app.py

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()    


def audio_tag(
    audio_path,
    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(label)


cached_audio_class = None 
template = None
prompt = None
chain = None

def formatted_message(audio_class):
    if cached_audio_class != audio_class:
        cached_audio_class = audio_class
    
        prefix = '''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 a data string which 
        represents the audio input, the source of the audio, and the human's text input for the conversation. 
        The goal is for you to embody the source of the audio, and use the length and variance in the signal data to produce 
        plausible responses to the humans input. Remember to embody the the source data. When we start the conversation, 
        you should generate a "personality profile" for the source and utilize that personality profile in your responses. 
        Let's begin:'''
        suffix = f'''Source: {audio_class}
        Length of Audio in Seconds: {audio_length} 
        Human Input: {userText}
        {audio_class} Response:'''
        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),
        )

    output = chatgpt_chain.predict(human_input=message)

    return output


demo = gr.Interface(
    audio_tag,
    gr.Audio(source="upload", type="filepath", label="Your audio"),
    gr.Textbox(),
    examples=[["metro_station-paris.wav"]]
).launch(debug=True)