import gradio as gr
import time
import transformers
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
from io import BytesIO
from urllib.request import urlopen
import librosa
import os, json
from sys import argv
from vllm import LLM, SamplingParams
import vllm
import re
def load_model_processor(model_path):
processor = AutoProcessor.from_pretrained(model_path)
llm = LLM(
model=model_path, trust_remote_code=True, gpu_memory_utilization=0.8,
enforce_eager=True, device = "cuda",
limit_mm_per_prompt={"audio": 5},
)
return llm, processor
model_path1 = "SeaLLMs/SeaLLMs-Audio-7B"
model1, processor1 = load_model_processor(model_path1)
def response_to_audio_conv(conversation, model=None, processor=None, temperature = 0.7,repetition_penalty=1.1, top_p = 0.5,max_new_tokens = 2048):
turn = conversation[-1]
if turn["role"] == "user":
for content in turn['content']:
if content["type"] == "text":
if contains_chinese(content["text"]):
return "ERROR! This demo does not support Chinese!"
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = []
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
if ele['audio_url'] != None:
audios.append(librosa.load(
ele['audio_url'],
sr=processor.feature_extractor.sampling_rate)[0]
)
sampling_params = SamplingParams(
temperature=temperature, max_tokens=max_new_tokens, repetition_penalty=repetition_penalty, top_p=top_p, top_k=20,
stop_token_ids=[],
)
input = {
'prompt': text,
'multi_modal_data': {
'audio': [(audio, 16000) for audio in audios]
}
}
output = model.generate([input], sampling_params=sampling_params)[0]
response = output.outputs[0].text
if contains_chinese(response):
return "ERROR! This demo does not support Chinese! Try a different instruction/prompt!"
return response
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def contains_chinese(text):
# Regular expression for Chinese characters
chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]')
return bool(chinese_char_pattern.search(text))
def add_message(history, message):
paths = []
for turn in history:
if turn['role'] == "user" and type(turn['content']) != str:
paths.append(turn['content'][0])
for x in message["files"]:
if x not in paths:
history.append({"role": "user", "content": {"path": x}})
if message["text"] is not None:
history.append({"role": "user", "content": message["text"]})
return history, gr.MultimodalTextbox(value=None, interactive=False)
def format_user_messgae(message):
if type(message['content']) == str:
return {"role": "user", "content": [{"type": "text", "text": message['content']}]}
else:
return {"role": "user", "content": [{"type": "audio", "audio_url": message['content'][0]}]}
def history_to_conversation(history):
conversation = []
audio_paths = []
for turn in history:
if turn['role'] == "user":
if not turn['content']:
continue
turn = format_user_messgae(turn)
if turn['content'][0]['type'] == 'audio':
if turn['content'][0]['audio_url'] in audio_paths:
continue
else:
audio_paths.append(turn['content'][0]['audio_url'])
if len(conversation) > 0 and conversation[-1]["role"] == "user":
conversation[-1]['content'].append(turn['content'][0])
else:
conversation.append(turn)
else:
conversation.append(turn)
print(json.dumps(conversation, indent=4, ensure_ascii=False))
return conversation
def bot(history: list, temperature = 0.7,repetition_penalty=1.1, top_p = 0.5,
max_new_tokens = 2048):
conversation = history_to_conversation(history)
response = response_to_audio_conv(conversation, model=model1, processor=processor1, temperature = temperature,repetition_penalty=repetition_penalty, top_p = top_p, max_new_tokens = max_new_tokens)
# response = "Nice to meet you!"
print("Bot:",response)
history.append({"role": "assistant", "content": ""})
for character in response:
history[-1]["content"] += character
time.sleep(0.01)
yield history
with gr.Blocks() as demo:
gr.HTML("""
""")
gr.HTML("""
SeaLLMs-Audio-Demo
""")
gr.HTML(
"""
This WebUI is based on SeaLLMs-Audio-7B, developed by Alibaba DAMO Academy.
You can interact with the chatbot in English, Indonesian, Thai, or Vietnamese.
For the input, you can provide audio and/or text.
