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Audio Flamingo 3

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This model was released on 2025-07-10 and added to Hugging Face Transformers on 2025-11-11.

Audio Flamingo 3

Overview

Audio Flamingo 3 (AF3) is a fully open large audio–language model designed for robust understanding and reasoning over speech, environmental sounds, and music. AF3 pairs a Whisper-style audio encoder with a causal language model and performs replace-in-place audio–text fusion: the processor aligns post-pool audio frames to a dedicated placeholder token and the model replaces those token slots with projected audio embeddings during the forward pass.

The model checkpoint is available at: nvidia/audio-flamingo-3-hf

Highlights:

Unified audio encoder across speech, sound, and music.
Long-audio support via windowing and post-pool alignment (up to 10 minutes maximum). The model processes audio in 30-second windows with a hard limit of 20 windows (10 minutes total). Audio longer than 10 minutes will be truncated.
Deterministic fusion that preserves sequence length by replacing audio placeholder tokens with audio embeddings.

This model was contributed by Lasha Koroshinadze and Eric Bezzam.

Paper

Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models
A. Goel, S. Ghosh, J. Kim, S. Kumar, Z. Kong, S. Lee, C.-H. H. Yang, R. Duraiswami, D. Manocha, R. Valle, B. Catanzaro
NVIDIA and University of Maryland
Project: https://research.nvidia.com/labs/adlr/AF3/

Usage

Audio Instruct Mode

The model supports audio-text instructions, including multi-turn interactions, all processed in batches.

➡️ audio + text instruction

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Transcribe the input speech."},
            {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/WhDJDIviAOg_120_10.mp3"},
        ],
    }
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(decoded_outputs)

➡️ multi-turn:

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Instruction: How does the tone of female speech change throughout the audio? Choose the correct option among the options below: (A) Sad to happy (B) Happy to sad (C) Neutral to happy (D) Happy to neutral.",
            },
            {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/000000786159.31.wav"},
        ],
    },
    {
        "role": "assistant",
        "content": [{"type": "text", "text": "(A) Sad to happy"}],
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Why do you think so?"},
        ],
    },
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(decoded_outputs)

➡️ text only:

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What is the capital of France?"},
        ],
    }
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(decoded_outputs)

➡️ audio only:

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/WhDJDIviAOg_120_10.mp3"},
        ],
    }
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(decoded_outputs)

➡️ batched inference!

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

conversations = [
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transcribe the input speech."},
                {
                    "type": "audio",
                    "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/t_837b89f2-26aa-4ee2-bdf6-f73f0dd59b26.wav",
                },
            ],
        }
    ],
    [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "This track feels really peaceful and introspective. What elements make it feel so calming and meditative?",
                },
                {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/FPSbCAANfbJLVSwD.mp3"},
            ],
        }
    ],
]

inputs = processor.apply_chat_template(
    conversations,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(decoded_outputs)

➡️ Training:

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")
model.train()

conversation = [
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transcribe the input speech."},
                {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/WhDJDIviAOg_120_10.mp3"},
            ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "The transcription of the audio is 'summer follows spring the days grow longer and the nights are warm'."}],
        }
    ],
    [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "This track feels really peaceful and introspective. What elements make it feel so calming and meditative?",
                },
                {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/FPSbCAANfbJLVSwD.mp3"},
            ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "The transcription of the audio is 'some transcription of the audio'."}],
        }

    ]
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    output_labels=True,
).to(model.device)

loss = model(**inputs).loss
loss.backward()

➡️ transcription shortcut

from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

model_id = "nvidia/audio-flamingo-3-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

inputs = processor.apply_transcription_request(audio="https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/t_837b89f2-26aa-4ee2-bdf6-f73f0dd59b26.wav").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True, strip_prefix=True)

print(decoded_outputs)

The model is trained to emit transcriptions prefixed with assistant framing such as The spoken content of the audio is "<text>".. Use strip_prefix=True (as shown above) to remove the fixed assistant sentence and surrounding quotes so that only the transcription remains.

