Create README.md

README.md

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+---
+inference: false
+license: cc-by-4.0
+---
+
+# Model Card
+
+<p align="center">
+  <img src="./icon.png" alt="Logo" width="350">
+</p>
+
+This is Owlet-Phi-2-Audio.
+
+Owlet is a family of lightweight but powerful multimodal models. 
+
+We provide Owlet-phi-2-audio, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Phi-2](https://huggingface.co/microsoft/phi-2) and [Whisper](https://huggingface.co/openai/whisper-small). 
+This model supports both audio and visual signals from video data as input, and performs competitevely on the task of Video Question-Answering(QA).
+The training procedure and architecture details will be released in a technical report soon.
+
+# Quickstart
+
+Here we show a code snippet to show you how to use the model with transformers.
+It accepts a mp4 video file, and wav audio file as the input, and generates the answer to the user query.
+
+Before running the snippet, you need to install the following dependencies:
+
+```shell
+pip install torch transformers accelerate pillow decord librosa
+```
+
+```python
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import warnings
+import librosa
+
+# disable some warnings
+transformers.logging.set_verbosity_error()
+transformers.logging.disable_progress_bar()
+warnings.filterwarnings('ignore')
+
+# set device
+device = 'cuda'  # or cpu
+torch.set_default_device(device)
+
+# create model
+print('Loading the model...')
+model = AutoModelForCausalLM.from_pretrained(
+    'phronetic-ai/owlet-phi-2-audio',
+    torch_dtype=torch.float16, # float32 for cpu
+    device_map='auto',
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    'phronetic-ai/owlet-phi-2-audio',
+    trust_remote_code=True)
+
+print('Model loaded. Processing the query...')
+# text prompt
+prompt = 'What is happening in the video?'
+text = f"A chat between a curious user and an artificial intelligence assistant. \
+    The assistant gives helpful, detailed, and polite answers to the user's questions. \
+        USER: <audio>\n<image>\n{prompt} ASSISTANT:"
+input_ids = tokenizer(text, return_tensors='pt').input_ids.to(model.device)
+
+# video and audio file path
+video_file_path = '/data/sample_files/sample.mp4'
+audio_file_path = '/data/sample_files/sample.wav'
+image_tensor, audio_tensor = (tensor.to(model.device, dtype=model.dtype) for tensor in model.process(video_file_path, audio_file_path, model.config))
+# passing token indices
+IMAGE_TOKEN_INDEX = tokenizer('<image>').input_ids[0]
+AUDIO_TOKEN_INDEX = tokenizer('<audio>').input_ids[0]
+
+# generate
+output_ids = model.generate(
+    input_ids,
+    images=image_tensor,
+    audio=audio_tensor,
+    IMAGE_TOKEN_INDEX=IMAGE_TOKEN_INDEX,
+    AUDIO_TOKEN_INDEX=AUDIO_TOKEN_INDEX,
+    max_new_tokens=100,
+    use_cache=True)[0]
+
+print(f'Response: {tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()}')
+
+
+```