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210 lines
8.8 KiB
210 lines
8.8 KiB
# Copyright 2025 NVIDIA CORPORATION and the HuggingFace Inc. team. All rights
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# reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.get_logger(__name__)
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class AudioFlamingo3EncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`AudioFlamingo3Encoder`]. It is used to instantiate an
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AudioFlamingo3 audio encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the audio encoder of the AudioFlamingo3
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architecture.
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e.g. [nvidia/audio-flamingo-3-hf](https://huggingface.co/nvidia/audio-flamingo-3-hf)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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num_mel_bins (`int`, *optional*, defaults to 128):
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Number of mel features used per input features. Should correspond to the value used in the
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`AudioFlamingo3Processor` class.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of encoder layers.
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num_attention_heads (`int`, *optional*, defaults to 20):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
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layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](https://huggingface.co/papers/1909.11556)
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for more details.
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activation_function (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the layers.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by dividing by sqrt(hidden_size).
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max_source_positions (`int`, *optional*, defaults to 1500):
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The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
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Example:
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```python
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>>> from transformers import AudioFlamingo3EncoderConfig, AudioFlamingo3Encoder
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>>> # Initializing an AudioFlamingo3EncoderConfig
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>>> configuration = AudioFlamingo3EncoderConfig()
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>>> # Initializing an AudioFlamingo3Encoder (with random weights)
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>>> model = AudioFlamingo3Encoder(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "audioflamingo3_encoder"
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attribute_map = {
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"d_model": "hidden_size",
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"encoder_layers": "num_hidden_layers",
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"encoder_attention_heads": "num_attention_heads",
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"encoder_ffn_dim": "intermediate_size",
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"encoder_layerdrop": "layerdrop",
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}
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def __init__(
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self,
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num_mel_bins=128,
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num_hidden_layers=32,
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num_attention_heads=20,
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intermediate_size=5120,
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layerdrop=0.0,
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activation_function="gelu",
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hidden_size=1280,
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dropout=0.0,
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attention_dropout=0.0,
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activation_dropout=0.0,
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initializer_range=0.02,
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scale_embedding=False,
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max_source_positions=1500,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_mel_bins = num_mel_bins
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.initializer_range = initializer_range
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self.layerdrop = layerdrop
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self.num_hidden_layers = num_hidden_layers
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self.scale_embedding = scale_embedding
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self.max_source_positions = max_source_positions
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class AudioFlamingo3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`AudioFlamingo3ForConditionalGeneration`]. It is used to instantiate an
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AudioFlamingo3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the AudioFlamingo3.
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e.g. [nvidia/audio-flamingo-3-hf](https://huggingface.co/nvidia/audio-flamingo-3-hf)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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audio_config (`Union[AudioFlamingo3EncoderConfig, dict]`, *optional*, defaults to `AudioFlamingo3EncoderConfig`):
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The config object or dictionary of the audio backbone.
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text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
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The config object or dictionary of the text backbone.
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audio_token_id (`int`, *optional*, defaults to 151669):
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The audio token index to encode the audio prompt.
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projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
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Activation function used in the projector.
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projector_bias (`bool`, *optional*, defaults to `True`):
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Whether to include bias terms in the projector.
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Example:
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```python
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>>> from transformers import AudioFlamingo3ForConditionalGeneration, AudioFlamingo3Config, AudioFlamingo3EncoderConfig, Qwen2Config
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>>> # Initializing an AudioFlamingo3Encoder config
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>>> audio_config = AudioFlamingo3EncoderConfig()
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>>> # Initializing a Qwen2 config
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>>> text_config = Qwen2Config()
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>>> # Initializing an AudioFlamingo3 configuration
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>>> configuration = AudioFlamingo3Config(audio_config, text_config)
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>>> # Initializing a model from the audioflamingo3 style configuration
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>>> model = AudioFlamingo3ForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "audioflamingo3"
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sub_configs = {
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"audio_config": AudioFlamingo3EncoderConfig,
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"text_config": AutoConfig,
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}
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def __init__(
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self,
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audio_config=None,
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text_config=None,
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audio_token_id=151669,
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projector_hidden_act="gelu",
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projector_bias=True,
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**kwargs,
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):
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self.audio_token_id = audio_token_id
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if isinstance(audio_config, dict):
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audio_config["model_type"] = audio_config.get("model_type", "audioflamingo3_encoder")
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audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config)
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elif audio_config is None:
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audio_config = CONFIG_MAPPING["audioflamingo3_encoder"]()
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self.audio_config = audio_config
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if isinstance(text_config, dict):
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text_config["model_type"] = text_config.get("model_type", "qwen2")
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text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
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elif text_config is None:
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text_config = CONFIG_MAPPING["qwen2"]()
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self.text_config = text_config
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self.projector_hidden_act = projector_hidden_act
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self.projector_bias = projector_bias
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super().__init__(**kwargs)
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__all__ = ["AudioFlamingo3Config", "AudioFlamingo3EncoderConfig"]
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