Create ultravox_processing.py

ultravox_processing.py

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+from typing import Any, Dict, 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]
+
+            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]
+                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))