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import sys
import json
import time
import random
import librosa # The new library for real audio analysis
import numpy as np # The library for numerical operations
# --- This is our new "intelligence" factor ---
# We'll consider any audio with average energy below this threshold as silence.
# You can experiment with this value; lower values make it more sensitive to quiet sounds.
SILENCE_THRESHOLD = 0.001
def get_mock_species_data(species_name):
"""
Returns a rich, detailed data structure for a given species name.
This simulates a database lookup for species information.
"""
all_species = {
"European Robin": { "scientificName": "Erithacus rubecula", "icon": "🐦", "conservationStatus": "Least Concern", "description": "A small insectivorous passerine bird. Known for its bright orange-red breast.", "habitat": "Woodlands, parks, gardens", "frequency": "2-6 kHz", "callType": "Melodic song", "sound": "Clear, warbling notes" },
"Great Tit": { "scientificName": "Parus major", "icon": "🐦", "conservationStatus": "Least Concern", "description": "A distinctive bird with a black head and neck, prominent white cheeks, and a black stripe down its yellow front.", "habitat": "Deciduous woodland, gardens", "frequency": "3-7 kHz", "callType": "Repetitive two-note song", "sound": "'Teacher-teacher' sound" },
"Common Nightingale": { "scientificName": "Luscinia megarhynchos", "icon": "🎶", "conservationStatus": "Least Concern", "description": "A small passerine bird best known for its powerful and beautiful song.", "habitat": "Dense scrub and woodland", "frequency": "1-8 kHz", "callType": "Complex, rich song", "sound": "Crescendo of notes" },
"Red-winged Blackbird": { "scientificName": "Agelaius phoeniceus", "icon": "⚫", "conservationStatus": "Least Concern", "description": "A passerine bird of the family Icteridae. Males are black with a red and yellow shoulder patch.", "habitat": "Marshes, wetlands", "frequency": "2-4 kHz", "callType": "Gurgling song", "sound": "'Conk-la-ree' sound" }
}
return all_species.get(species_name, { "scientificName": "Unknown", "icon": "❓", "conservationStatus": "Unknown", "description": "Could not identify species.", "habitat": "Unknown", "frequency": "N/A", "callType": "N/A", "sound": "N/A" })
def analyze_audio_file(file_path):
"""
A smarter simulation of AI audio analysis.
It now performs REAL energy analysis to detect silence.
"""
# --- REAL ANALYSIS STEP ---
# Load the audio file using librosa. This gives us the raw sound wave (y)
# and the sample rate (sr).
y, sr = librosa.load(file_path, sr=None, mono=True, res_type='kaiser_fast')
# Calculate the Root Mean Square (RMS) energy, a measure of average volume.
rms_energy = np.sqrt(np.mean(y**2))
# --- INTELLIGENT DECISION STEP ---
# If the energy is below our silence threshold, return a specific "silent" result.
if rms_energy < SILENCE_THRESHOLD:
return {
"confidence": 95,
"analysisQuality": "High",
"detectedSpecies": [], # Return an empty list of species
"biodiversityMetrics": {
"biodiversityScore": 0,
"shannonIndex": 0,
"ecosystemHealth": "Unknown (Silence)"
},
"acousticFeatures": { "duration": librosa.get_duration(y=y, sr=sr), "sampleRate": sr },
"recommendations": [
"No significant audio was detected in this recording.",
"Try recording in a location with more natural sounds.",
"Ensure your microphone is working and not covered."
]
}
# --- SIMULATION STEP (if sound is detected) ---
# If the audio is NOT silent, we proceed with our previous simulation.
time.sleep(random.uniform(1.5, 2.5)) # Simulate processing time
possible_species = ["European Robin", "Great Tit", "Common Nightingale", "Red-winged Blackbird"]
num_detected = random.randint(1, 3)
detected_species_names = random.sample(possible_species, num_detected)
detected_species_results = []
for species_name in detected_species_names:
species_data = get_mock_species_data(species_name)
species_data["name"] = species_name
species_data["confidence"] = random.randint(75, 98)
detected_species_results.append(species_data)
biodiversity_score = 60 + len(detected_species_results) * 15 + random.randint(-5, 5)
ecosystem_health = "Excellent" if biodiversity_score > 85 else "Good" if biodiversity_score > 70 else "Fair"
shannon_index = round(1.2 + len(detected_species_results) * 0.2 + random.uniform(-0.1, 0.1), 2)
return {
"confidence": random.randint(85, 99),
"analysisQuality": "High",
"detectedSpecies": detected_species_results,
"biodiversityMetrics": {
"biodiversityScore": biodiversity_score,
"shannonIndex": shannon_index,
"ecosystemHealth": ecosystem_health
},
"acousticFeatures": { "duration": librosa.get_duration(y=y, sr=sr), "sampleRate": sr },
"recommendations": [
"This area shows healthy species diversity.",
"Consider conservation efforts for nearby wetlands.",
"Continue monitoring during migratory seasons."
]
}
if __name__ == "__main__":
try:
audio_file_path = sys.argv[1]
analysis_data = analyze_audio_file(audio_file_path)
print(json.dumps(analysis_data, indent=4))
except Exception as e:
print(f"Error in Python script: {e}", file=sys.stderr)
sys.exit(1) |