How the model works

Architecture

AudioFlamingo3Encoder Whisper-style feature extractor + encoder → average-pool over time (stride 2) → LayerNorm. Produces per-frame hidden states at the post-pool rate.
AudioFlamingo3MultiModalProjector A small MLP that maps encoder features to the language model’s hidden size.
AudioFlamingo3ForConditionalGeneration A causal language model that accepts text embeddings where each audio placeholder token slot is replaced, in place, by an audio frame embedding. No sequence-length change is introduced by fusion.

Processor-level alignment

Each raw waveform is split into fixed-length windows based on the feature extractor’s chunk_length (seconds) and sampling_rate (Hz).
For each window, the processor computes the number of post-pool frames post_pool_len that the encoder will output (matching the conv/pool schedule).
The processor expands the audio placeholder token by the total number of post-pool frames across all windows.
The model later replaces those token positions with the corresponding projected audio embeddings.

Usage patterns

Transcription shortcut

For automatic speech recognition you can skip writing the default instruction each time and call apply_transcription_request():

inputs = processor.apply_transcription_request(audio=audio_array)

Pass prompt="Transcribe the input speech." (or a list of prompts for batch audio) to customize the instruction while keeping the audio placeholder handling.

audio accepts in-memory arrays, local file paths, or URLs. Any processor kwargs (text_kwargs, audio_kwargs, etc.) are forwarded, so you can tweak padding or tensor formats just like when calling processor(...).

Long audio and windowing

Important: Maximum audio length is 10 minutes. Audio longer than this will be truncated.

The default setup processes 30-second windows at 16 kHz mono.
The processor enforces a hard limit of 20 windows per sample, resulting in a maximum of 10 minutes of audio (20 windows × 30 seconds).
For each window:
- mel_len is the padded mel length.
- A conv stack reduces time as conv_output_len = (mel_len - 1) // 2 + 1.
- Post-pool frames per window: post_pool_len = (conv_output_len - 2) // 2 + 1.
- An audio placeholder token is expanded to the sum of post_pool_len across all windows.

Padding, attention, and caching

Left padding vs right padding For generation with mixed prompt lengths in a batch, left padding is usually preferable. For training, right padding is common; AF3’s fusion mechanism itself is padding-agnostic because it replaces in place.
Attention masks The processor returns attention_mask (text) and input_features_mask (audio). The model builds an internal 4-D mask on the encoder’s pre-pool axis with negative infinity at pad positions.
Caching During generation, input_features and input_features_mask are only passed on the first step. Subsequent steps use cached keys/values from the language model.

Troubleshooting

Empty or truncated outputs when batching Use left padding for batched generation and decode only the new tokens after the prompt length, as shown in the quickstart.

AudioFlamingo3Config

class transformers.AudioFlamingo3Config

< source >

( audio_config = None text_config = None audio_token_id = 151669 projector_hidden_act = 'gelu' projector_bias = True **kwargs )

Parameters

audio_config (Union[AudioFlamingo3EncoderConfig, dict], optional, defaults to AudioFlamingo3EncoderConfig) — The config object or dictionary of the audio backbone.
text_config (Union[AutoConfig, dict], optional, defaults to Qwen2Config) — The config object or dictionary of the text backbone.
audio_token_id (int, optional, defaults to 151669) — The audio token index to encode the audio prompt.
projector_hidden_act (str, optional, defaults to "gelu") — Activation function used in the projector.
projector_bias (bool, optional, defaults to True) — Whether to include bias terms in the projector.

