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README.md
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README.md
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# ultravox-v0_3_a14192466442186752847586
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---
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language:
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- en
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license: mit
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library_name: transformers
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datasets:
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- fixie-ai/librispeech_asr
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- fixie-ai/common_voice_17_0
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pipeline_tag: audio-text-to-text
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---
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ultravox-v0_3
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# Model Card for Ultravox
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Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) and [Whisper-small](https://huggingface.co/openai/whisper-small) backbone.\
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See https://ultravox.ai for the GitHub repo and more information.
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## Model Details
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### Model Description
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Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message).
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The input to the model is given as a text prompt with a special `<|audio|>` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio.
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Using the merged embeddings as input, the model will then generate output text as usual.
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In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output.
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No preference tuning has been applied to this revision of the model.
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- **Developed by:** Fixie.ai
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- **License:** MIT
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### Model Sources
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- **Repository:** https://ultravox.ai
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- **Demo:** See repo
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## Usage
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Think of the model as an LLM that can also hear and understand speech. As such, it can be used as a voice agent, and also to do speech-to-speech translation, analysis of spoken audio, etc.
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To use the model, try the following:
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```python
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# pip install transformers peft librosa
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import transformers
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import numpy as np
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import librosa
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pipe = transformers.pipeline(model='fixie-ai/ultravox-v0_3', trust_remote_code=True)
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path = "<path-to-input-audio>" # TODO: pass the audio here
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audio, sr = librosa.load(path, sr=16000)
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turns = [
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{
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"role": "system",
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"content": "You are a friendly and helpful character. You love to answer questions for people."
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},
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]
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pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=30)
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```
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## Training Details
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The model uses a pre-trained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) backbone as well as the encoder part of [Whisper-small](https://huggingface.co/openai/whisper-small).
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Only the multi-modal adapter is trained, while Whisper encoder and Llama are kept frozen.
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We use a knowledge-distillation loss where Ultravox is trying to match the logits of the text-based Llama backbone.
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### Training Data
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Training dataset is a mix of ASR datasets, extended by adding a "continuation" generated by Llama 3.1 8B.
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### Training Procedure
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Supervised speech to audio finetuning. For more info, see [training code in Ultravox repo](https://github.com/fixie-ai/ultravox/blob/main/ultravox/training/train.py).
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#### Training Hyperparameters
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- **Training regime:** BF16 mixed precision training
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- **Hardward used:** 8x H100 GPUs
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#### Speeds, Sizes, Times
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The current version of Ultravox, when invoked with audio content, has a time-to-first-token (TTFT) of approximately 200ms, and a tokens-per-second rate of ~50-100 when using an A100-40GB GPU, all using a Llama 3.1 8B backbone.
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Check out the audio tab on [TheFastest.ai](https://thefastest.ai/?m=audio) for daily benchmarks and a comparison with other existing models.
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## Evaluation
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| | en_de (BLEU) | es_en (BLEU) | LibriSpeech clean.test (WER) |
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|:------------------|:-------------|:-------------|:----------------------------|
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| Ultravox v0.2 | 12.07 | 15.17 | 6.07 |
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| **Ultravox v0.3** | 22.68 | 24.10 | 6.67 |
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| Whisper-Llama3.1 | 24.89 | 28.67 | 3.4 |
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| Llama3.1 (text-only) | 31.95 | 38.28 | - |
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@ -0,0 +1,204 @@
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{"framework": "pytorch", "task": "feature-extraction", "allow_remote": true}
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||||
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||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,157 @@
|
|||
import dataclasses
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import transformers
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class LoraConfigSimplified:
|
||||
"""
|
||||
Low Rank Approximation (LoRA) configuration.
|
||||
|
||||
Used for language and audio models separately.
|
||||
"""
|
||||
|
||||
# The rank of the approximation
|
||||
r: int = 0
|
||||
lora_alpha: float = 8
|
||||
target_modules: Optional[List[str]] = dataclasses.field(
|
||||
default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
|
||||
)
|
||||
|
||||
|
||||
class LossFunction(str, Enum):
|
||||
CrossEntropy = "ce"
|
||||
KL_Divergence = "kl"
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class LossConfig:
|
||||
loss_function: LossFunction = LossFunction.KL_Divergence
|
||||
kl_temperature: float = 2.0
|
||||
|
||||
@property
|
||||
def requires_alt_fields(self):
|
||||
return self.loss_function == LossFunction.KL_Divergence
|
||||
|
||||
|
||||
class UltravoxConfig(transformers.PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`UltravoxForConditionalGeneration`]. It is used to instantiate an
|
||||
Ultravox model according to the specified arguments, defining the model architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
audio_config (`Wav2Vec2Config`, *optional*):
|
||||
Custom audio config or dict
|
||||
text_config (`Union[AutoConfig, dict]`, *optional*):
|
||||
The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
|
||||
ignore_index (`int`, *optional*, defaults to -100):
|
||||
The ignore index for the loss function.
