We introduce LiveCC, the first video LLM capable of real-time commentary, trained with a novel video-ASR streaming method, SOTA on both streaming and offline benchmarks.

Project Page: https://showlab.github.io/livecc

[!Important] This is the Base model. The instruct model is at LiveCC-7B-Instruct.

Training with Streaming Frame-Words Paradigm

Quickstart

Gradio Demo

Please refer to https://github.com/showlab/livecc:

Hands-on

Like qwen-vl-utils, we offer a toolkit to help you handle various types of visual input more conveniently, especially on video streaming inputs. You can install it using the following command:

pip install qwen-vl-utils livecc-utils liger_kernel

Here we show a code snippet to show you how to do real-time video commentary with transformers and the above utils:

python

import functools, torch, os, tqdm
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
apply_liger_kernel_to_qwen2_vl() # important. our model is trained with this. keep consistency
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, LogitsProcessor, logging
from livecc_utils import prepare_multiturn_multimodal_inputs_for_generation, get_smart_resized_clip, get_smart_resized_video_reader
from qwen_vl_utils import process_vision_info

class LiveCCDemoInfer:
  fps = 2
  initial_fps_frames = 6
  streaming_fps_frames = 2
  initial_time_interval = initial_fps_frames / fps
  streaming_time_interval = streaming_fps_frames / fps
  frame_time_interval = 1 / fps
  def __init__(self, model_path: str = None, device_id: int = 0):
      self.model = Qwen2VLForConditionalGeneration.from_pretrained(
          model_path, torch_dtype="auto", 
          device_map=f'cuda:{device_id}', 
          attn_implementation='flash_attention_2'
      )
      self.processor = AutoProcessor.from_pretrained(model_path, use_fast=False)
      self.model.prepare_inputs_for_generation = functools.partial(prepare_multiturn_multimodal_inputs_for_generation, self.model)
      message = {
          "role": "user",
          "content": [
              {"type": "text", "text": 'livecc'},
          ]
      }
      texts = self.processor.apply_chat_template([message], tokenize=False)
      self.system_prompt_offset = texts.index('<|im_start|>user')
      self._cached_video_readers_with_hw = {}


  def live_cc(
      self,
      query: str,
      state: dict,
      max_pixels: int = 384 * 28 * 28,
      default_query: str = 'Please describe the video.',
      do_sample: bool = True,
      repetition_penalty: float = 1.05,
      **kwargs,
  ): 
      """
      state: dict, (maybe) with keys:
          video_path: str, video path
          video_timestamp: float, current video timestamp
          last_timestamp: float, last processed video timestamp
          last_video_pts_index: int, last processed video frame index
          video_pts: np.ndarray, video pts
          last_history: list, last processed history
          past_key_values: llm past_key_values
          past_ids: past generated ids
      """
      # 1. preparation: video_reader, and last processing info
      video_timestamp, last_timestamp = state.get('video_timestamp', 0), state.get('last_timestamp', -1 / self.fps)
      video_path = state['video_path']
      if video_path not in self._cached_video_readers_with_hw:
          self._cached_video_readers_with_hw[video_path] = get_smart_resized_video_reader(video_path, max_pixels)
          video_reader = self._cached_video_readers_with_hw[video_path][0]
          video_reader.get_frame_timestamp(0)
          state['video_pts'] = torch.from_numpy(video_reader._frame_pts[:, 1])
          state['last_video_pts_index'] = -1
      video_pts = state['video_pts']
      if last_timestamp + self.frame_time_interval > video_pts[-1]:
          state['video_end'] = True
          return 
      video_reader, resized_height, resized_width = self._cached_video_readers_with_hw[video_path]
      last_video_pts_index = state['last_video_pts_index']

      # 2. which frames will be processed
      initialized = last_timestamp >= 0
      if not initialized:
          video_timestamp = max(video_timestamp, self.initial_time_interval)
      if video_timestamp <= last_timestamp + self.frame_time_interval:
          return
      timestamps = torch.arange(last_timestamp + self.frame_time_interval, video_timestamp, self.frame_time_interval) # add compensation
      
      # 3. fetch frames in required timestamps
      clip, clip_timestamps, clip_idxs = get_smart_resized_clip(video_reader, resized_height, resized_width, timestamps, video_pts, video_pts_index_from=last_video_pts_index+1)
      state['last_video_pts_index'] = clip_idxs[-1]
      state['last_timestamp'] = clip_timestamps[-1]

