Spaces:
Sleeping
Sleeping
File size: 3,405 Bytes
b24406e 4d6e8c2 fe4a4cb 3b09640 fe4a4cb 4d6e8c2 b24406e 4d6e8c2 3b09640 4d6e8c2 b24406e 1c33274 70f5f26 fe4a4cb 3b09640 1c33274 70f5f26 4d6e8c2 fe4a4cb 70f5f26 b24406e 4d6e8c2 fe4a4cb 4d6e8c2 fe4a4cb 3b09640 fe4a4cb b24406e fe4a4cb b24406e fe4a4cb b24406e fe4a4cb 4d6e8c2 fe4a4cb 70f5f26 fe4a4cb 4d6e8c2 70f5f26 4d6e8c2 fe4a4cb b24406e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import librosa
import joblib
import numpy as np
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
import os
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 = "Decision tree"
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: Basic decision tree
"""
# 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"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
#--------------------------------------------------------------------------------------------
# MY MODEL
#--------------------------------------------------------------------------------------------
def extract_features(example, sampling_rate):
audio_array = example['audio']['array']
# mfcc = librosa.feature.mfcc(y=audio_array, sr=sampling_rate, n_mfcc=5)
mfcc = librosa.feature.spectral_contrast(y=audio_array)
return np.mean(mfcc, axis=1)
def predict_new_audio(model, dataset, sampling_rate):
features_list = [extract_features(example, sampling_rate) for example in dataset]
features_array = np.vstack(features_list)
predictions = model.predict(features_array)
return predictions
model_filename = "model_audio.pkl"
clf = joblib.load(model_filename)
predictions = predict_new_audio(clf, test_dataset, 12000)
#--------------------------------------------------------------------------------------------
# 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 |