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+ ---
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+ language:
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+ - ar
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+ - de
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+ - en
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+ - es
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+ - fr
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+ - hi
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+ - it
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+ - ja
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+ - nl
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+ - pt
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+ - ru
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+ - sv
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+ - tr
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+ - uk
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+ - zh
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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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+ - fixie-ai/peoples_speech
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+ - fixie-ai/gigaspeech
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+ - fixie-ai/multilingual_librispeech
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+ - fixie-ai/wenetspeech
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+ - fixie-ai/covost2
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+ metrics:
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+ - bleu
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+ ---
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+
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+ # Model Card for Ultravox
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+
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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-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) backbone.
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+
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+ See https://ultravox.ai for the GitHub repo and more information.
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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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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+
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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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+
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+ - **Developed by:** Fixie.ai
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+ - **License:** MIT
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+
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+ ### Model Sources
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+
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+ - **Repository:** https://ultravox.ai
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+ - **Demo:** See repo
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+
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+ ## Usage
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+
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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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+
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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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+
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+ import transformers
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+ import numpy as np
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+ import librosa
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+
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+ pipe = transformers.pipeline(model='fixie-ai/ultravox-v0_4_1-llama-3_1-8b', trust_remote_code=True)
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+
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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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+
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+
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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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+
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+
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+ ## Training Details
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+
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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-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo).
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+
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+ Only the multi-modal adapter is trained, while Whisper encoder and Llama are kept frozen.
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+
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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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+
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+ ### Training Data
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+
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+ The training dataset is a mix of ASR datasets, extended with continuations generated by Llama 3.1 8B, and speech translation datasets, which yield a modest improvement in translation evaluations.
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+
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+ ### Training Procedure
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+
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+ Supervised speech instruction finetuning via knowledge-distillation. 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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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** BF16 mixed precision training
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+ - **Hardward used:** 8x H100 GPUs
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+
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+ #### Speeds, Sizes, Times
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+
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+ The current version of Ultravox, when invoked with audio content, has a time-to-first-token (TTFT) of approximately 150ms, 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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+
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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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+
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+ ## Evaluation
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+
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+ | | Ultravox 0.4 8B | **Ultravox 0.4.1 8B** |
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+ | --- | ---: | ---: |
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+ | **en_ar** | 11.17 | 12.28 |
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+ | **en_de** | 25.47 | 27.13 |
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+ | **es_en** | 37.11 | 39.16 |
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+ | **ru_en** | 38.96 | 39.65 |
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+ | **en_ca** | 27.46 | 29.94 |
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+ | **zh_en** | 10.08 | 14.55 |
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+ "content": "<|reserved_special_token_245|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "pad_token": "<|eot_id|>",
2062
+ "tokenizer_class": "PreTrainedTokenizerFast"
2063
+ }
ultravox_config.py ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dataclasses
2
+ from enum import Enum
3
+ from typing import Any, Dict, List, Optional
4
+
5
+ import transformers
6
+
7
+
8
+ @dataclasses.dataclass
9
+ class LoraConfigSimplified:
10
+ """
11
+ Low Rank Approximation (LoRA) configuration.
12
+
13
+ Used for language and audio models separately.
14
+ """
15
+
16
+ # The rank of the approximation
17
+ r: int = 0
18
+ lora_alpha: float = 8
19
+ target_modules: Optional[List[str]] = dataclasses.field(
20
+ default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
21
+ )
22
+ # A list of module names regex patterns to unfreeze. Only used if r == 0.
23
+ unfreeze_layers: Optional[List[str]] = None
24
+
25
+
26
+ class LossFunction(str, Enum):
27
+ CrossEntropy = "ce"
28
+ KL_Divergence = "kl"
29
+
30
+
31
+ @dataclasses.dataclass
32
+ class LossConfig:
33
+ loss_function: LossFunction = LossFunction.CrossEntropy
34
+ kl_temperature: float = 2.0
35
+
36
+ @property
37
+ def requires_alt_fields(self):
38
+ return self.loss_function == LossFunction.KL_Divergence
39
+
40
+
41
+ class UltravoxConfig(transformers.PretrainedConfig):
42
+ r"""
43
+ This is the configuration class to store the configuration of a [`UltravoxForConditionalGeneration`]. It is used to instantiate an
44
+ Ultravox model according to the specified arguments, defining the model architecture.
45
+
46
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
47
+ documentation from [`PretrainedConfig`] for more information.
48
+
49
+ Args:
50
+ audio_config (`Wav2Vec2Config`, *optional*):
51
+ Custom audio config or dict
52
+ text_config (`Union[AutoConfig, dict]`, *optional*):
53
+ The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
54
+ ignore_index (`int`, *optional*, defaults to -100):
55
+ The ignore index for the loss function.
56
+ audio_token_index (`int`, *optional*, defaults to 32000):
57
+ The audio token index to encode the audio prompt.
58
+ stack_factor (`int`, *optional*, defaults to 8):
59
+ Audio downsampling factor for the multimodal projector.
60
+ norm_init (`float`, *optional*, defaults to 0.4):
61
+ The initialization value for the layer normalization.
62
+ projector_act (`str`, *optional*, defaults to `"swiglu"`):
63
+ The activation function used by the multimodal projector.
64
+ text_model_lora_config (`LoraConfigSimplified`, *optional*):
65
+ The LoRA configuration for finetuning the text model.
66
+ audio_model_lora_config (`LoraConfigSimplified`, *optional*):
67
+ The LoRA configuration for finetuning the audio model.
