first commit

docs/source/en/model_doc/audioflamingo3.md

@@ -0,0 +1,414 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2025-07-10 and contributed to Hugging Face Transformers on 2025-11-12.*
+
+# Audio Flamingo 3
+
+<div class="flex flex-wrap space-x-1">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## 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](https://huggingface.co/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](https://huggingface.co/lashahub) and [Eric Bezzam](https://huggingface.co/bezzam).
+
+### Paper
+
+[Audio Flamingo 3](https://huggingface.co/papers/2507.08128): 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
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+
+decoded_outputs = processor.decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+➡️ multi-turn:
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+
+decoded_outputs = processor.decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+➡️ text only:
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+
+decoded_outputs = processor.decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+➡️ audio only:
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+
+decoded_outputs = processor.decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+➡️ batched inference!
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+
+decoded_outputs = processor.decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+➡️ Training:
+
+```python
+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, dtype=model.dtype)
+
+loss = model(**inputs).loss
+loss.backward()
+```
+
+➡️ transcription shortcut
+
+```python
+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, dtype=model.dtype)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.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
+
+1. Each raw waveform is split into fixed-length windows based on the feature extractor’s `chunk_length` (seconds) and `sampling_rate` (Hz).
+2. 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).
+3. The processor expands the audio placeholder token by the total number of post-pool frames across all windows.
+4. 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
+[`~transformers.AudioFlamingo3Processor.apply_transcription_request`]:
+
+```python
+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
+
+[[autodoc]] AudioFlamingo3Config
+
+## AudioFlamingo3EncoderConfig
+
+[[autodoc]] AudioFlamingo3EncoderConfig
+
+## AudioFlamingo3Processor
+
+[[autodoc]] AudioFlamingo3Processor
+    - __call__
+
+## AudioFlamingo3Encoder
+
+[[autodoc]] AudioFlamingo3Encoder
+    - forward
+
+## AudioFlamingo3Model
+
+[[autodoc]] AudioFlamingo3Model
+    - forward
+
+## AudioFlamingo3ForConditionalGeneration
+
+[[autodoc]] AudioFlamingo3ForConditionalGeneration
+    - forward
+    - get_audio_features