README.md

---
license: mit
---

# LongVA-7B-Qwen2-Audio


LongVA-7B-Qwen2-Audio is an extension of [LongVA-7B](https://github.com/EvolvingLMMs-Lab/LongVA), further trained using the [LLaVA-NeXT-Audio](https://huggingface.co/datasets/ColorfulAI/LLaVA-NeXT-Audio) dataset for 0.4 epochs.


## Usage

*Please refer to [M4](https://github.com/patrick-tssn/M4) to install relvevant packages*

```python

import os
from PIL import Image
import numpy as np
import torchaudio
import torch
from decord import VideoReader, cpu
import whisper
# fix seed
torch.manual_seed(0)

from intersuit.model.builder import load_pretrained_model
from intersuit.mm_utils import tokenizer_image_speech_tokens, process_images
from intersuit.constants import IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX

import warnings
warnings.filterwarnings("ignore")

model_path = "ColorfulAI/LongVA-7B-Qwen2-Audio"
video_path = "local_demo/assets/water.mp4"
audio_path = "local_demo/wav/infer.wav"
max_frames_num = 16 # you can change this to several thousands so long you GPU memory can handle it :)
gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "llava_qwen", device_map="cuda:0")

query = "Give a detailed caption of the video as if I am blind."
query = None # comment this to use ChatTTS to convert the query to audio

#video input
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image><|im_end|>\n<|im_start|>user\n<speech>\n<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer_image_speech_tokens(prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
video_tensor = image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].to(model.device, dtype=torch.float16)

#audio input
# process speech for input question
if query is not None:
    import ChatTTS
    chat = ChatTTS.Chat()
    chat.load(source='local', compile=True)
    audio_path = "./local_demo/wav/" + "infer.wav"
    if os.path.exists(audio_path): os.remove(audio_path) # refresh
    if not os.path.exists(audio_path):
        wav = chat.infer(query)
        try:
            torchaudio.save(audio_path, torch.from_numpy(wav).unsqueeze(0), 24000)
        except:
            torchaudio.save(audio_path, torch.from_numpy(wav), 24000)
    print(f"Human: {query}")
    
else:
    print("Human: <audio>")
    
speech = whisper.load_audio(audio_path)
speech = whisper.pad_or_trim(speech)
speech = whisper.log_mel_spectrogram(speech, n_mels=128).permute(1, 0).to(device=model.device, dtype=torch.float16)
speech_length = torch.LongTensor([speech.shape[0]]).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(input_ids, images=[video_tensor],  modalities=["video"], speeches=speech.unsqueeze(0), speech_lengths=speech_length, **gen_kwargs)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(f"Agent: {outputs}")

```