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HunyuanVideo-Foley/siglip_encoder/README.md

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+---
+license: apache-2.0
+tags:
+- vision
+widget:
+  - src: >-
+      https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
+    candidate_labels: bee in the sky, bee on the flower
+    example_title: Bee
+library_name: transformers
+pipeline_tag: zero-shot-image-classification
+---
+
+# SigLIP 2 Base
+
+[SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
+[SigLIP](https://huggingface.co/papers/2303.15343) with prior, independently developed techniques
+into a unified recipe, for improved semantic understanding, localization, and dense features.
+
+## Intended uses
+
+You can use the raw model for tasks like zero-shot image classification and
+image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from transformers import pipeline
+
+# load pipeline
+ckpt = "google/siglip2-base-patch16-512"
+image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification")
+
+# load image and candidate labels
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+candidate_labels = ["2 cats", "a plane", "a remote"]
+
+# run inference
+outputs = image_classifier(image, candidate_labels)
+print(outputs)
+```
+
+You can encode an image using the Vision Tower like so:
+
+```python
+import torch
+from transformers import AutoModel, AutoProcessor
+from transformers.image_utils import load_image
+
+# load the model and processor
+ckpt = "google/siglip2-base-patch16-512"
+model = AutoModel.from_pretrained(ckpt, device_map="auto").eval()
+processor = AutoProcessor.from_pretrained(ckpt)
+
+# load the image
+image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
+inputs = processor(images=[image], return_tensors="pt").to(model.device)
+
+# run infernece
+with torch.no_grad():
+    image_embeddings = model.get_image_features(**inputs)    
+
+print(image_embeddings.shape)
+```
+
+For more code examples, we refer to the [siglip documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+SigLIP 2 adds some clever training objectives on top of SigLIP:
+
+1. Decoder loss
+2. Global-local and masked prediction loss
+3. Aspect ratio and resolution adaptibility 
+
+### Training data
+
+SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Compute
+
+The model was trained on up to 2048 TPU-v5e chips.
+
+## Evaluation results
+
+Evaluation of SigLIP 2 is shown below (taken from the paper).
+
+![Evaluation Table](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{tschannen2025siglip2multilingualvisionlanguage,
+      title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, 
+      author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
+      year={2025},
+      eprint={2502.14786},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.14786}, 
+}
+```

HunyuanVideo-Foley/siglip_encoder/config.json

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+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "model_type": "siglip_text_model",
+    "vocab_size": 256000
+  },
+  "transformers_version": "4.49.0.dev0",
+  "vision_config": {
+    "image_size": 512,
+    "model_type": "siglip_vision_model"
+  }
+}

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+  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": true,
+  "eos_token": "<eos>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "padding_side": "right",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

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HunyuanVideo-Foley/text_encoder/README.md

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+---
+license: apache-2.0
+---
+
+
+
+# Model
+
+## TL;DR
+
+CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on general audio, music and speech.
+
+## Description
+
+CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score.
+
+
+# Usage
+
+You can use this model for zero shot audio classification or extracting audio and/or textual features.
+
+# Uses
+
+## Perform zero-shot audio classification
+
+### Using `pipeline`
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+dataset = load_dataset("ashraq/esc50")
+audio = dataset["train"]["audio"][-1]["array"]
+
+audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/larger_clap_general")
+output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
+print(output)
+>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
+```
+
+## Run the model:
+
+You can also get the audio and text embeddings using `ClapModel`
+
+### Run the model on CPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/larger_clap_general")
+processor = ClapProcessor.from_pretrained("laion/larger_clap_general")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
+audio_embed = model.get_audio_features(**inputs)
+```
+
+### Run the model on GPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/larger_clap_general").to(0)
+processor = ClapProcessor.from_pretrained("laion/larger_clap_general")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
+audio_embed = model.get_audio_features(**inputs)
+```
+
+
+# Citation
+
+If you are using this model for your work, please consider citing the original paper:
+```
+@misc{https://doi.org/10.48550/arxiv.2211.06687,
+  doi = {10.48550/ARXIV.2211.06687},
+  url = {https://arxiv.org/abs/2211.06687},
+  author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
+  keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
+  title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+

HunyuanVideo-Foley/text_encoder/config.json

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+{
+  "_name_or_path": "/home/yoach/tmp/clap/larger_clap_music_speech_general",
+  "architectures": [
+    "ClapModel"
+  ],
+  "audio_config": {
+    "depths": [
+      2,
+      2,
+      12,
+      2
+    ],
+    "hidden_size": 1024,
+    "model_type": "clap_audio_model",
+    "patch_embeds_hidden_size": 128
+  },
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 14.285714285714285,
+  "model_type": "clap",
+  "num_hidden_layers": 12,
+  "projection_dim": 512,
+  "projection_hidden_act": "relu",
+  "text_config": {
+    "model_type": "clap_text_model"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0.dev0"
+}

HunyuanVideo-Foley/text_encoder/preprocessor_config.json

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+{
+  "chunk_length_s": 10,
+  "feature_extractor_type": "ClapFeatureExtractor",
+  "feature_size": 64,
+  "fft_window_size": 1024,
+  "frequency_max": 14000,
+  "frequency_min": 50,
+  "hop_length": 480,
+  "max_length_s": 10,
+  "n_fft": 1024,
+  "nb_frequency_bins": 513,
+  "nb_max_frames": 1000,
+  "nb_max_samples": 480000,
+  "padding": "repeatpad",
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "ClapProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 48000,
+  "top_db": null,
+  "truncation": "rand_trunc"
+}

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