File size: 7,839 Bytes
63e1917 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import os
import contextlib
import wave
import librosa
import numpy as np
import pandas as pd
import parselmouth
import soundfile as sf
import webrtcvad
from tensorflow.keras.models import load_model
import joblib
import warnings
import tempfile
# --- FastAPI Imports ---
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
# --- Configuration ---
TARGET_SR = 16000
MODEL_PATH = "vocal_model.h5"
SCALER_PATH = "vocal_scaler.joblib"
FEATURES_PATH = "feature_names.joblib"
# --- Suppress Warnings ---
warnings.filterwarnings('ignore')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# --- Load Models and Scaler at Startup ---
# This is efficient as they are loaded only once when the app starts
try:
model = load_model(MODEL_PATH)
scaler = joblib.load(SCALER_PATH)
feature_names = joblib.load(FEATURES_PATH)
print("✅ Model, scaler, and feature list loaded successfully.")
except Exception as e:
print(f"❌ FATAL ERROR: Could not load model files. The application will not work.")
print(f" Details: {e}")
# In a real-world scenario, you might want the app to fail to start here.
model, scaler, feature_names = None, None, None
# --- Feature Extraction Functions (Copied from your script) ---
# (I've omitted the functions for brevity, but you should copy ALL of them here)
# - preprocess_audio
# - extract_features
# ... (all your existing helper functions) ...
def preprocess_audio(input_path, target_sr=TARGET_SR):
try:
data, sr = librosa.load(input_path, sr=None, mono=False)
if data.ndim > 1: data = data.mean(axis=0)
if sr != target_sr: data = librosa.resample(data, orig_sr=sr, target_sr=target_sr)
base, ext = os.path.splitext(input_path)
output_path = f"{base}_processed_for_prediction.wav"
sf.write(output_path, data, target_sr, subtype='PCM_16')
return output_path
except Exception as e:
print(f"Error preprocessing {input_path}: {e}")
return None
def extract_features(file_path):
try:
y, sr = librosa.load(file_path, sr=None)
duration = librosa.get_duration(y=y, sr=sr)
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
mfcc_means = np.mean(mfccs, axis=1)
snd = parselmouth.Sound(file_path)
pitch = snd.to_pitch()
pitch_values = pitch.selected_array['frequency']
pitch_values = pitch_values[pitch_values != 0]
pitch_mean = np.mean(pitch_values) if len(pitch_values) > 0 else 0
pitch_std = np.std(pitch_values) if len(pitch_values) > 0 else 0
point_process = parselmouth.praat.call(snd, "To PointProcess (periodic, cc)", 75, 500)
jitter_local = parselmouth.praat.call(point_process, "Get jitter (local)", 0, 0, 0.0001, 0.02, 1.3)
shimmer_local = parselmouth.praat.call([snd, point_process], "Get shimmer (local)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
def read_wave(path):
with contextlib.closing(wave.open(path, 'rb')) as wf:
pcm_data, sample_rate = wf.readframes(wf.getnframes()), wf.getframerate()
return pcm_data, sample_rate
def frame_generator(frame_duration_ms, audio, sample_rate):
n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
offset = 0
while offset + n < len(audio):
yield audio[offset:offset + n]
offset += n
vad = webrtcvad.Vad(1)
audio, sample_rate = read_wave(file_path)
frames = list(frame_generator(30, audio, sample_rate))
voiced_seconds = 0
num_segments = 0
if frames:
for frame in frames:
if vad.is_speech(frame, sample_rate):
voiced_seconds += 0.03 # 30ms frame
num_segments +=1
silence_ratio = max(0, (duration - voiced_seconds) / duration) if duration > 0 else 0
speaking_rate = num_segments / duration if duration > 0 else 0
features = {
'Duration': duration,
'Pitch_Mean': pitch_mean,
'Pitch_Std': pitch_std,
'Jitter': jitter_local,
'Shimmer': shimmer_local,
'Speaking_Rate': speaking_rate,
'Silence_Ratio': silence_ratio,
}
for idx, val in enumerate(mfcc_means):
features[f'MFCC_{idx+1}'] = val
return features
except Exception as e:
print(f"Error extracting features from {file_path}: {e}")
return None
# --- Main Prediction Logic (Refactored to return a dictionary) ---
def predict_from_audio_path(file_path):
"""
Takes a file path, runs the full prediction pipeline, and returns a result dictionary.
"""
if not all([model, scaler, feature_names]):
raise HTTPException(status_code=503, detail="Model is not loaded or available.")
# 1. Preprocess audio
processed_path = preprocess_audio(file_path)
if not processed_path:
raise HTTPException(status_code=400, detail="Audio preprocessing failed.")
# 2. Extract features
features_dict = extract_features(processed_path)
if not features_dict:
os.remove(processed_path)
raise HTTPException(status_code=400, detail="Feature extraction failed.")
try:
# 3. Convert to DataFrame and ensure correct column order
feature_df = pd.DataFrame([features_dict])
feature_df = feature_df[feature_names] # Crucial step!
# 4. Scale features
scaled_features = scaler.transform(feature_df)
# 5. Make a prediction
prediction_prob = model.predict(scaled_features, verbose=0)[0][0]
prediction_label = int((prediction_prob > 0.5).astype("int32"))
# 6. Format the result
result_text = "Parkinson's Detected" if prediction_label == 1 else "Healthy"
# 7. Cleanup the temporary processed file
os.remove(processed_path)
return {
"status": "success",
"prediction": result_text,
"confidence": float(prediction_prob),
"label": prediction_label
}
except Exception as e:
# Ensure cleanup even if an error occurs after file creation
os.remove(processed_path)
raise HTTPException(status_code=500, detail=f"An error occurred during prediction: {str(e)}")
# --- FastAPI App Definition ---
app = FastAPI(
title="Parkinson's Voice Detection API",
description="An API that uses a deep learning model to predict the presence of Parkinson's disease from a voice recording.",
version="1.0"
)
@app.get("/", tags=["General"])
def read_root():
"""A welcome message to check if the API is running."""
return {"message": "Welcome to the Parkinson's Voice Prediction API. Go to /docs for usage."}
@app.post("/predict/", tags=["Prediction"])
async def create_prediction(file: UploadFile = File(...)):
"""
Accepts an audio file, processes it, and returns the prediction result.
The audio file can be in any format that librosa supports (wav, mp3, etc.).
"""
# Save the uploaded file to a temporary location on the server
try:
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as tmp_file:
content = await file.read()
tmp_file.write(content)
tmp_file_path = tmp_file.name
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error handling the uploaded file: {e}")
# Now, run the prediction on the saved temporary file
try:
result = predict_from_audio_path(tmp_file_path)
return JSONResponse(content=result)
finally:
# CRITICAL: Always clean up the temporary file
os.remove(tmp_file_path) |