The Hugging Face Transformers library absolutely supports models for images, audio/voice, and video — not just text. Here’s how it works: ## ✅ Hugging Face Transformers Supports Multiple Data Types ## 1. Text (NLP) Models: BERT, GPT-2, T5, LLaMA, Mistral Class: AutoModelForSequenceClassification, AutoModelForCausalLM, etc. ## 2. Images (Vision Transformers) Models: ViT, DeiT, BEiT, Swin, ConvNeXT, DINO, MAE Class: AutoModelForImageClassification Example: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import torch processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224") image = Image.open("cat.jpg") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) pred = outputs.logits.argmax(-1) print(pred) ``` ## 3. Audio / Speech Models: Wav2Vec2, Whisper, HuBERT, SpeechT5 Class: AutoModelForSpeechRecognition Example: ```python from transformers import AutoProcessor, AutoModelForSpeechRecognition import torch import soundfile as sf processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") model = AutoModelForSpeechRecognition.from_pretrained("facebook/wav2vec2-base-960h") speech, rate = sf.read("speech.wav") inputs = processor(speech, sampling_rate=rate, return_tensors="pt") logits = model(**inputs).logits pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) print(transcription) ``` ## 4. Video Models: TimeSformer, VideoMAE Class: AutoModelForVideoClassification Example: ```python from transformers import AutoImageProcessor, AutoModelForVideoClassification import torch, av processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") model = AutoModelForVideoClassification.from_pretrained("MCG-NJU/videomae-base") # (Pseudo example - you'd extract video frames first) frames = [...] inputs = processor(frames, return_tensors="pt") outputs = model(**inputs) print(outputs.logits.argmax(-1)) ``` ## 5. Multimodal (Text + Image / Video) Models: CLIP, BLIP, ViLT Class: AutoModel depending on task Example (CLIP): ```python from transformers import CLIPProcessor, CLIPModel from PIL import Image model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") image = Image.open("dog.jpg") inputs = processor(text=["a dog", "a cat"], images=image, return_tensors="pt", padding=True) outputs = model(**inputs) logits = outputs.logits_per_image print(logits.softmax(dim=-1)) ``` ## 🔹 Conclusion ➡️ Yes, Hugging Face Transformers includes models for text, images, audio/voice, video, and multimodal tasks. It started with NLP but has expanded into computer vision, speech, and multimodal AI.