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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 </s></s></s></s></s></s></s></s>"
# where the number of </s> 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))
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