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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 tensorflow as tf | |
| 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 = "Random Baseline" | |
| ROUTE = "/audio" | |
| 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"].train_test_split(test_size=request.test_size, seed=request.test_seed) | |
| test_dataset = train_test["test"] | |
| def compute_spectrogram(audio_array, sample_rate=16000, frame_length=256, frame_step=128): | |
| spectrogram = tf.signal.stft(audio_array, frame_length=frame_length, frame_step=frame_step) | |
| spectrogram = tf.abs(spectrogram) | |
| return tf.expand_dims(spectrogram, axis=-1) | |
| def preprocess(item, max_length=16000): | |
| audio_array = item["audio"]["array"] | |
| audio_array = tf.convert_to_tensor(audio_array, dtype=tf.float32) | |
| if len(audio_array) < max_length: | |
| pad_size = max_length - len(audio_array) | |
| audio_array = tf.concat([audio_array, tf.zeros(pad_size)], axis=0) | |
| else: | |
| audio_array = audio_array[:max_length] | |
| spectrogram = compute_spectrogram(audio_array) | |
| return spectrogram | |
| # 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. | |
| #-------------------------------------------------------------------------------------------- | |
| MODEL_PATH = './model' | |
| model = tf.keras.models.load_model(MODEL_PATH) | |
| true_labels = test_dataset["label"] | |
| predictions = [] | |
| for item in test_dataset: | |
| spectrogram = preprocess(item) | |
| spectrogram = tf.expand_dims(spectrogram, axis=0) # Add batch dimension | |
| pred_probs = model.predict(spectrogram, verbose=0) | |
| predicted_label = np.argmax(pred_probs) | |
| predictions.append(predicted_label) | |
| # Make random predictions (placeholder for actual model inference) | |
| #true_labels = test_dataset["label"] | |
| #predictions = [random.randint(0, 1) for _ in range(len(true_labels))] | |
| #-------------------------------------------------------------------------------------------- | |
| # 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 |