Ola-7B
Model Summary
The Ola-7B model is developed by people from Tencent, Tsinghua University and Nanyang Technological University. Based on Qwen2.5 language model, it is trained on text, image, video and audio data with a context window of 32K tokens. It can take both image/video, text and audio as input and output text.
Ola offers an on-demand solution to seamlessly and efficiently process visual inputs with arbitrary spatial sizes and temporal lengths.
- Repository: https://github.com/Ola-Omni/Ola
- Languages: English, Chinese
- Paper: https://huggingface.co/papers/2502.04328
Use
- Download the speech encoder at https://huggingface.co/THUdyh/Ola_speech_encoders.
- Replace the path in config.json with local path of speech encoders.
We provide a simple generation process for using our model. For more details, please refer to our Github Repo
import os
os.environ['LOWRES_RESIZE'] = '384x32'
os.environ['HIGHRES_BASE'] = '0x32'
os.environ['VIDEO_RESIZE'] = "0x64"
os.environ['VIDEO_MAXRES'] = "480"
os.environ['VIDEO_MINRES'] = "288"
os.environ['MAXRES'] = '1536'
os.environ['MINRES'] = '0'
os.environ['REGIONAL_POOL'] = '2x'
os.environ['FORCE_NO_DOWNSAMPLE'] = '1'
os.environ['LOAD_VISION_EARLY'] = '1'
os.environ['SKIP_LOAD_VIT'] = '1'
import gradio as gr
import torch
import re
from decord import VideoReader, cpu
from PIL import Image
import numpy as np
import transformers
import moviepy.editor as mp
from typing import Dict, Optional, Sequence, List
import librosa
import whisper
from ola.conversation import conv_templates, SeparatorStyle
from ola.model.builder import load_pretrained_model
from ola.utils import disable_torch_init
from ola.datasets.preprocess import tokenizer_image_token, tokenizer_speech_image_token, tokenizer_speech_question_image_token
from ola.mm_utils import get_model_name_from_path, KeywordsStoppingCriteria, process_anyres_video, process_anyres_highres_image_genli
from ola.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX, DEFAULT_SPEECH_TOKEN
model_path = ""
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None)
model = model.to('cuda').eval()
model = model.bfloat16()
USE_SPEECH=False
cur_dir = os.path.dirname(os.path.abspath(__file__))
def load_audio(audio_file_name):
speech_wav, samplerate = librosa.load(audio_file_name, sr=16000)
if len(speech_wav.shape) > 1:
speech_wav = speech_wav[:, 0]
speech_wav = speech_wav.astype(np.float32)
CHUNK_LIM = 480000
SAMPLE_RATE = 16000
speechs = []
speech_wavs = []
if len(speech_wav) <= CHUNK_LIM:
speech = whisper.pad_or_trim(speech_wav)
speech_wav = whisper.pad_or_trim(speech_wav)
speechs.append(speech)
speech_wavs.append(torch.from_numpy(speech_wav).unsqueeze(0))
else:
for i in range(0, len(speech_wav), CHUNK_LIM):
chunk = speech_wav[i : i + CHUNK_LIM]
if len(chunk) < CHUNK_LIM:
chunk = whisper.pad_or_trim(chunk)
speechs.append(chunk)
speech_wavs.append(torch.from_numpy(chunk).unsqueeze(0))
mels = []
for chunk in speechs:
chunk = whisper.log_mel_spectrogram(chunk, n_mels=128).permute(1, 0).unsqueeze(0)
mels.append(chunk)
mels = torch.cat(mels, dim=0)
speech_wavs = torch.cat(speech_wavs, dim=0)
if mels.shape[0] > 25:
mels = mels[:25]
speech_wavs = speech_wavs[:25]
speech_length = torch.LongTensor([mels.shape[1]] * mels.shape[0])
speech_chunks = torch.LongTensor([mels.shape[0]])
return mels, speech_length, speech_chunks, speech_wavs
def extract_audio(videos_file_path):
my_clip = mp.VideoFileClip(videos_file_path)
return my_clip.audio
def ola_inference(multimodal, audio_path):
visual, text = multimodal["files"][0], multimodal["text"]
if visual.endswith("image2.png"):
modality = "video"
visual = f"{cur_dir}/case/case1.mp4"
if visual.endswith(".mp4"):
modality = "video"
else:
modality = "image"
# input audio and video, do not parse audio in the video, else parse audio in the video
if audio_path:
USE_SPEECH = True
elif modality == "video":
USE_SPEECH = True
else:
USE_SPEECH = False
speechs = []
speech_lengths = []
speech_wavs = []
speech_chunks = []
if modality == "video":
vr = VideoReader(visual, ctx=cpu(0))
total_frame_num = len(vr)
fps = round(vr.get_avg_fps())
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, 64, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
spare_frames = vr.get_batch(frame_idx).asnumpy()
video = [Image.fromarray(frame) for frame in spare_frames]
else:
image = [Image.open(visual)]
image_sizes = [image[0].size]
if USE_SPEECH and audio_path:
audio_path = audio_path
speech, speech_length, speech_chunk, speech_wav = load_audio(audio_path)
speechs.append(speech.bfloat16().to('cuda'))
speech_lengths.append(speech_length.to('cuda'))
speech_chunks.append(speech_chunk.to('cuda'))
speech_wavs.append(speech_wav.to('cuda'))
print('load audio')
elif USE_SPEECH and not audio_path:
# parse audio in the video
audio = extract_audio(visual)
audio.write_audiofile("./video_audio.wav")
video_audio_path = './video_audio.wav'
