File size: 9,330 Bytes
4ff16d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
from typing import Optional, Union
import numpy as np
import torch
import transformers
from .ultravox_config import UltravoxConfig
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."
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
super().__init__(audio_processor=audio_processor, tokenizer=tokenizer)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
config: UltravoxConfig = transformers.AutoConfig.from_pretrained(
pretrained_model_name_or_path, **kwargs
)
audio_processor = transformers.AutoProcessor.from_pretrained(
config.audio_model_id
or config.audio_config._name_or_path
or "facebook/wav2vec2-base-960h"
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs
)
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token
return cls(
audio_processor=audio_processor,
tokenizer=tokenizer,
stack_factor=config.stack_factor,
)
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\n<|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))
UltravoxProcessor.register_for_auto_class()
transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor)
|