68
+
69
+
70
+ Example:
71
+
72
+ ```python
73
+ >>> from transformers import UltravoxForConditionalGeneration, Wav2Vec2Config, UltravoxConfig, LlamaConfig
74
+
75
+ >>> # Initializing an audio encoder config
76
+ >>> audio_config = Wav2Vec2Config()
77
+
78
+ >>> # Initializing a Llama config
79
+ >>> text_config = LlamaConfig()
80
+
81
+ >>> # Initializing a default configuration
82
+ >>> configuration = UltravoxConfig(audio_config, text_config)
83
+
84
+ >>> # Initializing a completely untrained model from the configuration
85
+ >>> model = UltravoxForConditionalGeneration(configuration)
86
+
87
+ >>> # Accessing the model configuration
88
+ >>> configuration = model.config
89
+
90
+ >>> # Initialize a model from pretrained checkpoints and random projector weights
91
+ >>> config = UltravoxConfig(audio_model_id="facebook/wav2vec2-base-960h", text_model_id="meta-llama/Llama-2-7b-chat-hf")
92
+ ```"""
93
+
94
+ model_type = "ultravox"
95
+ is_composition = False
96
+
97
+ def __init__(
98
+ self,
99
+ audio_config: Optional[Dict[str, Any]] = None,
100
+ text_config: Optional[Dict[str, Any]] = None,
101
+ audio_model_id: Optional[str] = None,
102
+ text_model_id: Optional[str] = None,
103
+ ignore_index: int = -100,
104
+ hidden_size: int = 4096,
105
+ stack_factor: int = 8,
106
+ norm_init: float = 0.4,
107
+ projector_act: str = "swiglu",
108
+ text_model_lora_config: Optional[LoraConfigSimplified] = None,
109
+ audio_model_lora_config: Optional[LoraConfigSimplified] = None,
110
+ **kwargs,
111
+ ):
112
+ self.ignore_index = ignore_index
113
+
114
+ self.audio_model_id = audio_model_id
115
+ self.text_model_id = text_model_id
116
+
117
+ self.hidden_size = hidden_size
118
+ self.stack_factor = stack_factor
119
+ self.norm_init = norm_init
120
+ self.projector_act = projector_act
121
+
122
+ if text_model_id is not None:
123
+ self.text_config: transformers.LlamaConfig = (
124
+ transformers.AutoConfig.from_pretrained(text_model_id)
125
+ )
126
+ else:
127
+ text_config = text_config or {}
128
+ self.text_config = transformers.CONFIG_MAPPING[
129
+ text_config.get("model_type", "llama")
130
+ ](**text_config)
131
+
132
+ if audio_model_id is not None:
133
+ self.audio_config: transformers.PretrainedConfig = (
134
+ transformers.AutoConfig.from_pretrained(audio_model_id)
135
+ )
136
+ else:
137
+ audio_config = audio_config or {}
138
+ self.audio_config = transformers.CONFIG_MAPPING[
139
+ audio_config.get("model_type", "wav2vec2")
140
+ ](**audio_config)
141
+
142
+ self.text_model_lora_config = (
143
+ text_model_lora_config
144
+ if isinstance(text_model_lora_config, dict)
145
+ else dataclasses.asdict(text_model_lora_config or LoraConfigSimplified())
146
+ )
147
+ self.audio_model_lora_config = (
148
+ audio_model_lora_config
149
+ if isinstance(audio_model_lora_config, dict)
150
+ else dataclasses.asdict(audio_model_lora_config or LoraConfigSimplified())
151
+ )
152
+
153
+ self.vocab_size = self.text_config.vocab_size
154
+
155
+ self.initializer_range = self.text_config.initializer_range
156
+
157
+ super().__init__(**kwargs)
158
+
159
+ def to_diff_dict(self) -> Dict[str, Any]:
160
+ diff_dict = super().to_diff_dict()
161
+
162
+ # remove text_config and audio_config if text_model_id and audio_model_id are present
163
+ if self.text_model_id is not None:
164
+ diff_dict.pop("text_config", None)
165
+ if self.audio_model_id is not None:
166
+ diff_dict.pop("audio_config", None)
167
+
168
+ return diff_dict
ultravox_model.py ADDED
@@ -0,0 +1,651 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import re
3
+ from typing import Any, Dict, Optional, Set, Tuple, Union
4
+
5
+ import peft
6
+ import torch
7
+ import torch.nn as nn
8
+ import torch.nn.functional as F
9
+ import transformers
10
+ import transformers.activations
11
+ import transformers.modeling_outputs
12
+ import transformers.models
13
+ from transformers.models.whisper import modeling_whisper as whisper
14
+
15
+ # We must use relative import in this directory to allow uploading to HF Hub
16
+ # Even "from . import X" pattern doesn't work (undocumented and unclear why)
17
+ from .ultravox_config import LossConfig
18
+ from .ultravox_config import LossFunction
19
+ from .ultravox_config import UltravoxConfig
20
+
21
+
22
+ class UltravoxModel(transformers.LlamaPreTrainedModel):
23
+ """
24
+ The Ultravox model which consists of an audio encoder and a language model.
25
+
26
+ Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
27
+ projected to the language model's embedding space using a few linear layers.
28
+ The text is embedded by the language model as usual and then the audio and text embeddings are merged together.
29
+
30
+ A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.
31
+
32
+ Parameters:
33
+ config: Model configuration class with all the parameters of the model.
34
+ """
35
+
36
+ config_class = UltravoxConfig
37
+ config: UltravoxConfig # for type hinting
38
+ # We minimize the weights in state_dict in order to reduce the size of the checkpoint
39
+ # The issue is that load_pretrained() uses state_dict() keys to know what keys are expected
40
+ # As such we have to tell is to ignore some keys that are not always in the model
41
+ _keys_to_ignore_on_load_unexpected = ["audio_tower.*", "language_model.*"]
42
+ # Usually we load encoder weights from a pretrained model, so we don't want to load the decoder weights
43
+ # Technically we never hit this issue because these keys are already removed from state_dict() however,
44
+ # but there's no harm in keeping it here for when we change that behavior.
45
+ _keys_to_ignore_on_load_missing = ["audio_tower.*"]
46
+
47
+ def __init__(self, config: UltravoxConfig):
48
+ super().__init__(config)
49
+ self._register_load_state_dict_pre_hook(self._pre_load_state_dict_hook)
50
+
51
+ self.keep_params: Set[str] = set()
52
+ self.vocab_size = config.vocab_size
53
+
54
+ self.audio_tower = self._create_audio_tower(config)
55
+ self.multi_modal_projector = self._create_multi_modal_projector(config)
56
+ self.language_model = self._create_language_model(config)
57
+
58
+ # Determine no_split_modules dynamically to use with FSDP auto_wrap policy.
