huggingface_library_transformer.md

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:

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:

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:

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):

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.