|
||||
audio_token_index (`int`, *optional*, defaults to 32000):
|
||||
The audio token index to encode the audio prompt.
|
||||
stack_factor (`int`, *optional*, defaults to 8):
|
||||
Audio downsampling factor for the multimodal projector.
|
||||
norm_init (`float`, *optional*, defaults to 0.4):
|
||||
The initialization value for the layer normalization.
|
||||
projector_act (`str`, *optional*, defaults to `"swiglu"`):
|
||||
The activation function used by the multimodal projector.
|
||||
text_model_lora_config (`LoraConfigSimplified`, *optional*):
|
||||
The LoRA configuration for finetuning the text model.
|
||||
audio_model_lora_config (`LoraConfigSimplified`, *optional*):
|
||||
The LoRA configuration for finetuning the audio model.
|
||||
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import UltravoxForConditionalGeneration, Wav2Vec2Config, UltravoxConfig, LlamaConfig
|
||||
|
||||
>>> # Initializing an audio encoder config
|
||||
>>> audio_config = Wav2Vec2Config()
|
||||
|
||||
>>> # Initializing a Llama config
|
||||
>>> text_config = LlamaConfig()
|
||||
|
||||
>>> # Initializing a default configuration
|
||||
>>> configuration = UltravoxConfig(audio_config, text_config)
|
||||
|
||||
>>> # Initializing a completely untrained model from the configuration
|
||||
>>> model = UltravoxForConditionalGeneration(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
|
||||
>>> # Initialize a model from pretrained checkpoints and random projector weights
|
||||
>>> config = UltravoxConfig(audio_model_id="facebook/wav2vec2-base-960h", text_model_id="meta-llama/Llama-2-7b-chat-hf")
|
||||
```"""
|
||||
|
||||
model_type = "ultravox"
|
||||
is_composition = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
audio_config: Optional[Dict[str, Any]] = None,
|
||||
text_config: Optional[Dict[str, Any]] = None,
|
||||
audio_model_id: Optional[str] = None,
|
||||
text_model_id: Optional[str] = None,
|
||||
ignore_index: int = -100,
|
||||
audio_token_index: int = 32000,
|
||||
hidden_size: int = 4096,
|
||||
stack_factor: int = 8,
|
||||
norm_init: float = 0.4,
|
||||
projector_act: str = "swiglu",
|
||||
text_model_lora_config: Optional[LoraConfigSimplified] = None,
|
||||
audio_model_lora_config: Optional[LoraConfigSimplified] = None,
|
||||
**kwargs,
|
||||
):
|
||||
self.ignore_index = ignore_index
|
||||
|
||||
self.audio_model_id = audio_model_id
|
||||
self.text_model_id = text_model_id
|
||||
self.audio_token_index = audio_token_index
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
self.stack_factor = stack_factor
|
||||
self.norm_init = norm_init
|
||||
self.projector_act = projector_act
|
||||
|
||||
if text_model_id is not None:
|
||||
self.text_config: transformers.LlamaConfig = (
|
||||
transformers.AutoConfig.from_pretrained(text_model_id)
|
||||
)
|
||||
else:
|
||||
text_config = text_config or {}
|
||||
self.text_config = transformers.CONFIG_MAPPING[
|
||||
text_config.get("model_type", "llama")
|
||||
](**text_config)
|
||||
|
||||
if audio_model_id is not None:
|
||||
self.audio_config: transformers.PretrainedConfig = (
|
||||
transformers.AutoConfig.from_pretrained(audio_model_id)
|
||||
)
|
||||
else:
|
||||
audio_config = audio_config or {}
|
||||
self.audio_config = transformers.CONFIG_MAPPING[
|
||||
audio_config.get("model_type", "wav2vec2")
|
||||
](**audio_config)
|
||||
|
||||
self.text_model_lora_config = (
|
||||
text_model_lora_config
|
||||
if isinstance(text_model_lora_config, dict)
|
||||
else dataclasses.asdict(text_model_lora_config or LoraConfigSimplified())
|
||||
)
|
||||
self.audio_model_lora_config = (
|
||||
audio_model_lora_config
|
||||
if isinstance(audio_model_lora_config, dict)
|
||||
else dataclasses.asdict(audio_model_lora_config or LoraConfigSimplified())
|
||||
)
|
||||
|
||||
self.vocab_size = self.text_config.vocab_size
|
||||
|
||||
self.initializer_range = self.text_config.initializer_range
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
|
@ -0,0 +1,504 @@
|
|||
import logging
|
||||
from typing import Any, Dict, Optional, Set, Tuple, Union
|
||||
|
||||
import peft
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import transformers
|
||||
import transformers.activations
|
||||
import transformers.modeling_outputs
|
||||
import transformers.models
|
||||
|
||||
# We must use relative import in this directory to allow uploading to HF Hub
|
||||
# Even "from . import X" pattern doesn't work (undocumented and unclear why)
|
||||
from .ultravox_config import LossConfig
|
||||
from .ultravox_config import LossFunction
|
||||
from .ultravox_config import UltravoxConfig
|
||||
from .whisper_model_modified import WhisperEncoder as ModifiedWhisperEncoder
|
||||
|
||||
|
||||
class UltravoxModel(transformers.LlamaPreTrainedModel):
|
||||
"""
|
||||
The Ultravox model which consists of an audio encoder and a language model.