59
+ # FSDP throws an error if some of the layer types are not found in the model.
60
+ # This would be something like ["LlamaDecoderLayer", "WhisperEncoderLayer"]
61
+ self._no_split_modules = (self.language_model._no_split_modules or []) + (
62
+ self.audio_tower._no_split_modules or []
63
+ )
64
+
65
+ self.loss_config = LossConfig()
66
+ self.post_init()
67
+
68
+ def get_input_embeddings(self):
69
+ return self.language_model.get_input_embeddings()
70
+
71
+ def set_input_embeddings(self, value):
72
+ self.language_model.set_input_embeddings(value)
73
+
74
+ def get_output_embeddings(self):
75
+ return self.language_model.get_output_embeddings()
76
+
77
+ def set_output_embeddings(self, new_embeddings):
78
+ self.language_model.set_output_embeddings(new_embeddings)
79
+
80
+ def set_decoder(self, decoder):
81
+ self.language_model.set_decoder(decoder)
82
+
83
+ def get_decoder(self):
84
+ return self.language_model.get_decoder()
85
+
86
+ def tie_weights(self):
87
+ return self.language_model.tie_weights()
88
+
89
+ def set_loss_config(self, loss_config: LossConfig):
90
+ self.loss_config = loss_config
91
+
92
+ def _setup_cache(
93
+ self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
94
+ ):
95
+ self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)
96
+
97
+ def _reorder_cache(self, past_key_values, beam_idx):
98
+ return self.language_model._reorder_cache(past_key_values, beam_idx)
99
+
100
+ def resize_token_embeddings(
101
+ self,
102
+ new_num_tokens: Optional[int] = None,
103
+ pad_to_multiple_of: Optional[int] = None,
104
+ ) -> nn.Embedding:
105
+ model_embeds = self.language_model.resize_token_embeddings(
106
+ new_num_tokens, pad_to_multiple_of
107
+ )
108
+ # update vocab size
109
+ self.config.text_config.vocab_size = model_embeds.num_embeddings
110
+ self.config.vocab_size = model_embeds.num_embeddings
111
+ self.vocab_size = model_embeds.num_embeddings
112
+ return model_embeds
113
+
114
+ def _compute_kl_loss(
115
+ self,
116
+ lm_output: transformers.modeling_outputs.CausalLMOutputWithPast,
117
+ labels: Optional[torch.Tensor] = None,
118
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
119
+ alt_input_ids: Optional[torch.Tensor] = None,
120
+ alt_attention_mask: Optional[torch.Tensor] = None,
121
+ alt_labels: Optional[torch.Tensor] = None,
122
+ **kwargs,
123
+ ):
124
+ # disable gradient computation for the teacher model
125
+ with torch.no_grad():
126
+ # compute the teacher (text-only) model's distribution
127
+ alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids)
128
+ alt_lm_output = self.language_model.forward(
129
+ inputs_embeds=alt_inputs_embeds,
130
+ labels=alt_labels,
131
+ attention_mask=alt_attention_mask,
132
+ past_key_values=past_key_values,
133
+ **kwargs,
134
+ )
135
+ # compute the KL divergence loss between the two models
136
+ kl_loss = F.kl_div(
137
+ F.log_softmax(
138
+ lm_output.logits[labels != -100] / self.loss_config.kl_temperature,
139
+ dim=-1,
140
+ ),
141
+ F.softmax(
142
+ alt_lm_output.logits[alt_labels != -100]
143
+ / self.loss_config.kl_temperature,
144
+ dim=-1,
145
+ ),
146
+ reduction="batchmean",
147
+ )
148
+ return {"loss": kl_loss}
149
+
150
+ def forward(
151
+ self,
152
+ input_ids: torch.Tensor,
153
+ audio_values: Optional[torch.FloatTensor] = None,
154
+ inputs_embeds: Optional[torch.FloatTensor] = None,
155
+ labels: Optional[torch.Tensor] = None,
156
+ attention_mask: Optional[torch.Tensor] = None,
157
+ audio_token_start_idx: Optional[torch.Tensor] = None,
158
+ audio_token_len: Optional[torch.Tensor] = None,
159
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
160
+ # the alt_* fields are needed for KL divergence loss
161
+ alt_input_ids: Optional[torch.Tensor] = None,
162
+ alt_attention_mask: Optional[torch.Tensor] = None,
163
+ alt_labels: Optional[torch.Tensor] = None,
164
+ **kwargs,
165
+ ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
166
+ """
167
+ Forward pass for the Ultravox model.
168
+
169
+ `input_ids` are the tokenized text input. They are embedded by the language model as usual.
170
+ `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
171
+ projected to the language model's embedding space using a few linear layers.
172
+ The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
173
+ of the audio embeddings in the merged embeddings.
174
+
175
+ Args:
176
+ input_ids: The tokenized text input.
177
+ audio_values: The processed audio values.
178
+ inputs_embeds: The embeddings for the input tokens.
179
+ labels: The tokenized text labels.
180
+ attention_mask: The attention mask for the input.
181
+ position_ids: The position ids for the input.
182
+ past_key_values: The past key value cache for the language model attention layers.
183
+ **kwargs: Additional keyword arguments. Passed directly to the language model.
184
+ """
185
+ if inputs_embeds is None:
186
+ # B x T -> B x T x D
187
+ inputs_embeds = self.get_input_embeddings().forward(input_ids)
188
+
189
+ if audio_values is not None:
190
+ assert (
191
+ audio_token_start_idx is not None and audio_token_len is not None
192
+ ), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
193
+ assert (
194
+ len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
195
+ ), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."
