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voicerecognizion/api/main.py

+import librosa
+import tensorflow as tf
+import numpy as np
+import os
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from fastapi.responses import FileResponse
+from typing import Optional
+from pydub import AudioSegment
+import io
+
+app = FastAPI()
+
+SAVED_MODEL_PATH = "../model.h5"
+SAMPLES_TO_CONSIDER = 22050
+
+class KeywordSpottingService:
+    """Singleton class for keyword spotting inference with trained models."""
+
+    model = None
+    _mapping = [
+        "dataset\\backward",
+        "dataset\\bed",
+        "dataset\\bird",
+        "dataset\\cat",
+        "dataset\\dog",
+        "dataset\\down",
+        "dataset\\eight",
+        "dataset\\five",
+        "dataset\\follow",
+        "dataset\\forward",
+        "dataset\\four",
+        "dataset\\go",
+        "dataset\\happy",
+        "dataset\\house",
+        "dataset\\learn",
+        "dataset\\left",
+        "dataset\\nine",
+        "dataset\\no",
+        "dataset\\off",
+        "dataset\\on",
+        "dataset\\one",
+        "dataset\\right",
+        "dataset\\seven",
+        "dataset\\six",
+        "dataset\\stop",
+        "dataset\\three",
+        "dataset\\tree",
+        "dataset\\two",
+        "dataset\\up",
+        "dataset\\visual",
+        "dataset\\wow",
+        "dataset\\yes",
+        "dataset\\zero"
+    ]
+    _instance = None
+
+    def __init__(self):
+        self.model = tf.keras.models.load_model(SAVED_MODEL_PATH)
+
+    def predict(self, audio_data: np.ndarray, sample_rate: int) -> str:
+        """Predict keyword from audio data."""
+        MFCCs = self.preprocess(audio_data, sample_rate)
+        MFCCs = MFCCs[np.newaxis, ..., np.newaxis]
+        predictions = self.model.predict(MFCCs)
+        predicted_index = np.argmax(predictions)
+        predicted_keyword = self._mapping[predicted_index]
+        # Extracting only the word from the directory path
+        predicted_word = predicted_keyword.split("\\")[-1]
+        return predicted_word
+
+    def preprocess(self, audio_data: np.ndarray, sample_rate: int, num_mfcc: int = 13, n_fft: int = 2048, hop_length: int = 512) -> np.ndarray:
+        """Extract MFCCs from audio data."""
+        if len(audio_data) >= SAMPLES_TO_CONSIDER:
+            audio_data = audio_data[:SAMPLES_TO_CONSIDER]
+        else:
+            # Pad the signal with zeros or truncate it to the required length
+            shortage = SAMPLES_TO_CONSIDER - len(audio_data)
+            audio_data = np.pad(audio_data, (0, shortage), mode='constant')
+        MFCCs = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)
+        return MFCCs.T
+
+kss = KeywordSpottingService()
+
+@app.post("/predict/upload/")
+async def predict_keyword_upload(file: UploadFile = File(...)):
+    """Endpoint to predict keyword from uploaded audio file."""
+    file_path = "temp_audio.wav"
+    try:
+        with open(file_path, "wb") as f:
+            f.write(await file.read())
+        audio_data, sample_rate = librosa.load(file_path)
+        predicted_keyword = kss.predict(audio_data, sample_rate)
+        return {"predicted_keyword": predicted_keyword}
+    finally:
+        os.remove(file_path)
+
+@app.post("/predict/microphone/")
+async def predict_keyword_microphone(audio_file: UploadFile = File(...)):
+    """Endpoint to predict keyword from audio file sent from microphone."""
+    try:
+        audio_data = await audio_file.read()
+        audio = AudioSegment.from_file(io.BytesIO(audio_data))
+        audio_data = np.array(audio.get_array_of_samples())
+        sample_rate = audio.frame_rate
+        predicted_keyword = kss.predict(audio_data, sample_rate)
+        return {"predicted_keyword": predicted_keyword}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail="Invalid audio file format")
+
+
+@app.get("/")
+async def get_index():
+    return FileResponse("index.html")