import os import gradio as gr import torch import numpy as np import librosa from efficientat.models.MobileNetV3 import get_model as get_mobilenet, get_ensemble_model from efficientat.models.preprocess import AugmentMelSTFT from efficientat.helpers.utils import NAME_TO_WIDTH, labels from torch import autocast from contextlib import nullcontext from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate from langchain.chains.conversation.memory import ConversationalBufferWindowMemory MODEL_NAME = "mn40_as" device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') model = get_mobilenet(width_mult=NAME_TO_WIDTH(MODEL_NAME), pretrained_name=MODEL_NAME) model.to(device) model.eval() cached_audio_class = "c" template = None prompt = None chain = None def format_classname(classname): return classname.capitalize() def audio_tag( audio_path, human_input, sample_rate=32000, window_size=800, hop_size=320, n_mels=128, cuda=True, ): (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) mel = AugmentMelSTFT(n_mels=n_mels, sr=sample_rate, win_length=window_size, hopsize=hop_size) mel.to(device) mel.eval() waveform = torch.from_numpy(waveform[None, :]).to(device) # our models are trained in half precision mode (torch.float16) # run on cuda with torch.float16 to get the best performance # running on cpu with torch.float32 gives similar performance, using torch.bfloat16 is worse with torch.no_grad(), autocast(device_type=device.type) if cuda and torch.cuda.is_available() else nullcontext(): spec = mel(waveform) preds, features = model(spec.unsqueeze(0)) preds = torch.sigmoid(preds.float()).squeeze().cpu().numpy() sorted_indexes = np.argsort(preds)[::-1] output = {} # Print audio tagging top probabilities label = labels[sorted_indexes[0]] return formatted_message(format_classname(label), human_input) def format_classname(classname): return classname.capitalize() def formatted_message(audio_class, human_input): global cached_audio_class formatted_classname = format_classname(audio_class) if cached_audio_class != formatted_classname: cached_audio_class = formatted_classname prefix = f"""You are going to act as a magical tool that allows for humans to communicate with non-human entities like rocks, crackling fire, trees, animals, and the wind. In order to do this, we're going to provide you the human's text input for the conversation. The goal is for you to embody that non-human entity and converse with the human. Examples: Non-human Entity: Tree Human Input: Hello tree Tree: Hello human, I am a tree Let's begin: Non-human Entity: {formatted_classname}""" suffix = f''' {{history}} Human Input: {{human_input}} {formatted_classname}:''' template = prefix + suffix prompt = PromptTemplate( input_variables=["history", "human_input"], template=template ) chatgpt_chain = LLMChain( llm=OpenAI(temperature=.5, openai_api_key=session_token), prompt=prompt, verbose=True, memory=ConversationalBufferWindowMemory(k=2, ai=formatted_classname), ) output = chatgpt_chain.predict(human_input=human_input) return output with gr.Blocks() as demo: gr.HTML("""
anychat

Non-Human entities have many things to say, listen to them!

""") with gr.Row(): with gr.Column(): aud = gr.Audio(source="upload", type="filepath", label="Your audio") inp = gr.Textbox() out = gr.Textbox() btn = gr.Button("Run") btn.click(fn=audio_tag, inputs=[aud, inp], outputs=out) demo.launch()