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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from accelerate import Accelerator
from tqdm import tqdm
from torch.amp import autocast
import random
import os
import torch
from .utils.evaluation import AudioEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
from audio_utils import AudioClassifier, AudioDataset, Config, collate_fn, Evaluator
from transformers import AutoModelForImageClassification
from torch.utils.data import DataLoader
from loguru import logger
from dotenv import load_dotenv
load_dotenv()
router = APIRouter()
DESCRIPTION = "Audio pipeline to classify sounds."
ROUTE = "/audio"
device = "cuda"
@router.post(ROUTE, tags=["Audio Task"],
description=DESCRIPTION)
async def evaluate_audio(request: AudioEvaluationRequest): #, model_path: str):
"""
Evaluate audio classification for rainforest sound detection.
Current Model: Random Baseline
- Makes random predictions from the label space (0-1)
- Used as a baseline for comparison
"""
# Get space info
username, space_url = get_space_info()
# Define the label mapping
LABEL_MAPPING = {
"chainsaw": 0,
"environment": 1
}
# Load and prepare the dataset
# Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate
config = Config()
accelerator = Accelerator()
device = accelerator.device
dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
test_dataset = dataset["test"]
test_dataset = test_dataset.filter(lambda x: len(x["audio"]["array"]) > 0)
true_labels = test_dataset["label"]
test_dataset = AudioDataset(test_dataset)
test_loader = DataLoader(test_dataset, batch_size=2 * config.BATCH_SIZE, shuffle=False, collate_fn=collate_fn, num_workers=config.NUM_WORKERS, pin_memory=True)
model = AudioClassifier(config.MODEL_NAME, config.MODEL_PATH)
model, test_loader = accelerator.prepare(model, test_loader)
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE CODE HERE
# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
#--------------------------------------------------------------------------------------------
predictions = []
logger.info("Running inference ...")
evaluator = Evaluator(model, test_loader, device)
predictions = evaluator.evaluate()
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
print("accuracy", accuracy)
# Prepare results dictionary
results = {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION,
"accuracy": float(accuracy),
"energy_consumed_wh": emissions_data.energy_consumed * 1000,
"emissions_gco2eq": emissions_data.emissions * 1000,
"emissions_data": clean_emissions_data(emissions_data),
"api_route": ROUTE,
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
return results |