|
||||
|
||||
Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
|
||||
projected to the language model's embedding space using a few linear layers.
|
||||
The text is embedded by the language model as usual and then the audio and text embeddings are merged together.
|
||||
|
||||
A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.
|
||||
|
||||
Parameters:
|
||||
config: Model configuration class with all the parameters of the model.
|
||||
"""
|
||||
|
||||
config_class = UltravoxConfig
|
||||
config: UltravoxConfig # for type hinting
|
||||
_no_split_modules = ["Wav2Vec2Model", "WhisperEncoder", "LlamaDecoderLayer"]
|
||||
# We minimize the weights in state_dict in order to reduce the size of the checkpoint
|
||||
# The issue is that load_pretrained() uses state_dict() keys to know what keys are expected
|
||||
# As such we have to tell is to ignore some keys that are not always in the model
|
||||
_keys_to_ignore_on_load_unexpected = ["audio_tower.*", "language_model.*"]
|
||||
# Usually we load encoder weights from a pretrained model, so we don't want to load the decoder weights
|
||||
# Technically we never hit this issue because these keys are already removed from state_dict() however,
|
||||
# but there's no harm in keeping it here for when we change that behavior.
|
||||
_keys_to_ignore_on_load_missing = ["audio_tower.*"]
|
||||
|
||||
def __init__(self, config: UltravoxConfig):
|
||||
super().__init__(config)
|
||||
|
||||
self.keep_params: Set[str] = set()
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.audio_tower = self._create_audio_tower(config)
|
||||
self.multi_modal_projector = UltravoxProjector(config)
|
||||
self.language_model = self._create_language_model(config)
|
||||
|
||||
self.loss_config = LossConfig()
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.language_model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.language_model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.language_model.get_output_embeddings()
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.language_model.set_output_embeddings(new_embeddings)
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.language_model.set_decoder(decoder)
|
||||
|
||||
def get_decoder(self):
|
||||
return self.language_model.get_decoder()
|
||||
|
||||
def tie_weights(self):
|
||||
return self.language_model.tie_weights()
|
||||
|
||||
def set_loss_config(self, loss_config: LossConfig):
|
||||
self.loss_config = loss_config
|
||||
|
||||
def _setup_cache(
|
||||
self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
|
||||
):
|
||||
self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)
|
||||
|
||||
def _reorder_cache(self, past_key_values, beam_idx):
|
||||
return self.language_model._reorder_cache(past_key_values, beam_idx)
|
||||
|
||||
def resize_token_embeddings(
|
||||
self,
|
||||
new_num_tokens: Optional[int] = None,
|
||||
pad_to_multiple_of: Optional[int] = None,
|
||||
) -> nn.Embedding:
|
||||
model_embeds = self.language_model.resize_token_embeddings(
|
||||
new_num_tokens, pad_to_multiple_of
|
||||
)
|
||||
# update vocab size
|
||||
self.config.text_config.vocab_size = model_embeds.num_embeddings
|
||||
self.config.vocab_size = model_embeds.num_embeddings
|
||||
self.vocab_size = model_embeds.num_embeddings
|
||||
return model_embeds
|
||||
|
||||
def _compute_kl_loss(
|
||||
self,
|
||||
lm_output: transformers.modeling_outputs.CausalLMOutputWithPast,
|
||||
labels: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
|
||||
alt_input_ids: Optional[torch.Tensor] = None,
|
||||
alt_attention_mask: Optional[torch.Tensor] = None,
|
||||
alt_labels: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# disable gradient computation for the teacher model
|
||||
with torch.no_grad():
|
||||
# compute the teacher (text-only) model's distribution
|
||||
alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids)
|
||||
alt_lm_output = self.language_model.forward(
|
||||
inputs_embeds=alt_inputs_embeds,
|
||||
labels=alt_labels,
|
||||
attention_mask=alt_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
**kwargs,
|
||||
)
|
||||
# compute the KL divergence loss between the two models
|
||||
kl_loss = F.kl_div(
|
||||
F.log_softmax(
|
||||
lm_output.logits[labels != -100] / self.loss_config.kl_temperature,
|
||||
dim=-1,
|
||||
),
|
||||
F.softmax(
|
||||
alt_lm_output.logits[alt_labels != -100]
|
||||
/ self.loss_config.kl_temperature,
|
||||
dim=-1,
|
||||
),
|
||||
reduction="batchmean",
|
||||
)
|
||||
return {"loss": kl_loss}
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
audio_values: Optional[torch.FloatTensor] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
audio_token_start_idx: Optional[torch.Tensor] = None,
|
||||
audio_token_len: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
|
||||
# the alt_* fields are needed for KL divergence loss
|
||||
alt_input_ids: Optional[torch.Tensor] = None,
|
||||
alt_attention_mask: Optional[torch.Tensor] = None,
|
||||
alt_labels: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
|
||||
"""
|
||||
Forward pass for the Ultravox model.
