diff --git a/egs/speech_llm/SPEECH2SPEECH/web_demo.py b/egs/speech_llm/SPEECH2SPEECH/web_demo.py index 6e2cfb18f..f856bf26f 100644 --- a/egs/speech_llm/SPEECH2SPEECH/web_demo.py +++ b/egs/speech_llm/SPEECH2SPEECH/web_demo.py @@ -1,26 +1,18 @@ # Modified from https://github.com/QwenLM/Qwen2.5-Omni/blob/main/web_demo.py 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_5OmniForConditionalGeneration, Qwen2_5OmniProcessor +from transformers import AutoModelForCausalLM 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: @@ -35,37 +27,9 @@ def _load_model_processor(args): 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): + def format_history(history: list): messages = [] - # messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]}) for item in history: if isinstance(item["content"], str): messages.append({"role": item['role'], "content": item['content']}) @@ -74,25 +38,7 @@ def _launch_demo(args, model, processor): 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"): + if mime_type.startswith("audio"): messages.append({ "role": item['role'], @@ -103,17 +49,17 @@ def _launch_demo(args, model, processor): }) return messages - def predict(messages, voice=DEFAULT_VOICE): + def predict(messages): print('predict history: ', messages) text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) - audios, images, videos = process_mm_info(messages, use_audio_in_video=True) + audios = [msg['content'][0]['audio'] for msg in messages if msg['role'] == 'user' and isinstance(msg['content'], list) and msg['content'][0]['type'] == 'audio'] - inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=True) + inputs = processor(text=text, audio=audios, return_tensors="pt", padding=True) inputs = inputs.to(model.device).to(model.dtype) - text_ids, audio = model.generate(**inputs, speaker=voice, use_audio_in_video=True) + text_ids, audio = model.generate(**inputs) response = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) response = response[0].split("\n")[-1] @@ -128,37 +74,31 @@ def _launch_demo(args, model, processor): 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): + def media_predict(audio, history): # 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] + files = [audio] for f in files: if f: history.append({"role": "user", "content": (f, )}) - formatted_history = format_history(history=history, - system_prompt=system_prompt,) + formatted_history = format_history(history=history) history.append({"role": "assistant", "content": ""}) - for chunk in predict(formatted_history, voice_choice): + for chunk in predict(formatted_history): 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 @@ -172,79 +112,47 @@ def _launch_demo(args, model, processor): # Final yield yield ( None, # microphone - None, # webcam history, # media_chatbot gr.update(visible=True), # submit_btn gr.update(visible=False), # stop_btn ) - 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") + with gr.Blocks() as demo: + with gr.Tab("Online"): + with gr.Row(): + with gr.Column(scale=1): + microphone = gr.Audio(sources=['microphone'], + type="filepath") + 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) + def clear_history(): + return [], 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]) + submit_event = submit_btn.click(fn=media_predict, + inputs=[ + microphone, + media_chatbot, + ], + outputs=[ + microphone, + 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]) demo.queue(default_concurrency_limit=100, max_size=100).launch(max_threads=100, ssr_mode=False, @@ -254,16 +162,13 @@ def _launch_demo(args, model, processor): server_name=args.server_name,) -DEFAULT_CKPT_PATH = "Qwen/Qwen2.5-Omni-7B" def _get_args(): parser = ArgumentParser() - parser.add_argument('-c', - '--checkpoint-path', + parser.add_argument('--checkpoint-path', type=str, - default=DEFAULT_CKPT_PATH, + default=None, 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', @@ -279,7 +184,6 @@ def _get_args(): 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