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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import os
import joblib
import librosa
import numpy as np
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 = "Model 1 : Knn audio classification"
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: 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
dataset = load_dataset(request.dataset_name, token=os.getenv("HF_TOKEN"))
# Split dataset
train_test = dataset["train"]
test_dataset = dataset["test"]
# 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.
# --------------------------------------------------------------------------------------------
# data formatting
def preprocess(dataset):
features = []
for row in dataset:
# Load the audio file and resample it
target_sr = 6000
audio = row['audio']['array']
audio = librosa.resample(audio, orig_sr=12000, target_sr=target_sr)
# Extract MFCC features
mfccs = librosa.feature.mfcc(y=audio, sr=target_sr, n_mfcc=7)
mfccs_scaled = np.mean(mfccs.T, axis=0)
# Append features and labels
features.append(mfccs_scaled)
return np.array(features)
X_test = preprocess(test_dataset)
classification_model = joblib.load("./models/audio_classification_knn.pkl")
predictions = classification_model.predict(X_test)
true_labels = test_dataset["label"]
# --------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
# --------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
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
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