|
||||
|
||||
`input_ids` are the tokenized text input. They are embedded by the language model as usual.
|
||||
`audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
|
||||
projected to the language model's embedding space using a few linear layers.
|
||||
The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
|
||||
of the audio embeddings in the merged embeddings.
|
||||
|
||||
Args:
|
||||
input_ids: The tokenized text input.
|
||||
audio_values: The processed audio values.
|
||||
inputs_embeds: The embeddings for the input tokens.
|
||||
labels: The tokenized text labels.
|
||||
attention_mask: The attention mask for the input.
|
||||
position_ids: The position ids for the input.
|
||||
past_key_values: The past key value cache for the language model attention layers.
|
||||
**kwargs: Additional keyword arguments. Passed directly to the language model.
|
||||
"""
|
||||
if inputs_embeds is None:
|
||||
# B x T -> B x T x D
|
||||
inputs_embeds = self.get_input_embeddings().forward(input_ids)
|
||||
|
||||
if audio_values is not None:
|
||||
assert (
|
||||
audio_token_start_idx is not None and audio_token_len is not None
|
||||
), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
|
||||
assert (
|
||||
len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
|
||||
), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."
|
||||
|
||||
# B x A/3200 x D
|
||||
audio_tower_output = self.audio_tower.forward(
|
||||
audio_values
|
||||
).last_hidden_state
|
||||
audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
|
||||
|
||||
audio_embeds = self.multi_modal_projector.forward(audio_tower_output)
|
||||
|
||||
# combine audio and text embeddings
|
||||
for i, (audio, start, length) in enumerate(
|
||||
zip(audio_embeds, audio_token_start_idx, audio_token_len)
|
||||
):
|
||||
length = min(length, audio.shape[0])
|
||||
inputs_embeds[i, start : start + length] = audio[:length]
|
||||
|
||||
lm_output = self.language_model.forward(
|
||||
inputs_embeds=inputs_embeds,
|
||||
labels=labels,
|
||||
attention_mask=attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
**kwargs,
|
||||
)
|
||||
if self.training:
|
||||
if self.loss_config.loss_function == LossFunction.CrossEntropy:
|
||||
return lm_output
|
||||
elif self.loss_config.loss_function == LossFunction.KL_Divergence:
|
||||
return self._compute_kl_loss(
|
||||
lm_output=lm_output,
|
||||
labels=labels,
|
||||
past_key_values=past_key_values,
|
||||
alt_input_ids=alt_input_ids,
|
||||
alt_attention_mask=alt_attention_mask,
|
||||
alt_labels=alt_labels,
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported loss function: {self.loss_config.loss_function}"
|
||||
)
|
||||
else:
|
||||
return lm_output
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
audio_values: Optional[torch.FloatTensor] = None,
|
||||
audio_token_start_idx: Optional[torch.Tensor] = None,
|
||||
audio_token_len: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
) -> Dict[str, Any]:
|
||||
model_input = self.language_model.prepare_inputs_for_generation(
|
||||
input_ids=input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if is_cache_empty(past_key_values) and audio_values is not None:
|
||||
# We only want to use audio features in the 1st generation step
|
||||
model_input["audio_values"] = audio_values
|
||||
model_input["audio_token_start_idx"] = audio_token_start_idx
|
||||
model_input["audio_token_len"] = audio_token_len
|
||||
|
||||
return model_input
|
||||
|
||||
@classmethod
|
||||
def _create_audio_tower(
|
||||
cls, config: UltravoxConfig
|
||||
) -> Union[transformers.Wav2Vec2Model, ModifiedWhisperEncoder]:
|
||||
if config.audio_model_id is not None:
|
||||
if "whisper" in config.audio_model_id is not None:
|
||||
audio_tower = ModifiedWhisperEncoder.from_pretrained(
|
||||
config.audio_model_id
|
||||
)
|
||||
else:
|
||||
audio_tower = transformers.AutoModel.from_pretrained(
|
||||
config.audio_model_id
|
||||
)
|
||||
else:
|
||||
if "whisper" in config.audio_config._name_or_path:
|
||||
audio_tower = ModifiedWhisperEncoder(config.audio_config)
|
||||
else:
|
||||
with transformers.modeling_utils.no_init_weights():
