode( text[: text.index(self.audio_placeholder)], add_special_tokens=False, ) ) data["audio_token_start_idx"] = [start_idx] # Replace the audio placeholder with the audio token. # e.g. "Transcribe <|audio|>" -> "Transcribe " # where the number of is the number of audio frames. text = text.replace( self.audio_placeholder, self.audio_token_replacement * audio_embed_frames, ) # Special tokens like BOS should already have been added by the caller. data.update(self.tokenizer([text], add_special_tokens=False, **kwargs)) return transformers.BatchFeature(data=data, tensor_type=return_tensors) def batch_decode(self, *args, **kwargs): return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names audio_processor_input_names = self.audio_processor.model_input_names return list(set(tokenizer_input_names + audio_processor_input_names))