      # 4. organize to interleave frames
      interleave_clips, interleave_timestamps = [], []
      if not initialized:
          interleave_clips.append(clip[:self.initial_fps_frames])
          interleave_timestamps.append(clip_timestamps[:self.initial_fps_frames])
          clip = clip[self.initial_fps_frames:]
          clip_timestamps = clip_timestamps[self.initial_fps_frames:]
      if len(clip) > 0:
          interleave_clips.extend(list(clip.split(self.streaming_fps_frames)))
          interleave_timestamps.extend(list(clip_timestamps.split(self.streaming_fps_frames)))

      # 5. make conversation and send to model
      for clip, timestamps in zip(interleave_clips, interleave_timestamps):
          start_timestamp, stop_timestamp = timestamps[0].item(), timestamps[-1].item() + self.frame_time_interval
          message = {
              "role": "user",
              "content": [
                  {"type": "text", "text": f'Time={start_timestamp:.1f}-{stop_timestamp:.1f}s'},
                  {"type": "video", "video": clip}
              ]
          }
          if not query and not state.get('query', None):
              query = default_query
              print(f'No query provided, use default_query={default_query}')
          if query and state.get('query', None) != query:
              message['content'].append({"type": "text", "text": query})
              state['query'] = query
          texts = self.processor.apply_chat_template([message], tokenize=False, add_generation_prompt=True, return_tensors='pt')
          past_ids = state.get('past_ids', None)
          if past_ids is not None:
              texts = '<|im_end|>\n' + texts[self.system_prompt_offset:]
          inputs = self.processor(
              text=texts,
              images=None,
              videos=[clip],
              return_tensors="pt",
              return_attention_mask=False
          )
          inputs.to('cuda')
          if past_ids is not None:
              inputs['input_ids'] = torch.cat([past_ids, inputs.input_ids], dim=1) 
          outputs = self.model.generate(
              **inputs, past_key_values=state.get('past_key_values', None), 
              return_dict_in_generate=True, do_sample=do_sample, 
              repetition_penalty=repetition_penalty,
          )
          state['past_key_values'] = outputs.past_key_values
          state['past_ids'] = outputs.sequences[:, :-1]
          yield (start_timestamp, stop_timestamp), self.processor.decode(outputs.sequences[0, inputs.input_ids.size(1):], skip_special_tokens=True), state

model_path = 'chenjoya/LiveCC-7B-Base'
# download a test video at: https://github.com/showlab/livecc/blob/main/demo/sources/howto_fix_laptop_mute_1080p.mp4
video_path = "demo/sources/howto_fix_laptop_mute_1080p.mp4"
query = "Please describe the video."

infer = LiveCCDemoInfer(model_path=model_path)
state = {'video_path': video_path}
commentaries = []
t = 0
for t in range(31):
    state['video_timestamp'] = t
    for (start_t, stop_t), response, state in infer.live_cc(
        query=query, state=state, 
        max_pixels = 384 * 28 * 28, repetition_penalty=1.05, 
        streaming_eos_base_threshold=0.0, streaming_eos_threshold_step=0
    ):
        print(f'{start_t}s-{stop_t}s: {response}')
        commentaries.append([start_t, stop_t, response])
    if state.get('video_end', False):
        break
    t += 1

Limitations

This model is only performed video-ASR streaming pre-training, so it may not support well in common video qa.
When performing real-time video commentary, it may appear collapse --- e.g., repeat pattern. If you encounter this situation, try to adjust repetition_penalty, streaming_eos_base_threshold, and streaming_eos_threshold_step.
This model only has a context window of 32768. Using more visual tokens per frame (e.g. 768 * 28 * 28) will have better performance, but will shorten the working duration.

These limitations serve as ongoing directions for model optimization and improvement, and we are committed to continually enhancing the model's performance and scope of application.

Citation

If you find our work helpful, feel free to give us a cite.

@article{livecc,
  author       = {Joya Chen and Ziyun Zeng and Yiqi Lin and Wei Li and Zejun Ma and Mike Zheng Shou},
  title        = {LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale},
  journal      = {arXiv preprint arXiv:2504.16030}
  year         = {2025},
}