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Browse files- __pycache__/__init__.cpython-311.pyc +0 -0
- __pycache__/ultravox_config.cpython-311.pyc +0 -0
- __pycache__/ultravox_model.cpython-311.pyc +0 -0
- __pycache__/ultravox_pipeline.cpython-311.pyc +0 -0
- __pycache__/ultravox_processing.cpython-311.pyc +0 -0
- __pycache__/wandb_utils.cpython-311.pyc +0 -0
- __pycache__/whisper_model_modified.cpython-311.pyc +0 -0
- ultravox_config.py +0 -3
- ultravox_model.py +8 -12
- ultravox_pipeline.py +2 -0
- ultravox_processing.py +1 -1
__pycache__/__init__.cpython-311.pyc
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__pycache__/ultravox_config.cpython-311.pyc
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__pycache__/ultravox_model.cpython-311.pyc
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__pycache__/ultravox_pipeline.cpython-311.pyc
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__pycache__/ultravox_processing.cpython-311.pyc
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__pycache__/wandb_utils.cpython-311.pyc
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__pycache__/whisper_model_modified.cpython-311.pyc
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ultravox_config.py
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@@ -139,6 +139,3 @@ class UltravoxConfig(transformers.PretrainedConfig):
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self.initializer_range = self.text_config.initializer_range
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super().__init__(**kwargs)
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UltravoxConfig.register_for_auto_class()
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self.initializer_range = self.text_config.initializer_range
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super().__init__(**kwargs)
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ultravox_model.py
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@@ -11,8 +11,8 @@ import transformers.modeling_outputs
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import transformers.models
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# We must use relative import in this directory to allow uploading to HF Hub
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from .ultravox_config import UltravoxConfig
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from .ultravox_processing import UltravoxProcessor
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from .whisper_model_modified import WhisperEncoder as ModifiedWhisperEncoder
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@@ -188,10 +188,9 @@ class UltravoxModel(
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return model_input
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@classmethod
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def _create_audio_tower(
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]:
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if config.audio_model_id is not None:
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if "whisper" in config.audio_model_id is not None:
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audio_tower = ModifiedWhisperEncoder.from_pretrained(
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@@ -398,14 +397,11 @@ class UltravoxProjector(nn.Sequential):
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return hidden_states
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transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
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# transformers.AutoModelForCausalLM.register(
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# UltravoxConfig, UltravoxModel
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# )
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UltravoxModel.register_for_auto_class()
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transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor)
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# UltravoxModel.register_for_auto_class("AutoModelForCausalLM")
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transformers.activations.ACT2FN["swiglu"] = SwiGLU
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import transformers.models
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# We must use relative import in this directory to allow uploading to HF Hub
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# Even "from . import X" pattern doesn't work (undocumented and unclear why)
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from .ultravox_config import UltravoxConfig
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from .whisper_model_modified import WhisperEncoder as ModifiedWhisperEncoder
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return model_input
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@classmethod
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def _create_audio_tower(
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cls, config: UltravoxConfig
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) -> Union[transformers.Wav2Vec2Model, ModifiedWhisperEncoder]:
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if config.audio_model_id is not None:
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if "whisper" in config.audio_model_id is not None:
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audio_tower = ModifiedWhisperEncoder.from_pretrained(
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return hidden_states
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UltravoxConfig.register_for_auto_class()
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UltravoxModel.register_for_auto_class()
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transformers.AutoConfig.register("ultravox", UltravoxConfig)
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transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
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# transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor) # TODO: make processo work standalone
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transformers.activations.ACT2FN["swiglu"] = SwiGLU
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ultravox_pipeline.py
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@@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional
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import transformers
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# We must use relative import in this directory to allow uploading to HF Hub
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from .ultravox_model import UltravoxModel
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from .ultravox_processing import UltravoxProcessor
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transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
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"ultravox-pipeline",
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pipeline_class=UltravoxPipeline,
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type="multimodal",
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)
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import transformers
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# We must use relative import in this directory to allow uploading to HF Hub
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# Even "from . import X" pattern doesn't work (undocumented and unclear why)
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from .ultravox_model import UltravoxModel
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from .ultravox_processing import UltravoxProcessor
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transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
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"ultravox-pipeline",
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pipeline_class=UltravoxPipeline,
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pt_model=transformers.AutoModel,
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type="multimodal",
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)
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ultravox_processing.py
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from typing import
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import numpy as np
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import torch
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from typing import Optional, Union
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import numpy as np
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import torch
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