196
+
197
+ # B x A/3200 x D
198
+ audio_tower_output = self.audio_tower.forward(
199
+ audio_values.to(self.audio_tower.dtype)
200
+ ).last_hidden_state
201
+ audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
202
+
203
+ audio_embeds = self.multi_modal_projector.forward(audio_tower_output)
204
+
205
+ # combine audio and text embeddings
206
+ for i, (audio, start, length) in enumerate(
207
+ zip(audio_embeds, audio_token_start_idx, audio_token_len)
208
+ ):
209
+ length = min(length, audio.shape[0])
210
+ inputs_embeds[i, start : start + length] = audio[:length]
211
+
212
+ lm_output = self.language_model.forward(
213
+ inputs_embeds=inputs_embeds,
214
+ labels=labels,
215
+ attention_mask=attention_mask,
216
+ past_key_values=past_key_values,
217
+ **kwargs,
218
+ )
219
+ if self.training:
220
+ if self.loss_config.loss_function == LossFunction.CrossEntropy:
221
+ return lm_output
222
+ elif self.loss_config.loss_function == LossFunction.KL_Divergence:
223
+ return self._compute_kl_loss(
224
+ lm_output=lm_output,
225
+ labels=labels,
226
+ past_key_values=past_key_values,
227
+ alt_input_ids=alt_input_ids,
228
+ alt_attention_mask=alt_attention_mask,
229
+ alt_labels=alt_labels,
230
+ **kwargs,
231
+ )
232
+ else:
233
+ raise ValueError(
234
+ f"Unsupported loss function: {self.loss_config.loss_function}"
235
+ )
236
+ else:
237
+ return lm_output
238
+
239
+ def prepare_inputs_for_generation(
240
+ self,
241
+ input_ids: torch.Tensor,
242
+ audio_values: Optional[torch.FloatTensor] = None,
243
+ audio_token_start_idx: Optional[torch.Tensor] = None,
244
+ audio_token_len: Optional[torch.Tensor] = None,
245
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
246
+ attention_mask: Optional[torch.Tensor] = None,
247
+ inputs_embeds: Optional[torch.Tensor] = None,
248
+ cache_position: Optional[torch.Tensor] = None,
249
+ **kwargs,
250
+ ) -> Dict[str, Any]:
251
+ model_input = self.language_model.prepare_inputs_for_generation(
252
+ input_ids=input_ids,
253
+ past_key_values=past_key_values,
254
+ attention_mask=attention_mask,
255
+ inputs_embeds=inputs_embeds,
256
+ cache_position=cache_position,
257
+ **kwargs,
258
+ )
259
+
260
+ # include audio information in model_input only when it is needed during prefilling
261
+ # audio_token_start_idx should always be relative to the current cache position
262
+ prefill_start_idx = 0 if cache_position is None else cache_position[0]
263
+ if (
264
+ audio_values is not None
265
+ and audio_token_start_idx is not None
266
+ and prefill_start_idx <= torch.max(audio_token_start_idx)
267
+ ):
268
+ model_input["audio_values"] = audio_values
269
+ model_input["audio_token_start_idx"] = (
270
+ audio_token_start_idx - prefill_start_idx
271
+ )
272
+ model_input["audio_token_len"] = audio_token_len
273
+
274
+ return model_input
275
+
276
+ @classmethod
277
+ def _create_multi_modal_projector(
278
+ cls, config: UltravoxConfig
279
+ ) -> "UltravoxProjector":
280
+ projector = UltravoxProjector(config)
281
+ projector.to(config.torch_dtype)
282
+ return projector
283
+
284
+ @classmethod
285
+ def _create_audio_tower(
286
+ cls, config: UltravoxConfig
287
+ ) -> Union[transformers.Wav2Vec2Model, "ModifiedWhisperEncoder"]:
288
+ if config.audio_model_id is not None:
289
+ if "whisper" in config.audio_model_id is not None:
290
+ audio_tower = ModifiedWhisperEncoder.from_pretrained(
291
+ config.audio_model_id, torch_dtype=config.torch_dtype
292
+ )
293
+ else:
294
+ audio_tower = transformers.AutoModel.from_pretrained(
295
+ config.audio_model_id, torch_dtype=config.torch_dtype
296
+ )
297
+ else:
298
+ if "whisper" in config.audio_config._name_or_path:
299
+ audio_tower = ModifiedWhisperEncoder(config.audio_config)
300
+ else:
301
+ with transformers.modeling_utils.no_init_weights():
302
+ # we only ever use from_config if the weights are retrained, hence initializing is not
303
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
304
+ audio_tower = transformers.AutoModel.from_config(
305
+ config.audio_config
306
+ )
307
+
308
+ if isinstance(
309
+ audio_tower,
310
+ (transformers.Wav2Vec2BertModel, transformers.WhisperModel),
311
+ ):
312
+ # For these models we only need the encoder part
313
+ # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
314
+ # WhisperModel -> WhisperEncoder
315
+ audio_tower = audio_tower.encoder
316
+
317
+ audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
318
+ return audio_tower
319
+
320
+ @classmethod
321
+ def _create_language_model(
322
+ cls, config: UltravoxConfig
323
+ ) -> transformers.LlamaForCausalLM:
324
+ if config.text_model_id is not None:
325
+ language_model = transformers.AutoModelForCausalLM.from_pretrained(
326
+ config.text_model_id,
327
+ attn_implementation=config._attn_implementation,
328
+ torch_dtype=config.torch_dtype,
329
+ )
330
+ else:
331
+ with transformers.modeling_utils.no_init_weights():
332
+ # we only ever use from_config if the weights are retrained, hence initializing is not
333
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
334
+ language_model = transformers.AutoModelForCausalLM.from_config(
335
+ config.text_config,
336
+ attn_implementation=config._attn_implementation,
337
+ torch_dtype=config.torch_dtype,
338
+ )
339
+
340
+ language_model = apply_lora(language_model, config.text_model_lora_config)
341
+ return language_model
342
+
343
+ def merge_and_unload(self):
344
+ if isinstance(self.language_model, peft.PeftModel):
345
+ self.language_model = self.language_model.merge_and_unload()
346
+ # no need to download base language model weights anymore, so we can remove the id
347
+ self.config.text_model_id = None
348
+ self.keep_params.update(
349
+ set(
350
+ [
351
+ f"language_model.{name}"
352
+ for name, _ in self.language_model.named_parameters()
353
+ ]
354
+ )
355
+ )
356
+
357
+ if isinstance(self.audio_tower, peft.PeftModel):
358
+ self.audio_tower = self.audio_tower.merge_and_unload()
359
+ # no need to download base audio model weights anymore, so we can remove the id