|
||||
# we only ever use from_config if the weights are retrained, hence initializing is not
|
||||
# required. This makes the model quite creation faster since init on CPU is quite slow.
|
||||
audio_tower = transformers.AutoModel.from_config(
|
||||
config.audio_config
|
||||
)
|
||||
|
||||
if isinstance(
|
||||
audio_tower,
|
||||
(transformers.Wav2Vec2BertModel, transformers.WhisperModel),
|
||||
):
|
||||
# For these models we only need the encoder part
|
||||
# Wav2Vec2BertModel -> Wav2Vec2BertEncoder
|
||||
# WhisperModel -> WhisperEncoder
|
||||
audio_tower = audio_tower.encoder
|
||||
|
||||
audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
|
||||
return audio_tower
|
||||
|
||||
@classmethod
|
||||
def _create_language_model(
|
||||
cls, config: UltravoxConfig
|
||||
) -> transformers.LlamaForCausalLM:
|
||||
if config.text_model_id is not None:
|
||||
language_model = transformers.AutoModelForCausalLM.from_pretrained(
|
||||
config.text_model_id, attn_implementation=config._attn_implementation
|
||||
)
|
||||
else:
|
||||
with transformers.modeling_utils.no_init_weights():
|
||||
# we only ever use from_config if the weights are retrained, hence initializing is not
|
||||
# required. This makes the model quite creation faster since init on CPU is quite slow.
|
||||
language_model = transformers.AutoModelForCausalLM.from_config(
|
||||
config.text_config, attn_implementation=config._attn_implementation
|
||||
)
|
||||
|
||||
language_model = apply_lora(language_model, config.text_model_lora_config)
|
||||
return language_model
|
||||
|
||||
def _add_language_model_weights_to_keep(self):
|
||||
if self.config.text_model_id is not None:
|
||||
self.config.text_model_id = None
|
||||
self.keep_params.update(
|
||||
set(
|
||||
[
|
||||
f"language_model.{name}"
|
||||
for name, _ in self.language_model.named_parameters()
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
def _add_audio_tower_weights_to_keep(self):
|
||||
if self.config.audio_model_id is not None:
|
||||
self.config.audio_model_id = None
|
||||
self.keep_params.update(
|
||||
set(
|
||||
[
|
||||
f"audio_tower.{name}"
|
||||
for name, _ in self.audio_tower.named_parameters()
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
def merge_and_unload(self):
|
||||
if isinstance(self.language_model, peft.PeftModel):
|
||||
self.language_model = self.language_model.merge_and_unload()
|
||||
# no need to download base language model weights anymore, so we can remove the id
|
||||
self._add_language_model_weights_to_keep()
|
||||
|
||||
if isinstance(self.audio_tower, peft.PeftModel):
|
||||
self.audio_tower = self.audio_tower.merge_and_unload()
|
||||
# no need to download base audio model weights anymore, so we can remove the id
|
||||
self._add_audio_tower_weights_to_keep()
|
||||
|
||||
for param in ["text_model_lora_config", "audio_model_lora_config"]:
|
||||
if hasattr(self.config, param):
|
||||
delattr(self.config, param)
|
||||
|
||||
def push_to_hub(self, *args, **kwargs):
|
||||
self.merge_and_unload()
|
||||
self.to(self.language_model.dtype)
|
||||
return super().push_to_hub(*args, **kwargs)
|
||||
|
||||
def state_dict(self, *args, **kwargs):
|
||||
named_params = dict(self.named_parameters())
|
||||
state_dict = super().state_dict(*args, **kwargs)
|
||||
|
||||
state_dict = {
|
||||
k: v
|
||||
for k, v in state_dict.items()
|
||||
if k in self.keep_params
|
||||
or (k in named_params and named_params[k].requires_grad)
|
||||
}
|
||||
return state_dict
|
||||
|
||||
def load_state_dict(
|
||||
self,
|
||||
state_dict: Dict[str, Any],
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
self.keep_params.update(set(state_dict.keys()))
|
||||
return super().load_state_dict(state_dict, *args, **kwargs)
|
||||
|
||||
def print_trainable_parameters(self):
|
||||
"""
|
||||
Prints the number of trainable parameters in the model (reuses Peft model's method)
|
||||
"""
|
||||
count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
|
||||
|
||||
trainable_params, all_param = count_params(self)
|
||||
|
||||
logging.info(
|
||||
f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
|
||||
f" || trainable%: {100 * trainable_params / all_param:.1f}%"
|
||||
)
|
||||
|
||||
lm_trainable_params, lm_all_params = count_params(self.language_model)
|
||||
audio_trainable_params, audio_all_params = count_params(self.audio_tower)
|
||||
|
||||
