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# -*- coding: utf-8 -*-
"""SONG.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1D_uRTl2aO65nt7ABxgODlb57aLqY05HC
"""
import gradio as gr
import numpy as np
import librosa
import os
import requests
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import joblib # to save/load model
# === CONFIGURATION ===
SAAVN_SEARCH_URL = "https://saavn.dev/api/search/songs?query={query}&limit=1"
DATA_DIR = "audio"
# update this if your folder is different
# Create data directory if it doesn't exist
if not os.path.exists(DATA_DIR):
os.makedirs(DATA_DIR)
print(f"Created directory: {DATA_DIR}. Please upload your audio dataset into this folder.")
# You might want to exit or handle this case where data is missing
# === FEATURE EXTRACTION ===
def extract_features(file_path):
try:
audio, sr = librosa.load(file_path, duration=3, offset=0.5)
mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
return np.mean(mfccs.T, axis=0)
except Exception as e:
print("Error loading file:", e)
return None
# === LOAD DATA AND TRAIN MODEL ===
def train_model(data_dir):
features = []
labels = []
for folder in os.listdir(data_dir):
emotion = folder.split('_')[-1].lower()
folder_path = os.path.join(data_dir, folder)
for filename in os.listdir(folder_path):
if filename.endswith('.wav'):
file_path = os.path.join(folder_path, filename)
mfcc = extract_features(file_path)
if mfcc is not None:
features.append(mfcc)
labels.append(emotion)
# Check if any data was loaded
if not features:
print(f"No audio files found in {data_dir}. Please upload your dataset.")
return None # Or raise an error
X = np.array(features)
y = np.array(labels)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Optional: Save model
joblib.dump(model, "voice_mood_model.pkl")
# Evaluate (Optional)
y_pred = model.predict(X_test)
print("✅ Model Trained - Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))
return model
# Load existing or train new model
model = None # Initialize model to None
if os.path.exists("voice_mood_model.pkl"):
try:
model = joblib.load("voice_mood_model.pkl")
print("Loaded existing model.")
except Exception as e:
print(f"Error loading model: {e}. Retraining model.")
model = train_model(DATA_DIR)
else:
model = train_model(DATA_DIR)
# Ensure model is trained before proceeding
if model is None:
print("Model could not be trained or loaded. Exiting.")
# You might want to add sys.exit() here if running as a script
else:
# === PREDICT EMOTION FROM AUDIO ===
def predict_emotion(file_path):
mfcc = extract_features(file_path)
if mfcc is not None:
# Ensure the model is available before predicting
if model:
return model.predict(mfcc.reshape(1, -1))[0]
else:
print("Model not loaded or trained.")
return "Error: Model not available"
return "Unknown"
# === GET SONG FROM SAAVN ===
def get_song_from_mood(mood, language="tamil"):
query = f"{mood} {language} song"
url = SAAVN_SEARCH_URL.format(query=query)
try:
response = requests.get(url)
response.raise_for_status() # Raise an exception for bad status codes
data = response.json()
if data.get("data") and data["data"]["results"]:
song = data["data"]["results"][0]
title = song["name"]
artist = (
song.get("artists", {}).get("primary", [{}])[0].get("name") or
song.get("primaryArtists") or song.get("artist") or "Unknown Artist"
)
return f"🎶 **{title}** by *{artist}*\n🔗 [Listen here]({song['url']})"
except requests.exceptions.RequestException as e:
print("Song fetch error:", e)
except Exception as e:
print("Song fetch error:", e)
return "😕 No song found for this mood."
# === GRADIO INTERFACE FUNCTION ===
def detect_and_recommend(audio_path):
if model: # Check if model is available
mood = predict_emotion(audio_path)
song = get_song_from_mood(mood)
return f"🎭 Detected Mood: **{mood}**\n\n{song}"
else:
return "Model not trained or loaded. Cannot detect mood."
# === LAUNCH GRADIO APP ===
gr.Interface(
fn=detect_and_recommend,
inputs=gr.Audio(type="filepath", label="🎙 Upload Your Voice"),
outputs="markdown",
title="🎤 Voice2Vibes (Offline Version)",
description="Detects mood from your voice using your dataset and recommends a matching Tamil song 🎶"
).launch() |