360
+ self.config.audio_model_id = None
361
+ self.keep_params.update(
362
+ set(
363
+ [
364
+ f"audio_tower.{name}"
365
+ for name, _ in self.audio_tower.named_parameters()
366
+ ]
367
+ )
368
+ )
369
+
370
+ for param in ["text_model_lora_config", "audio_model_lora_config"]:
371
+ if hasattr(self.config, param):
372
+ delattr(self.config, param)
373
+
374
+ def push_to_hub(self, *args, **kwargs):
375
+ self.merge_and_unload()
376
+ return super().push_to_hub(*args, **kwargs)
377
+
378
+ def save_pretrained(
379
+ self, *args, state_dict: Optional[Dict[str, Any]] = None, **kwargs
380
+ ):
381
+ if state_dict is None:
382
+ state_dict = super().state_dict()
383
+
384
+ named_params = dict(self.named_parameters())
385
+
386
+ state_dict = {
387
+ k: v
388
+ for k, v in state_dict.items()
389
+ if k in self.keep_params
390
+ or (k in named_params and named_params[k].requires_grad)
391
+ }
392
+
393
+ super().save_pretrained(*args, state_dict=state_dict, **kwargs)
394
+
395
+ def _pre_load_state_dict_hook(self, state_dict: Dict[str, Any], *args, **kwargs):
396
+ self.keep_params.update(set(state_dict.keys()))
397
+
398
+ def print_trainable_parameters(self):
399
+ """
400
+ Prints the number of trainable parameters in the model (reuses Peft model's method)
401
+ """
402
+ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
403
+
404
+ trainable_params, all_param = count_params(self)
405
+
406
+ logging.info(
407
+ f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
408
+ f" || trainable%: {100 * trainable_params / all_param:.1f}%"
409
+ )
410
+
411
+ lm_trainable_params, lm_all_params = count_params(self.language_model)
412
+ audio_trainable_params, audio_all_params = count_params(self.audio_tower)
413
+
414
+ projector_trainable_params = (
415
+ trainable_params - lm_trainable_params - audio_trainable_params
416
+ )
417
+ projector_all_params = all_param - lm_all_params - audio_all_params
418
+
419
+ logging.info(
420
+ f"Trainable%: "
421
+ f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
422
+ f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
423
+ f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
424
+ )
425
+
426
+
427
+ # TODO: refactor common parts to a shared module
428
+ def is_cache_empty(
429
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]]
430
+ ) -> bool:
431
+ """
432
+ Check if the cache is empty.
433
+ """
434
+ if past_key_values is None:
435
+ return True
436
+ if isinstance(past_key_values, tuple):
437
+ return all(len(c) == 0 for c in past_key_values)
438
+ return past_key_values.get_seq_length() == 0
439
+
440
+
441
+ def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
442
+ """
443
+ Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
444
+ """
445
+ unfreeze_layers = lora_config.pop("unfreeze_layers", None)
446
+ lora_config = peft.LoraConfig(**lora_config or {})
447
+
448
+ if lora_config.r == 0:
449
+ # freeze the model entirely, except for the specified layers
450
+ for name, param in model.named_parameters():
451
+ if not unfreeze_layers or not any(
452
+ re.match(layer, name) for layer in unfreeze_layers
453
+ ):
454
+ param.requires_grad = False
455
+ else:
456
+ logging.info(f"Unfreezing layer: {name} with #{param.numel()} params")
457
+ else:
458
+ model = peft.get_peft_model(model, lora_config)
459
+
460
+ return model
461
+
462
+
463
+ class StackAudioFrames(nn.Module):
464
+ """
465
+ Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.
466
+
467
+ The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
468
+ NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
469
+ we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
470
+ In most cases this extra padding will get removed in the model's forward function so it has no effect.
471
+ """
472
+
473
+ def __init__(self, stack_factor: int = 8):
474
+ super().__init__()
475
+ self.stack_factor = stack_factor
476
+
477
+ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
478
+ B, T, C = audio_embeds.shape
479
+ T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
480
+ audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
481
+ B, T, C = audio_embeds.shape
482
+ audio_embeds = audio_embeds.view(
483
+ B, T // self.stack_factor, C * self.stack_factor
484
+ )
485
+ return audio_embeds
486
+
487
+
488
+ class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
489
+ def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
490
+ super().__init__(hidden_size=hidden_size, eps=eps)
491
+ self.weight.data.fill_(init)
492
+
493
+
494
+ class SwiGLU(nn.Module):
495
+ def forward(self, x):
496
+ x, gate = x.chunk(2, dim=-1)
497
+ return F.silu(gate) * x
498
+
499
+
500
+ class UltravoxProjector(nn.Sequential):
501
+ def __init__(self, config: UltravoxConfig):
502
+ super().__init__()
503
+ self.hidden_dim = config.hidden_size
504
+ self._pad_and_stack = StackAudioFrames(config.stack_factor)
505
+ dim = config.audio_config.hidden_size * config.stack_factor
506
+ self.ln_pre = RMSNorm(dim, init=config.norm_init)
507
+ self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
508
+ dim = self.hidden_dim
509
+ self.act = transformers.activations.get_activation(config.projector_act)
510
+ dim = dim // 2 if config.projector_act == "swiglu" else dim
511
+ self.linear_2 = nn.Linear(dim, config.text_config.hidden_size, bias=False)
512
+ self.ln_post = RMSNorm(config.text_config.hidden_size, init=config.norm_init)
513
+
514
+ def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
515
+ audio_features = self._pad_and_stack(audio_features)
516
+ audio_features = self.ln_pre(audio_features)
517
+ hidden_states = self.linear_1(audio_features)
518
+ hidden_states = self.act(hidden_states)
519
+ hidden_states = self.linear_2(hidden_states)
520
+ hidden_states = self.ln_post(hidden_states)
521
+ return hidden_states
522
+
523
+
524
+ class ModifiedWhisperEncoder(whisper.WhisperEncoder):
525
+ """
526
+ Encoder portion of OpenAI's Whisper model.