projector_trainable_params = (
|
||||
trainable_params - lm_trainable_params - audio_trainable_params
|
||||
)
|
||||
projector_all_params = all_param - lm_all_params - audio_all_params
|
||||
|
||||
logging.info(
|
||||
f"Trainable%: "
|
||||
f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
|
||||
f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
|
||||
f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
|
||||
)
|
||||
|
||||
|
||||
def is_cache_empty(
|
||||
past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]]
|
||||
) -> bool:
|
||||
"""
|
||||
Check if the cache is empty.
|
||||
"""
|
||||
if past_key_values is None:
|
||||
return True
|
||||
if isinstance(past_key_values, tuple):
|
||||
return all(len(c) == 0 for c in past_key_values)
|
||||
return past_key_values.get_seq_length() == 0
|
||||
|
||||
|
||||
def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
|
||||
"""
|
||||
Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
|
||||
"""
|
||||
lora_config = peft.LoraConfig(**lora_config or {})
|
||||
|
||||
if lora_config.r == 0:
|
||||
# freeze the model entirely
|
||||
for param in model.parameters():
|
||||
param.requires_grad = False
|
||||
else:
|
||||
model = peft.get_peft_model(model, lora_config)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
class StackAudioFrames(nn.Module):
|
||||
"""
|
||||
Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.
|
||||
|
||||
The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
|
||||
NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
|
||||
we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
|
||||
In most cases this extra padding will get removed in the model's forward function so it has no effect.
|
||||
"""
|
||||
|
||||
def __init__(self, stack_factor: int = 8):
|
||||
super().__init__()
|
||||
self.stack_factor = stack_factor
|
||||
|
||||
def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
|
||||
B, T, C = audio_embeds.shape
|
||||
T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
|
||||
audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
|
||||
B, T, C = audio_embeds.shape
|
||||
audio_embeds = audio_embeds.view(
|
||||
B, T // self.stack_factor, C * self.stack_factor
|
||||
)
|
||||
return audio_embeds
|
||||
|
||||
|
||||
class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
|
||||
def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
|
||||
super().__init__(hidden_size=hidden_size, eps=eps)
|
||||
self.weight.data.fill_(init)
|
||||
|
||||
|
||||
class SwiGLU(nn.Module):
|
||||
def forward(self, x):
|
||||
x, gate = x.chunk(2, dim=-1)
|
||||
return F.silu(gate) * x
|
||||
|
||||
|
||||
class UltravoxProjector(nn.Sequential):
|
||||
def __init__(self, config: UltravoxConfig):
|
||||
super().__init__()
|
||||
self.hidden_dim = config.hidden_size
|
||||
self._pad_and_stack = StackAudioFrames(config.stack_factor)
|
||||
dim = config.audio_config.hidden_size * config.stack_factor
|
||||
self.ln_pre = RMSNorm(dim, init=config.norm_init)
|
||||
self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
|
||||
dim = self.hidden_dim
|
||||
self.act = transformers.activations.get_activation(config.projector_act)
|
||||
dim = dim // 2 if config.projector_act == "swiglu" else dim
|
||||
self.linear_2 = nn.Linear(dim, config.text_config.hidden_size, bias=False)
|
||||
self.ln_post = RMSNorm(config.text_config.hidden_size, init=config.norm_init)
|
||||
|
||||
def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
|
||||
audio_features = self._pad_and_stack(audio_features)
|
||||
audio_features = self.ln_pre(audio_features)
|
||||
hidden_states = self.linear_1(audio_features)
|
||||
hidden_states = self.act(hidden_states)
|
||||
hidden_states = self.linear_2(hidden_states)
|
||||
hidden_states = self.ln_post(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
UltravoxConfig.register_for_auto_class()
|
||||
UltravoxModel.register_for_auto_class()
|
||||
|
||||
transformers.AutoConfig.register("ultravox", UltravoxConfig)
|
||||
transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
|
||||
# transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor) # TODO: make processor work standalone
|
||||
|
||||
transformers.activations.ACT2FN["swiglu"] = SwiGLU
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
import logging
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
import transformers
|
||||
