import io import os import ffmpeg import numpy as np import gradio as gr import soundfile as sf import modelscope_studio.components.base as ms import modelscope_studio.components.antd as antd import gradio.processing_utils as processing_utils from transformers import Qwen2_5OmniModel, Qwen2_5OmniProcessor from gradio_client import utils as client_utils from qwen_omni_utils import process_mm_info from argparse import ArgumentParser def _load_model_processor(args): if args.cpu_only: device_map = 'cpu' else: device_map = 'auto' # Check if flash-attn2 flag is enabled and load model accordingly if args.flash_attn2: model = Qwen2_5OmniModel.from_pretrained(args.checkpoint_path, torch_dtype='auto', attn_implementation='flash_attention_2', device_map=device_map) else: model = Qwen2_5OmniModel.from_pretrained(args.checkpoint_path, device_map=device_map) processor = Qwen2_5OmniProcessor.from_pretrained(args.checkpoint_path) return model, processor def _launch_demo(args, model, processor): # Voice settings VOICE_LIST = ['Chelsie', 'Ethan'] DEFAULT_VOICE = 'Chelsie' default_system_prompt = 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.' language = args.ui_language def get_text(text: str, cn_text: str): if language == 'en': return text if language == 'zh': return cn_text return text def convert_webm_to_mp4(input_file, output_file): try: ( ffmpeg .input(input_file) .output(output_file, acodec='aac', ar='16000', audio_bitrate='192k') .run(quiet=True, overwrite_output=True) ) print(f"Conversion successful: {output_file}") except ffmpeg.Error as e: print("An error occurred during conversion.") print(e.stderr.decode('utf-8')) def format_history(history: list, system_prompt: str): messages = [] messages.append({"role": "system", "content": system_prompt}) for item in history: if isinstance(item["content"], str): messages.append({"role": item['role'], "content": item['content']}) elif item["role"] == "user" and (isinstance(item["content"], list) or isinstance(item["content"], tuple)): file_path = item["content"][0] mime_type = client_utils.get_mimetype(file_path) if mime_type.startswith("image"): messages.append({ "role": item['role'], "content": [{ "type": "image", "image": file_path }] }) elif mime_type.startswith("video"): messages.append({ "role": item['role'], "content": [{ "type": "video", "video": file_path }] }) elif mime_type.startswith("audio"): messages.append({ "role": item['role'], "content": [{ "type": "audio", "audio": file_path, }] }) return messages def predict(messages, voice=DEFAULT_VOICE): print('predict history: ', messages) text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) audios, images, videos = process_mm_info(messages, True) inputs = processor(text=text, audios=audios, images=images, videos=videos, return_tensors="pt", padding=True) inputs = inputs.to(model.device).to(model.dtype) text_ids, audio = model.generate(**inputs, spk=voice, use_audio_in_video=True) response = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) response = response[0].split("\n")[-1] yield {"type": "text", "data": response} audio = np.array(audio * 32767).astype(np.int16) wav_io = io.BytesIO() sf.write(wav_io, audio, samplerate=24000, format="WAV") wav_io.seek(0) wav_bytes = wav_io.getvalue() audio_path = processing_utils.save_bytes_to_cache( wav_bytes, "audio.wav", cache_dir=demo.GRADIO_CACHE) yield {"type": "audio", "data": audio_path} def media_predict(audio, video, history, system_prompt, voice_choice): # First yield yield ( None, # microphone None, # webcam history, # media_chatbot gr.update(visible=False), # submit_btn gr.update(visible=True), # stop_btn ) if video is not None: convert_webm_to_mp4(video, video.replace('.webm', '.mp4')) video = video.replace(".webm", ".mp4") files = [audio, video] for f in files: if f: history.append({"role": "user", "content": (f, )}) formatted_history = format_history(history=history, system_prompt=system_prompt,) history.append({"role": "assistant", "content": ""}) for chunk in predict(formatted_history, voice_choice): if chunk["type"] == "text": history[-1]["content"] = chunk["data"] yield ( None, # microphone None, # webcam history, # media_chatbot gr.update(visible=False), # submit_btn gr.update(visible=True), # stop_btn ) if chunk["type"] == "audio": history.append({ "role": "assistant", "content": gr.Audio(chunk["data"]) }) # Final yield yield ( None, # microphone None, # webcam history, # media_chatbot gr.update(visible=True), # submit_btn gr.update(visible=False), # stop_btn ) def chat_predict(text, audio, image, video, history, system_prompt, voice_choice): # Process text input if text: history.append({"role": "user", "content": text}) # Process audio input if audio: history.append({"role": "user", "content": (audio, )}) # Process image input if image: history.append({"role": "user", "content": (image, )}) # Process video input if video: history.append({"role": "user", "content": (video, )}) formatted_history = format_history(history=history, system_prompt=system_prompt) yield None, None, None, None, history history.append({"role": "assistant", "content": ""}) for chunk in predict(formatted_history, voice_choice): if