This is the configuration class to store the configuration of an AudioFlamingo3ForConditionalGeneration. It is used to instantiate an AudioFlamingo3 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the AudioFlamingo3.

e.g. nvidia/audio-flamingo-3-hf

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

>>> from transformers import AudioFlamingo3ForConditionalGeneration, AudioFlamingo3Config, AudioFlamingo3EncoderConfig, Qwen2Config

>>> # Initializing an AudioFlamingo3Encoder config
>>> audio_config = AudioFlamingo3EncoderConfig()

>>> # Initializing a Qwen2 config
>>> text_config = Qwen2Config()

>>> # Initializing an AudioFlamingo3 configuration
>>> configuration = AudioFlamingo3Config(audio_config, text_config)

>>> # Initializing a model from the audioflamingo3 style configuration
>>> model = AudioFlamingo3ForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

AudioFlamingo3EncoderConfig

class transformers.AudioFlamingo3EncoderConfig

< source >

( num_mel_bins = 128 num_hidden_layers = 32 num_attention_heads = 20 intermediate_size = 5120 layerdrop = 0.0 activation_function = 'gelu' hidden_size = 1280 dropout = 0.0 attention_dropout = 0.0 activation_dropout = 0.0 initializer_range = 0.02 scale_embedding = False max_source_positions = 1500 **kwargs )

Parameters

num_mel_bins (int, optional, defaults to 128) — Number of mel features used per input features. Should correspond to the value used in the AudioFlamingo3Processor class.
num_hidden_layers (int, optional, defaults to 32) — Number of encoder layers.
num_attention_heads (int, optional, defaults to 20) — Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 5120) — Dimensionality of the “intermediate” (often named feed-forward) layer in encoder.
layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the encoder. See the LayerDrop paper for more details.
activation_function (str, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.
hidden_size (int, optional, defaults to 1280) — Dimensionality of the layers.
dropout (float, optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
activation_dropout (float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_embedding (bool, optional, defaults to False) — Scale embeddings by dividing by sqrt(hidden_size).
max_source_positions (int, optional, defaults to 1500) — The maximum sequence length of log-mel filter-bank features that this model might ever be used with.

This is the configuration class to store the configuration of an AudioFlamingo3Encoder. It is used to instantiate an AudioFlamingo3 audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the AudioFlamingo3 architecture.

e.g. nvidia/audio-flamingo-3-hf

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

>>> from transformers import AudioFlamingo3EncoderConfig, AudioFlamingo3Encoder

>>> # Initializing an AudioFlamingo3EncoderConfig
>>> configuration = AudioFlamingo3EncoderConfig()

>>> # Initializing an AudioFlamingo3Encoder (with random weights)
>>> model = AudioFlamingo3Encoder(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

AudioFlamingo3Processor

class transformers.AudioFlamingo3Processor

< source >

( feature_extractor tokenizer chat_template = None audio_token = '<sound>' )

Parameters

feature_extractor (WhisperFeatureExtractor) — The feature extractor is a required input.
tokenizer (Qwen2TokenizerFast) — The tokenizer is a required input.
chat_template (Optional[str], optional) — The Jinja template to use for formatting the conversation. If not provided, the tokenizer’s default chat template will be used.
audio_token (Optional[str], optional, defaults to "<sound>") — Special token used to represent audio inputs in the chat template.

Constructs an AudioFlamingo3 processor which wraps an AudioFlamingo3 feature extractor and an AudioFlamingo3 tokenizer into a single processor.

AudioFlamingo3Processor offers all the functionalities of WhisperFeatureExtractor and Qwen2TokenizerFast. See the __call__() for more information.

apply_transcription_request

< source >

( audio: typing.Union[str, list[str], numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor']] prompt: typing.Union[str, list[str], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.audioflamingo3.processing_audioflamingo3.AudioFlamingo3ProcessorKwargs] ) → BatchFeature

Parameters

audio (str, list[str], np.ndarray, torch.Tensor, list[np.ndarray], list[torch.Tensor]) — Audio to transcribe. Strings are interpreted as local paths or URLs and will be loaded automatically by the chat template loader; NumPy arrays and PyTorch tensors are forwarded directly.
prompt (str or list[str], optional) — Custom prompt(s) to include in the user turn. A list must be the same length as the batch. When None, each sample uses "Transcribe the input speech.".
**kwargs — Additional keyword arguments forwarded to apply_chat_template() (for example text_kwargs, audio_kwargs, …).