|
||||
# We must use relative import in this directory to allow uploading to HF Hub
|
||||
# Even "from . import X" pattern doesn't work (undocumented and unclear why)
|
||||
from .ultravox_model import UltravoxModel
|
||||
from .ultravox_processing import UltravoxProcessor
|
||||
|
||||
|
||||
class UltravoxPipeline(transformers.Pipeline):
|
||||
def __init__(
|
||||
self,
|
||||
model: UltravoxModel,
|
||||
tokenizer: Optional[transformers.PreTrainedTokenizerBase] = None,
|
||||
audio_processor: Optional[transformers.ProcessorMixin] = None,
|
||||
**kwargs
|
||||
):
|
||||
if tokenizer is None:
|
||||
try:
|
||||
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
||||
model.config._name_or_path
|
||||
)
|
||||
except:
|
||||
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
||||
model.config.text_model_id or model.config.text_config._name_or_path
|
||||
)
|
||||
|
||||
if audio_processor is None:
|
||||
audio_processor = transformers.AutoProcessor.from_pretrained(
|
||||
model.config.audio_model_id or model.config.audio_config._name_or_path
|
||||
)
|
||||
|
||||
super().__init__(model=model, tokenizer=tokenizer, **kwargs)
|
||||
|
||||
self.processor = UltravoxProcessor(
|
||||
audio_processor=audio_processor,
|
||||
tokenizer=tokenizer,
|
||||
stack_factor=model.config.stack_factor,
|
||||
)
|
||||
|
||||
def _sanitize_parameters(self, **kwargs):
|
||||
generation_keys = ["temperature", "max_new_tokens", "repetition_penalty"]
|
||||
generation_kwargs = {k: kwargs[k] for k in kwargs if k in generation_keys}
|
||||
return {}, generation_kwargs, {}
|
||||
|
||||
def preprocess(self, inputs: Dict[str, Any]):
|
||||
turns: list = inputs.get("turns", [])
|
||||
|
||||
audio = inputs.get("audio", None)
|
||||
# Convert to float32 if needed.
|
||||
if isinstance(audio, np.ndarray):
|
||||
if audio.dtype == np.float64:
|
||||
audio = audio.astype(np.float32)
|
||||
elif audio.dtype == np.int16:
|
||||
audio = audio.astype(np.float32) / np.float32(32768.0)
|
||||
elif audio.dtype == np.int32:
|
||||
audio = audio.astype(np.float32) / np.float32(2147483648.0)
|
||||
|
||||
if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
|
||||
prompt = inputs.get("prompt", "<|audio|>")
|
||||
if "<|audio|>" not in prompt:
|
||||
logging.warning(
|
||||
"Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
|
||||
)
|
||||
|
||||
prompt += " <|audio|>"
|
||||
turns.append({"role": "user", "content": prompt})
|
||||
|
||||
text = self.processor.tokenizer.apply_chat_template(
|
||||
turns, add_generation_prompt=True, tokenize=False
|
||||
)
|
||||
|
||||
if "sampling_rate" not in inputs and audio is not None:
|
||||
logging.warning(
|
||||
"No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
|
||||
)
|
||||
|
||||
output = self.processor(
|
||||
text=text,
|
||||
audio=audio,
|
||||
sampling_rate=inputs.get("sampling_rate", 16000),
|
||||
)
|
||||
if "audio_values" in output:
|
||||
output["audio_values"] = output["audio_values"].to(self.model.dtype)
|
||||
|
||||
return output
|
||||
|
||||
def _forward(
|
||||
self,
|
||||
model_inputs: Dict[str, Any],
|
||||
temperature: Optional[float] = None,
|
||||
max_new_tokens: Optional[int] = None,
|
||||
repetition_penalty: float = 1.1,
|
||||
) -> List[int]:
|
||||
temperature = temperature or None
|
||||
do_sample = temperature is not None
|
||||
|
||||
terminators = [self.tokenizer.eos_token_id]
|
||||
if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
|
||||
terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
|
||||
|
||||
input_len = model_inputs["input_ids"].shape[1]
|
||||
|
||||
outputs = self.model.generate(
|
||||
**model_inputs,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
max_new_tokens=max_new_tokens,
|
||||
repetition_penalty=repetition_penalty,
|
||||
eos_token_id=terminators
|
||||
)
|
||||
return outputs[0][input_len:]
|
||||
|
||||
def postprocess(self, model_outputs) -> str:
|
||||
output_text = self.tokenizer.decode(model_outputs, skip_special_tokens=True)
|
||||
return output_text
|
||||
|
||||
|
||||
transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
|
||||
"ultravox-pipeline",
|
||||
pipeline_class=UltravoxPipeline,
|
||||
pt_model=transformers.AutoModel,
|
||||
type="multimodal",
|
||||
)
|
||||
|
|
@ -0,0 +1,205 @@
|
|||
from typing import Optional, Union, Dict, Any
|
||||
|
||||
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."
