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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
import os
import librosa
import joblib
import numpy as np
import lightgbm
from .utils.evaluation import AudioEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
from dotenv import load_dotenv
load_dotenv()
router = APIRouter()
DESCRIPTION = "LGBM Classifier Baseline on Mel-frequency cepstral coefficients"
ROUTE = "/audio"
@router.post(ROUTE, tags=["Audio Task"],
description=DESCRIPTION)
async def evaluate_audio(request: AudioEvaluationRequest):
"""
Evaluate audio classification for rainforest sound detection.
Current Model: LGBM
- 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
dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
print('dataset loaded')
# Split dataset
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
print('train/test splitted')
# 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.
#--------------------------------------------------------------------------------------------
def compute_mfcc(row, sr):
audio_array = row['audio']['array']
mfcc = librosa.feature.mfcc(y=audio_array, sr=sr, n_mfcc=5)
return np.mean(mfcc, axis=1)
def predict_new_audio(model, dataset, sr):
list_mfcc = [compute_mfcc(row, sr) for row in dataset]
print('mfcc computed')
array_mfcc = np.vstack(list_mfcc)
predictions = model.predict(array_mfcc)
return predictions
model_filename = "lightgbm_baseline_87_acc.pkl"
clf = joblib.load(model_filename)
print('model loaded')
predictions = predict_new_audio(clf, test_dataset, 12000)
print('predictions done')
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
true_labels = test_dataset["label"]
accuracy = accuracy_score(true_labels, predictions)
# 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 |