chunk["type"] == "text": history[-1]["content"] = chunk["data"] yield gr.skip(), gr.skip(), gr.skip(), gr.skip( ), history if chunk["type"] == "audio": history.append({ "role": "assistant", "content": gr.Audio(chunk["data"]) }) yield gr.skip(), gr.skip(), gr.skip(), gr.skip(), history with gr.Blocks() as demo, ms.Application(), antd.ConfigProvider(): with gr.Sidebar(open=False): system_prompt_textbox = gr.Textbox(label="System Prompt", value=default_system_prompt) with antd.Flex(gap="small", justify="center", align="center"): with antd.Flex(vertical=True, gap="small", align="center"): antd.Typography.Title("Qwen2.5-Omni Demo", level=1, elem_style=dict(margin=0, fontSize=28)) with antd.Flex(vertical=True, gap="small"): antd.Typography.Text(get_text("🎯 Instructions for use:", "🎯 使用说明："), strong=True) antd.Typography.Text( get_text( "1️⃣ Click the Audio Record button or the Camera Record button.", "1️⃣ 点击音频录制按钮，或摄像头-录制按钮")) antd.Typography.Text( get_text("2️⃣ Input audio or video.", "2️⃣ 输入音频或者视频")) antd.Typography.Text( get_text( "3️⃣ Click the submit button and wait for the model's response.", "3️⃣ 点击提交并等待模型的回答")) voice_choice = gr.Dropdown(label="Voice Choice", choices=VOICE_LIST, value=DEFAULT_VOICE) with gr.Tabs(): with gr.Tab("Online"): with gr.Row(): with gr.Column(scale=1): microphone = gr.Audio(sources=['microphone'], type="filepath") webcam = gr.Video(sources=['webcam'], height=400, include_audio=True) submit_btn = gr.Button(get_text("Submit", "提交"), variant="primary") stop_btn = gr.Button(get_text("Stop", "停止"), visible=False) clear_btn = gr.Button(get_text("Clear History", "清除历史")) with gr.Column(scale=2): media_chatbot = gr.Chatbot(height=650, type="messages") def clear_history(): return [], gr.update(value=None), gr.update(value=None) submit_event = submit_btn.click(fn=media_predict, inputs=[ microphone, webcam, media_chatbot, system_prompt_textbox, voice_choice ], outputs=[ microphone, webcam, media_chatbot, submit_btn, stop_btn ]) stop_btn.click( fn=lambda: (gr.update(visible=True), gr.update(visible=False)), inputs=None, outputs=[submit_btn, stop_btn], cancels=[submit_event], queue=False) clear_btn.click(fn=clear_history, inputs=None, outputs=[media_chatbot, microphone, webcam]) with gr.Tab("Offline"): chatbot = gr.Chatbot(type="messages", height=650) # Media upload section in one row with gr.Row(equal_height=True): audio_input = gr.Audio(sources=["upload"], type="filepath", label="Upload Audio", elem_classes="media-upload", scale=1) image_input = gr.Image(sources=["upload"], type="filepath", label="Upload Image", elem_classes="media-upload", scale=1) video_input = gr.Video(sources=["upload"], label="Upload Video", elem_classes="media-upload", scale=1) # Text input section text_input = gr.Textbox(show_label=False, placeholder="Enter text here...") # Control buttons with gr.Row(): submit_btn = gr.Button(get_text("Submit", "提交"), variant="primary", size="lg") stop_btn = gr.Button(get_text("Stop", "停止"), visible=False, size="lg") clear_btn = gr.Button(get_text("Clear History", "清除历史"), size="lg") def clear_chat_history(): return [], gr.update(value=None), gr.update( value=None), gr.update(value=None), gr.update(value=None) submit_event = gr.on( triggers=[submit_btn.click, text_input.submit], fn=chat_predict, inputs=[ text_input, audio_input, image_input, video_input, chatbot, system_prompt_textbox, voice_choice ], outputs=[ text_input, audio_input, image_input, video_input, chatbot ]) stop_btn.click(fn=lambda: (gr.update(visible=True), gr.update(visible=False)), inputs=None, outputs=[submit_btn, stop_btn], cancels=[submit_event], queue=False) clear_btn.click(fn=clear_chat_history, inputs=None, outputs=[ chatbot, text_input, audio_input, image_input, video_input ]) # Add some custom CSS to improve the layout gr.HTML(""" """) demo.queue(default_concurrency_limit=100, max_size=100).launch(max_threads=100, ssr_mode=False, share=args.share, inbrowser=args.inbrowser, server_port=args.server_port, server_name=args.server_name,) DEFAULT_CKPT_PATH = "Qwen/Qwen2.5-Omni-7B" def _get_args(): parser = ArgumentParser() parser.add_argument('-c', '--checkpoint-path', type=str, default=DEFAULT_CKPT_PATH, help='Checkpoint name or path, default to %(default)r') parser.add_argument('--cpu-only', action='store_true', help='Run demo with CPU only') parser.add_argument('--flash-attn2', action='store_true', default=False, help='Enable flash_attention_2 when loading the model.') parser.add_argument('--share', action='store_true', default=False, help='Create a publicly shareable link for the interface.') parser.add_argument('--inbrowser', action='store_true', default=False, help='Automatically launch the interface in a new tab on the default browser.') parser.add_argument('--server-port', type=int, default=7860, help='Demo server port.') parser.add_argument('--server-name', type=str, default='127.0.0.1', help='Demo server name.') parser.add_argument('--ui-language', type=str, choices=['en', 'zh'], default='en', help='Display language for the UI.') args = parser.parse_args() return args if __name__ == "__main__": args = _get_args() args.share = True model, processor = _load_model_processor(args) _launch_demo(args, model, processor)