|
||||
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))
|
||||
|
||||
|
||||
transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor)
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
# modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
|
||||
# see this issue for the commentary: https://github.com/huggingface/transformers/issues/25744
|
||||
#
|
||||
# Copyright 2022 The OpenAI Authors and 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.
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import transformers
|
||||
import transformers.modeling_outputs
|
||||
from transformers.models.whisper import modeling_whisper as whisper
|
||||
|
||||
|
||||
class WhisperEncoder(whisper.WhisperEncoder):
|
||||
"""
|
||||
Encoder portion of OpenAI's Whisper model.
|
||||
|
||||
This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
|
||||
1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
|
||||
2. allow less than 30 second of audio padding to be passed in:
|
||||
- relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
|
||||
- embed_pos is now sliced to match the length of `inputs_embeds`
|
||||
|
||||
Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
|
||||
"""
|
||||
|
||||
base_model_prefix = "model.encoder"
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_features,
|
||||
attention_mask=None,
|
||||
head_mask=None,
|
||||
output_attentions=None,
|
||||
output_hidden_states=None,
|
||||
return_dict=None,
|
||||
):
|
||||
expected_seq_length = (
|
||||
self.config.max_source_positions
|
||||
* self.conv1.stride[0]
|
||||
* self.conv2.stride[0]
|
||||
)
|
||||
if input_features.shape[-1] > expected_seq_length:
|
||||
raise ValueError(
|
||||
f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
|
||||
)
|
||||
|
||||
output_attentions = (
|
||||
output_attentions
|
||||
if output_attentions is not None
|
||||
else self.config.output_attentions
|
||||
)
|
||||
output_hidden_states = (
|
||||
output_hidden_states
|
||||
if output_hidden_states is not None
|
||||
else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = (
|
||||
return_dict if return_dict is not None else self.config.use_return_dict
|
||||
)
|
||||
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
|
||||
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
|
||||
|
||||
inputs_embeds = inputs_embeds.permute(0, 2, 1)
|
||||
embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
|
||||
|
||||
hidden_states = inputs_embeds + embed_pos
|
||||
hidden_states = nn.functional.dropout(
|
||||
hidden_states, p=self.dropout, training=self.training
|
||||
)
|
||||
|
||||
encoder_states = () if output_hidden_states else None
|
||||
all_attentions = () if output_attentions else None
|
||||
|
||||
# check if head_mask has a correct number of layers specified if desired
|
||||
if head_mask is not None:
|
||||
assert head_mask.size()[0] == (
|
||||
len(self.layers)
|
||||
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
|
||||
|
||||
for idx, encoder_layer in enumerate(self.layers):
|
||||
if output_hidden_states:
|
||||
encoder_states = encoder_states + (hidden_states,)
|
||||
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
||||
to_drop = False
|
||||
if self.training:
|
||||
dropout_probability = torch.rand([])
|
||||
if dropout_probability < self.layerdrop: # skip the layer
|
||||
to_drop = True
|
||||
|
||||
if to_drop:
|
||||
layer_outputs = (None, None)
|
||||
else:
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
encoder_layer.__call__,
|
||||
hidden_states,
|
||||
None,
|
||||
(head_mask[idx] if head_mask is not None else None),
|
||||
output_attentions,
|
||||
)
|
||||
else:
|
||||
layer_outputs = encoder_layer(
|
||||
hidden_states,
|
||||
None,
|
||||
layer_head_mask=(
|
||||
head_mask[idx] if head_mask is not None else None
|
||||
),
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_attentions = all_attentions + (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
if output_hidden_states:
|
||||
encoder_states = encoder_states + (hidden_states,)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v
|
||||
for v in [hidden_states, encoder_states, all_attentions]
|
||||
if v is not None
|
||||
)
|
||||
return transformers.modeling_outputs.BaseModelOutput(
|
||||
last_hidden_state=hidden_states,
|
||||
hidden_states=encoder_states,
|
||||
attentions=all_attentions,
|
||||
)
|
||||
Loading…
Reference in New Issue