Returns

BatchFeature

Processor outputs ready to be passed to AudioFlamingo3ForConditionalGeneration.generate().

Prepare inputs for automatic speech recognition without manually writing the default transcription prompt.

batch_decode

< source >

( *args strip_prefix = False **kwargs )

Forward arguments to batch_decode() and optionally remove the assistant framing the model was trained to produce.

AF3 transcription requests respond with sentences such as "The spoken content of the audio is "...".". Setting strip_prefix=True trims the fixed prefix for just the transcription text.

AudioFlamingo3Encoder

class transformers.AudioFlamingo3Encoder

< source >

( config: AudioFlamingo3EncoderConfig )

Parameters

config (AudioFlamingo3EncoderConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The audio model from AudioFlamingo3 without any head or projection on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_features: Tensor input_features_mask: typing.Optional[torch.Tensor] = None )

Parameters

input_features (torch.FloatTensor of shape (batch_size, feature_size, sequence_length)) — Log-Mel features extracted from raw audio. Use the processor/feature extractor to compute and pad these features from waveform input.
input_features_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding feature indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.

AudioFlamingo3ForConditionalGeneration

class transformers.AudioFlamingo3ForConditionalGeneration

< source >

( config )

Parameters

config (AudioFlamingo3ForConditionalGeneration) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The AudioFlamingo3 model which consists of a fine-tuned Whisper encoder, a multi-modal projector and a Qwen2 language model.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None input_features: typing.Optional[torch.FloatTensor] = None input_features_mask: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are input IDs?
input_features (torch.FloatTensor of shape (batch_size, sequence_length, feature_dim), optional) — The tensors corresponding to the input audio features. Audio features can be obtained using feature_extractor_class. See feature_extractor_class.__call__ for details (AudioFlamingo3Processor uses feature_extractor_class for processing audios).
input_features_mask (torch.Tensor of shape (batch_size, feature_sequence_length)) — Mask to avoid performing attention on padding feature indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
What are attention masks?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

What are position IDs?
past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

The model will output the same cache format that is fed as input.

If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
logits_to_keep (Union[int, torch.Tensor], defaults to 0) — If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (AudioFlamingo3Config) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The AudioFlamingo3ForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

>>> from transformers import AudioFlamingo3ForConditionalGeneration, AutoProcessor

>>> model_id = "nvidia/audio-flamingo-3-hf"
>>> processor = AutoProcessor.from_pretrained(model_id)
>>> model = AudioFlamingo3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")

>>> conversations = [
>>>     [
>>>         {
>>>             "role": "user",
>>>             "content": [
>>>                 {"type": "text", "text": "Transcribe the input speech."},
>>>                 {
>>>                     "type": "audio",
>>>                     "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/t_837b89f2-26aa-4ee2-bdf6-f73f0dd59b26.wav",
>>>                 },
>>>             ],
>>>         }
>>>     ],
>>>     [
>>>         {
>>>             "role": "user",
>>>             "content": [
>>>                 {
>>>                     "type": "text",
>>>                     "text": "This track feels really peaceful and introspective. What elements make it feel so calming and meditative?",
>>>                 },
>>>                 {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/FPSbCAANfbJLVSwD.mp3"},
>>>             ],
>>>         }
>>>     ],
>>> ]

>>> inputs = processor.apply_chat_template(
>>>     conversations,
>>>     tokenize=True,
>>>     add_generation_prompt=True,
>>>     return_dict=True,
>>> ).to(model.device)

>>> outputs = model.generate(**inputs, max_new_tokens=500)

>>> decoded_outputs = processor.batch_decode(
>>>     outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True
>>> )
>>> print(decoded_outputs)
["The spoken content of the audio is...", "The track's calming and meditative feel can be attributed to..."]

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