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Training TinyML Speaker Recognition Model for Deployment on Raspberry Pi

Introduction

Speaker recognition on low-power devices is a challenge, but with TinyML, we can build a lightweight model that runs efficiently on a Raspberry Pi 4. Since training requires more computational power, we will use a Cloud GPU to train the model and deploy the optimized version on the Raspberry Pi for real-time inference.

1. Audio Preprocessing (Feature Extraction)

To recognize a speaker in real-time, we extract key features from audio. The best method for TinyML is Mel-Frequency Cepstral Coefficients (MFCCs).

Extracting MFCCs in Python

import librosa
import librosa.display
import numpy as np
import matplotlib.pyplot as plt

# Load an audio file (recorded from your microphone)
audio_file = "speaker1.wav"
y, sr = librosa.load(audio_file, sr=16000)

# Extract MFCC features
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)

# Display MFCCs
plt.figure(figsize=(10, 4))
librosa.display.specshow(mfccs, x_axis='time')
plt.colorbar()
plt.title('MFCCs')
plt.show()

👉 These MFCCs will be used as input to our model.

2. Training a TinyML Speaker Model on Cloud GPU

Since training deep learning models on a Raspberry Pi is inefficient, we will use a Cloud GPU (e.g., Google Colab, AWS EC2, or Azure ML) for model training.

Dataset Requirements

  • At least 5 minutes of speech per speaker
  • Multiple speakers for training
  • Use VoxCeleb dataset or record your own

Training a Lightweight Model in TensorFlow on Cloud GPU

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.utils import to_categorical

# Normalize data
X_train = X_train / np.max(X_train)
X_test = X_test / np.max(X_test)

# Convert labels to categorical
y_train = to_categorical(y_train, num_classes=num_speakers)
y_test = to_categorical(y_test, num_classes=num_speakers)

# Build TinyML Model
model = Sequential([
    Conv2D(8, kernel_size=(3,3), activation='relu', input_shape=(13, 100, 1)),
    MaxPooling2D(pool_size=(2,2)),
    Flatten(),
    Dense(16, activation='relu'),
    Dense(num_speakers, activation='softmax')  # Output layer
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train model on Cloud GPU
model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))

3. Optimizing the Model for TinyML

Once training is complete on the cloud, we need to convert the model to TensorFlow Lite for deployment on Raspberry Pi 4.

Convert to TensorFlow Lite

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save model
with open("speaker_model.tflite", "wb") as f:
    f.write(tflite_model)

This quantizes the model, making it smaller and faster for inference on the Raspberry Pi.

4. Deploying the Model on Raspberry Pi 4

Transfer Model to Raspberry Pi

Use SCP or a cloud storage service to transfer the trained .tflite model to Raspberry Pi.

scp speaker_model.tflite pi@raspberrypi:/home/pi/

Install TensorFlow Lite Interpreter on Raspberry Pi

pip install tflite-runtime

Run Real-Time Speaker Recognition

import tflite_runtime.interpreter as tflite
import numpy as np

# Load model
interpreter = tflite.Interpreter(model_path="speaker_model.tflite")
interpreter.allocate_tensors()

# Get input/output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Function to predict speaker
def predict_speaker(mfcc_features):
    mfcc_features = np.expand_dims(mfcc_features, axis=0).astype(np.float32)
    
    # Set input tensor
    interpreter.set_tensor(input_details[0]['index'], mfcc_features)
    
    # Run inference
    interpreter.invoke()
    
    # Get output
    output_data = interpreter.get_tensor(output_details[0]['index'])
    predicted_speaker = np.argmax(output_data)
    
    return predicted_speaker

5. Is Raspberry Pi 4 Enough for Real-Time Speaker Recognition?

✅ Yes, if:

  • Using TensorFlow Lite with optimized MFCC-based models.
  • Inference time is 10-20ms per prediction.
  • Handling up to 5-10 speakers.

🚫 No, if:

  • Using deep models like wav2vec (requires GPU or Edge TPU).
  • Need continuous real-time classification on large datasets.

6. Next Steps

  • Test latency on Raspberry Pi with real microphone input.
  • Optimize model further using TensorFlow Model Optimization Toolkit.
  • Use Edge TPU (Coral) for better performance if needed.

By leveraging Cloud GPU for training and deploying optimized models on Raspberry Pi, we can achieve real-time speaker recognition with high efficiency! 🎤🚀