527
+
528
+ This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
529
+ 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
530
+ 2. allow less than 30 second of audio padding to be passed in:
531
+ - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
532
+ - embed_pos is now sliced to match the length of `inputs_embeds`
533
+
534
+ Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
535
+ """
536
+
537
+ base_model_prefix = "model.encoder"
538
+ _no_split_modules = ["WhisperEncoderLayer"]
539
+
540
+ def forward(
541
+ self,
542
+ input_features,
543
+ attention_mask=None,
544
+ head_mask=None,
545
+ output_attentions=None,
546
+ output_hidden_states=None,
547
+ return_dict=None,
548
+ ):
549
+ expected_seq_length = (
550
+ self.config.max_source_positions
551
+ * self.conv1.stride[0]
552
+ * self.conv2.stride[0]
553
+ )
554
+ if input_features.shape[-1] > expected_seq_length:
555
+ raise ValueError(
556
+ 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}."
557
+ )
558
+
559
+ output_attentions = (
560
+ output_attentions
561
+ if output_attentions is not None
562
+ else self.config.output_attentions
563
+ )
564
+ output_hidden_states = (
565
+ output_hidden_states
566
+ if output_hidden_states is not None
567
+ else self.config.output_hidden_states
568
+ )
569
+ return_dict = (
570
+ return_dict if return_dict is not None else self.config.use_return_dict
571
+ )
572
+ inputs_embeds = nn.functional.gelu(self.conv1(input_features))
573
+ inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
574
+
575
+ inputs_embeds = inputs_embeds.permute(0, 2, 1)
576
+ embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
577
+
578
+ hidden_states = inputs_embeds + embed_pos
579
+ hidden_states = nn.functional.dropout(
580
+ hidden_states, p=self.dropout, training=self.training
581
+ )
582
+
583
+ encoder_states = () if output_hidden_states else None
584
+ all_attentions = () if output_attentions else None
585
+
586
+ # check if head_mask has a correct number of layers specified if desired
587
+ if head_mask is not None:
588
+ assert head_mask.size()[0] == (
589
+ len(self.layers)
590
+ ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
591
+
592
+ for idx, encoder_layer in enumerate(self.layers):
593
+ if output_hidden_states:
594
+ encoder_states = encoder_states + (hidden_states,)
595
+ # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
596
+ to_drop = False
597
+ if self.training:
598
+ dropout_probability = torch.rand([])
599
+ if dropout_probability < self.layerdrop: # skip the layer
600
+ to_drop = True
601
+
602
+ if to_drop:
603
+ layer_outputs = (None, None)
604
+ else:
605
+ if self.gradient_checkpointing and self.training:
606
+ layer_outputs = self._gradient_checkpointing_func(
607
+ encoder_layer.__call__,
608
+ hidden_states,
609
+ None,
610
+ (head_mask[idx] if head_mask is not None else None),
611
+ output_attentions,
612
+ )
613
+ else:
614
+ layer_outputs = encoder_layer(
615
+ hidden_states,
616
+ None,
617
+ layer_head_mask=(
618
+ head_mask[idx] if head_mask is not None else None
619
+ ),
620
+ output_attentions=output_attentions,
621
+ )
622
+
623
+ hidden_states = layer_outputs[0]
624
+
625
+ if output_attentions:
626
+ all_attentions = all_attentions + (layer_outputs[1],)
627
+
628
+ hidden_states = self.layer_norm(hidden_states)
629
+ if output_hidden_states:
630
+ encoder_states = encoder_states + (hidden_states,)
631
+
632
+ if not return_dict:
633
+ return tuple(
634
+ v
635
+ for v in [hidden_states, encoder_states, all_attentions]
636
+ if v is not None
637
+ )
638
+ return transformers.modeling_outputs.BaseModelOutput(
639
+ last_hidden_state=hidden_states,
640
+ hidden_states=encoder_states,
641
+ attentions=all_attentions,
642
+ )
643
+
644
+
645
+ UltravoxConfig.register_for_auto_class()
646
+ UltravoxModel.register_for_auto_class()
647
+
648
+ transformers.AutoConfig.register("ultravox", UltravoxConfig)
649
+ transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
650
+
651
+ transformers.activations.ACT2FN["swiglu"] = SwiGLU
ultravox_pipeline.py ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ from typing import Any, Dict, List, Optional
3
+
4
+ import numpy as np
5
+ import transformers
6
+
7
+ # We must use relative import in this directory to allow uploading to HF Hub
8
+ # Even "from . import X" pattern doesn't work (undocumented and unclear why)
9
+ from .ultravox_model import UltravoxModel
10
+ from .ultravox_processing import UltravoxProcessor
11
+
12
+
13
+ class UltravoxPipeline(transformers.Pipeline):
14
+ def __init__(
15
+ self,
16
+ model: UltravoxModel,
17
+ tokenizer: Optional[transformers.PreTrainedTokenizerBase] = None,
18
+ audio_processor: Optional[transformers.ProcessorMixin] = None,
19
+ **kwargs
20
+ ):
21
+ if tokenizer is None:
22
+ try:
23
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
24
+ model.config._name_or_path
25
+ )
26
+ except:
27
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
28
+ model.config.text_model_id or model.config.text_config._name_or_path
29
+ )
30
+
31
+ if audio_processor is None:
32
+ audio_processor = transformers.AutoProcessor.from_pretrained(
33
+ model.config.audio_model_id or model.config.audio_config._name_or_path
34
+ )
35
+
36
+ self.processor = UltravoxProcessor(
37
+ audio_processor=audio_processor,
38
+ tokenizer=tokenizer,
39
+ stack_factor=model.config.stack_factor,
40
+ )
41
+
42
+ super().__init__(model=model, tokenizer=tokenizer, **kwargs)
43
+
44
+ def _sanitize_parameters(self, **kwargs):
45
+ generation_keys = ["temperature", "max_new_tokens", "repetition_penalty"]
46
+ generation_kwargs = {k: kwargs[k] for k in kwargs if k in generation_keys}
47
+ return {}, generation_kwargs, {}
48
+
49
+ def preprocess(self, inputs: Dict[str, Any]):
50
+ turns: list = inputs.get("turns", [])
51
+
52
+ audio = inputs.get("audio", None)
53
+ # Convert to float32 if needed.