speech, speech_length, speech_chunk, speech_wav = load_audio(video_audio_path)
speechs.append(speech.bfloat16().to('cuda'))
speech_lengths.append(speech_length.to('cuda'))
speech_chunks.append(speech_chunk.to('cuda'))
speech_wavs.append(speech_wav.to('cuda'))
else:
speechs = [torch.zeros(1, 3000, 128).bfloat16().to('cuda')]
speech_lengths = [torch.LongTensor([3000]).to('cuda')]
speech_wavs = [torch.zeros([1, 480000]).to('cuda')]
speech_chunks = [torch.LongTensor([1]).to('cuda')]
conv_mode = "qwen_1_5"
if text:
qs = text
else:
qs = ''
if USE_SPEECH and audio_path:
qs = DEFAULT_IMAGE_TOKEN + "
" + "User's question in speech: " + DEFAULT_SPEECH_TOKEN + '
'
elif USE_SPEECH:
qs = DEFAULT_SPEECH_TOKEN + DEFAULT_IMAGE_TOKEN + "
" + qs
else:
qs = DEFAULT_IMAGE_TOKEN + "
" + qs
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if USE_SPEECH and audio_path:
input_ids = tokenizer_speech_question_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')
elif USE_SPEECH:
input_ids = tokenizer_speech_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')
else:
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')
if modality == "video":
video_processed = []
for idx, frame in enumerate(video):
image_processor.do_resize = False
image_processor.do_center_crop = False
frame = process_anyres_video(frame, image_processor)
if frame_idx is not None and idx in frame_idx:
video_processed.append(frame.unsqueeze(0))
elif frame_idx is None:
video_processed.append(frame.unsqueeze(0))
if frame_idx is None:
frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
video_processed = torch.cat(video_processed, dim=0).bfloat16().to("cuda")
video_processed = (video_processed, video_processed)
video_data = (video_processed, (384, 384), "video")
else:
image_processor.do_resize = False
image_processor.do_center_crop = False
image_tensor, image_highres_tensor = [], []
for visual in image:
image_tensor_, image_highres_tensor_ = process_anyres_highres_image_genli(visual, image_processor)
image_tensor.append(image_tensor_)
image_highres_tensor.append(image_highres_tensor_)
if all(x.shape == image_tensor[0].shape for x in image_tensor):
image_tensor = torch.stack(image_tensor, dim=0)
if all(x.shape == image_highres_tensor[0].shape for x in image_highres_tensor):
image_highres_tensor = torch.stack(image_highres_tensor, dim=0)
if type(image_tensor) is list:
image_tensor = [_image.bfloat16().to("cuda") for _image in image_tensor]
else:
image_tensor = image_tensor.bfloat16().to("cuda")
if type(image_highres_tensor) is list:
image_highres_tensor = [_image.bfloat16().to("cuda") for _image in image_highres_tensor]
else:
image_highres_tensor = image_highres_tensor.bfloat16().to("cuda")
pad_token_ids = 151643
attention_masks = input_ids.ne(pad_token_ids).long().to('cuda')
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
gen_kwargs = {}
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 1024
if "temperature" not in gen_kwargs:
gen_kwargs["temperature"] = 0.2
if "top_p" not in gen_kwargs:
gen_kwargs["top_p"] = None
if "num_beams" not in gen_kwargs:
gen_kwargs["num_beams"] = 1
with torch.inference_mode():
if modality == "video":
output_ids = model.generate(
inputs=input_ids,
images=video_data[0][0],
images_highres=video_data[0][1],
modalities=video_data[2],
speech=speechs,
speech_lengths=speech_lengths,
speech_chunks=speech_chunks,
speech_wav=speech_wavs,
attention_mask=attention_masks,
use_cache=True,
stopping_criteria=[stopping_criteria],
do_sample=True if gen_kwargs["temperature"] > 0 else False,
temperature=gen_kwargs["temperature"],
top_p=gen_kwargs["top_p"],
num_beams=gen_kwargs["num_beams"],
max_new_tokens=gen_kwargs["max_new_tokens"],
)
else:
output_ids = model.generate(
inputs=input_ids,
images=image_tensor,
images_highres=image_highres_tensor,
image_sizes=image_sizes,
modalities=['image'],
speech=speechs,
speech_lengths=speech_lengths,
speech_chunks=speech_chunks,
speech_wav=speech_wavs,
attention_mask=attention_masks,
use_cache=True,
stopping_criteria=[stopping_criteria],
do_sample=True if gen_kwargs["temperature"] > 0 else False,
temperature=gen_kwargs["temperature"],
top_p=gen_kwargs["top_p"],
num_beams=gen_kwargs["num_beams"],
max_new_tokens=gen_kwargs["max_new_tokens"],
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
return outputs, None
Model Architecture
-
Architecture: Pre-trained Oryx-ViT + Qwen2.5-7B
-
Data: a mixture of more than 5M image/video/audio data, training for 3 stage.
-
Precision: BFloat16
Hardware & Software
-
Hardware: 64 * NVIDIA Tesla A100
-
Orchestration: HuggingFace Trainer
-
Code: Pytorch
Citation
@article{liu2025ola, title={Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive Modality Alignment}, author={Liu, Zuyan and Dong, Yuhao and Wang, Jiahui and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming}, journal={arXiv preprint arXiv:2502.04328}, year={2025} }