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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(),
).launch(debug=True)
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