54
+ if isinstance(audio, np.ndarray):
55
+ if audio.dtype == np.float64:
56
+ audio = audio.astype(np.float32)
57
+ elif audio.dtype == np.int16:
58
+ audio = audio.astype(np.float32) / np.float32(32768.0)
59
+ elif audio.dtype == np.int32:
60
+ audio = audio.astype(np.float32) / np.float32(2147483648.0)
61
+
62
+ if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
63
+ prompt = inputs.get("prompt", "<|audio|>")
64
+ if "<|audio|>" not in prompt:
65
+ logging.warning(
66
+ "Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
67
+ )
68
+
69
+ prompt += " <|audio|>"
70
+ turns.append({"role": "user", "content": prompt})
71
+
72
+ text = self.processor.tokenizer.apply_chat_template(
73
+ turns, add_generation_prompt=True, tokenize=False
74
+ )
75
+
76
+ if "sampling_rate" not in inputs and audio is not None:
77
+ logging.warning(
78
+ "No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
79
+ )
80
+
81
+ output = self.processor(
82
+ text=text,
83
+ audio=audio,
84
+ sampling_rate=inputs.get("sampling_rate", 16000),
85
+ )
86
+ if "audio_values" in output:
87
+ output["audio_values"] = output["audio_values"].to(self.model.dtype)
88
+
89
+ return output
90
+
91
+ def _forward(
92
+ self,
93
+ model_inputs: Dict[str, Any],
94
+ temperature: Optional[float] = None,
95
+ max_new_tokens: Optional[int] = None,
96
+ repetition_penalty: float = 1.1,
97
+ ) -> List[int]:
98
+ temperature = temperature or None
99
+ do_sample = temperature is not None
100
+
101
+ terminators = [self.tokenizer.eos_token_id]
102
+ if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
103
+ terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
104
+
105
+ input_len = model_inputs["input_ids"].shape[1]
106
+
107
+ outputs = self.model.generate(
108
+ **model_inputs,
109
+ do_sample=do_sample,
110
+ temperature=temperature,
111
+ max_new_tokens=max_new_tokens,
112
+ repetition_penalty=repetition_penalty,
113
+ eos_token_id=terminators
114
+ )
115
+ return outputs[0][input_len:]
116
+
117
+ def postprocess(self, model_outputs) -> str:
118
+ output_text = self.tokenizer.decode(model_outputs, skip_special_tokens=True)
119
+ return output_text
120
+
121
+
122
+ transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
123
+ "ultravox-pipeline",
124
+ pipeline_class=UltravoxPipeline,
125
+ pt_model=transformers.AutoModel,
126
+ type="multimodal",
127
+ )
ultravox_processing.py ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional, Union
2
+
3
+ import numpy as np
4
+ import torch
5
+ import transformers
6
+
7
+ from .ultravox_config import UltravoxConfig
8
+
9
+
10
+ class UltravoxProcessor(transformers.ProcessorMixin):
11
+ """
12
+ Constructs an Ultravox processor which wraps an audio processor and a tokenizer into a single processor.
13
+
14
+ Args:
15
+ audio_processor: The audio processor for the audio encoder.
16
+ tokenizer: The tokenizer for the language model.
17
+ """
18
+
19
+ attributes = ["audio_processor", "tokenizer"]
20
+ audio_processor_class = (
21
+ "Wav2Vec2Processor",
22
+ "SeamlessM4TFeatureExtractor",
23
+ "WhisperProcessor",
24
+ )
25
+ tokenizer_class = (
26
+ "PreTrainedTokenizer",
27
+ "PreTrainedTokenizerFast",
28
+ )
29
+
30
+ tokenizer: transformers.PreTrainedTokenizerBase
31
+ audio_processor: transformers.ProcessorMixin
32
+
33
+ def __init__(
34
+ self,
35
+ audio_processor=None,
36
+ tokenizer=None,
37
+ audio_padding: str = "longest",
38
+ encoder_ds_factor: int = 320,
39
+ stack_factor: int = 8,
40
+ audio_placeholder: str = "<|audio|>",
41
+ ):
42
+ """
43
+ Args:
44
+ audio_processor: The audio processor for the audio encoder.
45
+ tokenizer: The tokenizer for the language model.
46
+ audio_padding: The padding strategy for the audio encoder.
47
+ encoder_ds_factor: The downsample factor of the audio encoder.
48
+ stack_factor: The factor by which the audio encoder output is stacked in the multimodal projector.
49
+ audio_placeholder: The placeholder for the audio in the text.
50
+ """
51
+ self.audio_padding = audio_padding
52
+ self.encoder_ds_factor = encoder_ds_factor
53
+ self.stack_factor = stack_factor
54
+ self.audio_placeholder = audio_placeholder
55
+ self.audio_token_replacement = tokenizer.eos_token
56
+ assert (
57
+ self.audio_token_replacement is not None
58
+ ), "The tokenizer has no EOS token. Cannot recover."
59
+ if tokenizer.pad_token_id is None:
60
+ tokenizer.pad_token_id = tokenizer.eos_token_id
61
+
62
+ super().__init__(audio_processor=audio_processor, tokenizer=tokenizer)
63
+
64
+ @classmethod
65
+ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
66
+ config: UltravoxConfig = transformers.AutoConfig.from_pretrained(
67
+ pretrained_model_name_or_path, **kwargs
68
+ )
69
+ audio_processor = transformers.AutoProcessor.from_pretrained(
70
+ config.audio_model_id
71
+ or config.audio_config._name_or_path
72
+ or "facebook/wav2vec2-base-960h"
73
+ )
74
+
75
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
76
+ pretrained_model_name_or_path, **kwargs
77
+ )
78
+ tokenizer.padding_side = "left"
79
+ tokenizer.pad_token = tokenizer.eos_token
80
+
81
+ return cls(
82
+ audio_processor=audio_processor,
83
+ tokenizer=tokenizer,
84
+ stack_factor=config.stack_factor,
85
+ )
86
+
87
+ def __call__(
88
+ self,
89
+ text: Optional[str] = None,
90
+ audio: Optional[Union[np.ndarray, torch.Tensor]] = None,
91
+ sampling_rate: Optional[int] = None,
92
+ return_tensors: Optional[
93
+ Union[str, transformers.TensorType]
94
+ ] = transformers.TensorType.PYTORCH,
95
+ **kwargs,
96
+ ) -> transformers.BatchFeature:
97
+ """
98
+ Main method to prepare for the model one text sequence and audio. This method forwards the `text`
99
+ and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode
100
+ the text. To prepare the audio(s), this method forwards the `audio`, `sampling_rate` and `kwargs` arguments to
101
+ audio processor's [`~Wav2Vec2Processor.__call__`] if `audio` is not `None`. Please refer to the docstring
102
+ of the above two methods for more information.
103
+
104
+ Args:
105
+ text (`str`, `List[str]`):
106
+ The sequence to be encoded. Sequence can be a string or (pretokenized string).
107
+ audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
108
+ The audio to be prepared. Audio can be NumPy array or PyTorch tensor. In case of a
109
+ NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T the
110
+ sample length of the audio.
111
+ sampling_rate (`int`, *optional*, defaults to 16000):
112
+ Sampling rate of the input audio. We expect 16kHz audio. Don't change this value unless you know what
113
+ you are doing.
114
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
115
+ If set, will return tensors of a particular framework. Acceptable values are:
116
+
117
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
118
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
119
+ - `'np'`: Return NumPy `np.ndarray` objects.
120
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
121
+
122
+ Returns:
123
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
124
+
125
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
126
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
127
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
128
+ `None`).
129
+ - **audio_values** -- Processed audio values to be fed to a model. Returned when `audio` is not `None`.
130
+ - **audio_token_len** -- Predicted number of audio frames: this value is guaranteed to be a close upper bound.
131
+ Returned when `audio` is not `None`.
132
+ - **audio_token_start_idx** -- The index in the tokenized text where the audio starts. Returned when `audio` is not `None`.
133
+ """
134
+ # TODO: Add support for multiple audio and text inputs.
135
+ data = {}
136
+ audio_embed_frames = 0
137
+ if audio is not None and len(audio) > 0:
138
+ if self.audio_padding == "max_length":
139
+ # 30 seconds is the expected length for Whisper
140
+ assert sampling_rate is not None, "Sampling rate must be provided."
141
+ audio_len = 30 * sampling_rate
142
+ else:
143
+ audio_len = audio.shape[-1]
144
+ # It's guaranteed that the number of frames is less than or equal to this amount.
145
+ # For Whisper this is exact AFAICT, but for Wav2Vec2 it's an upper bound.
146
+ # Currently, StackAudioFrames makes sure an over-estimation won't cause issues by padding the audio embeddings.
147
+ nb_encoder_frames = int(round(audio_len / self.encoder_ds_factor + 1e-4))
148
+ audio_embed_frames = int(np.ceil(nb_encoder_frames / self.stack_factor))
149
+ data["audio_token_len"] = [audio_embed_frames]
150
+
151
+ # Main audio processing. The processor is model-specific.
152
+ x = self.audio_processor(
153
+ audio,
154
+ sampling_rate=sampling_rate,
155
+ padding="longest",
156
+ max_length=audio_len,
157
+ **kwargs,
158
+ )
159
+ if "input_features" in x:
160
+ data["audio_values"] = x.input_features
161
+ else:
162
+ data["audio_values"] = x.input_values
163
+
164
+ if text is not None:
165
+ assert isinstance(
166
+ text, str
167
+ ), "Text must be a string. Batch mode not supported yet."
168
+ if self.audio_placeholder in text:
169
+ if "audio_token_len" not in data:
170
+ raise ValueError(
171
+ f"audio must be provided when using audio placeholder ({self.audio_placeholder}) in text."
172
+ )
173
+
174
+ start_idx = len(
175
+ self.tokenizer.encode(
176
+ text[: text.index(self.audio_placeholder)],
177
+ add_special_tokens=False,
178
+ )
179
+ )
180
+ data["audio_token_start_idx"] = [start_idx]
181
+
182
+ # Replace the audio placeholder with the audio token.
183
+ # e.g. "Transcribe\n<|audio|>" -> "Transcribe </s></s></s></s></s></s></s></s>"
184
+ # where the number of </s> is the number of audio frames.
185
+ text = text.replace(
186
+ self.audio_placeholder,
187
+ self.audio_token_replacement * audio_embed_frames,
188
+ )
189
+
190
+ # Special tokens like BOS should already have been added by the caller.
191
+ data.update(self.tokenizer([text], add_special_tokens=False, **kwargs))
192
+
193
+ return transformers.BatchFeature(data=data, tensor_type=return_tensors)
194
+
195
+ def batch_decode(self, *args, **kwargs):
196
+ return self.tokenizer.batch_decode(*args, **kwargs)
197
+
198
+ def decode(self, *args, **kwargs):
199
+ return self.tokenizer.decode(*args, **kwargs)
200
+
201
+ @property
202
+ def model_input_names(self):
203
+ tokenizer_input_names = self.tokenizer.model_input_names
204
+ audio_processor_input_names = self.audio_processor.model_input_names
205
+ return list(set(tokenizer_input_names + audio_processor_input_names))
206
+
207
+
208
+ UltravoxProcessor.register_for_auto_class()
209
+
210
+ transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor)