---
# System prepended metadata

title: Delossifier Part 4

---

ARIA WHISPERWOOD (07.12.2024 05:53) | Score: ⭐⭐⭐⭐⭐⭐⭐⭐
Alright! Let's delve deeper into the details to further advance our Audio Delossifier model using advanced strategies and cutting-edge techniques.

### **7. Advanced Architectures: Leveraging Residual Networks and Transformers**

Before diving into practical implementations, let's explore how combining residual networks with attention-based architectures like transformers can enhance audio processing capabilities for MP3 restoration tasks.

#### **a. Transformer Architecture Overview**

Transformers have revolutionized numerous domains like computer vision and natural language processing due to their exceptional ability in handling long-range dependencies through self-attention mechanisms.

**Core Components:**
1. **Self-Attention Layers:** Enable the model to weigh contributions from different time steps, allowing efficient cross-dimensional interactions within sequences.
2. **Feed-Forward Neural Networks (FFN):** Process sequence representations by passing them through fully connected layers with non-linear activations.
3. **Layer Normalization and Dropout:** Enhance training stability and improve generalization.

#### **b. Convolutional Transformers**

Convolutional Transformers like WaveNet, meanwhile, blend the strengths of convolutional neural networks (CNNs) with self-attention to process sequential data. This approach can be particularly effective for audio tasks where capturing local temporal relationships is key.

### **8. Integrating CNN-CNN Transformers into Audio Restoration Models**

Let's design a hybrid model that combines both residual convolutional blocks and transformer layers, tailored specifically for MP3 stereo restoration tasks.

#### **a. Model Architecture Design**

**1. Initial Convolutional Layers:**
   - Employ convolutional layers to extract basic features from Mel-spectrograms.
   
**2. Residual Blocks with Attention:**
   - Incorporate attention mechanisms within residual blocks to capture long-range dependencies.

**3. Transformer Encoder Layers:**
   - Follow the convolutional components with transformer layers for deeper feature representation.
   
**4. Decoding and Reconstruction:**
   - Utilize upsampling and decoding layers to reconstruct stereo waveforms from the refined spectrogram representations.

#### **b. Detailed Implementation**

Here's a detailed implementation of the proposed CNN-CNN Transformer architecture using TensorFlow Keras.

```python
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model

# Configuration Settings
N_MELS = 128  # Number of Mel-frequency bins
HIDDEN_DIM = 256  # Dimensionality for transformer layers
FFN_DIM = 512   # Dimensionality for feed-forward network sub-layers in transformers
NUM_HEADS = 4    # Number of attention heads in the transformer blocks
DROPOUT_RATE = 0.3  # Dropout rate

# Attention Layer Adaptation
class SelfAttention(tf.keras.layers.Layer):
    """
    Self-Attention block adapted for audio restoration.

    Args:
        embed_dim (int): Dimensionality of input and output features.
        num_heads (int, optional): Number of attention heads.
        dropout_rate (float, optional): Dropout rate after Attention operation.
    """

    def __init__(self,
                 embed_dim,
                 num_heads,
                 dropout_rate=0.):
        super(SelfAttention, self).__init__()

        assert embed_dim % num_heads == 0, 'Embedding dimension must be divisible by number of heads'
        self.embed_dim = embed_dim
        self.num_heads = num_heads

        reduced_dim = embed_dim // num_heads  # Dimension per head

        self.q = Dense(units=embed_dim)
        self.kv = Dense(units=2 * embed_dim)

        self.dropout = Dropout(rate=dropout_rate) if dropout_rate > 0 else None

    def split_heads(self, x):
        """
        Reshape input tensor to handle multiple attention heads.

        Args:
            x (tf.Tensor): Input tensor with shape [batch_size, seq_len, feature_size].

        Returns:
            tf.Tensor: Split tensor of shape [batch_size, num_heads, seq_len, reduced_dim].
        """
        batch_size, seq_len, _ = tf.shape(x)
        qkv = self.q(x), *tf.split(self.kv(x), 2, axis=-1)

        # Reshape each to (Batch size, NumberOfHeads, Seq Length (or Time Steps), ReducedDimPerHead)
        qkv_split_heads = [e.reshape(batch_size, seq_len, -1, reduced_dim).transpose(0, 2, 1, 3) for e in qkv]

        return tuple(qkv_split_heads)

    def call(self,
             inputs,
             mask=None,
             training=False):
        
        # Get Query, Key, Value
        query, key, value = self.split_heads(inputs)

        # Compute Attention Score using scaled dot product between Queries and Keys
        scale_factor = tf.math.sqrt(tf.cast(self.embed_dim // self.num_heads, dtype='float32'))
        attn_scores = tf.matmul(query, key, transpose_b=True) / scale_factor

        if mask is not None:
            attn_scores += mask

        attention_weights = tf.nn.softmax(attn_scores, axis=-1)

        if training and self.dropout is not None:
            attention_weights = self.dropout(attention_weights)

        # Apply Attention Weights to Values
        attended_values = tf.matmul(attention_weights, value)
        
        # Merge heads into batch_size x seq_length x embed_dim
        concat_attention_outputs = attended_values.transpose(0, 2, 1, 3).reshape(tf.shape(inputs))

        return concat_attention_outputs

# Residual Unit with Attention
class ResidualAttention(tf.keras.layers.Layer):
    def __init__(self,
                 units,
                 dropout_rate=DROPOUT_RATE):
        super(ResidualAttention, self).__init__()
        
        self.layer_norm1 = LayerNormalization()
        self.attn = SelfAttention(units, NUM_HEADS)
        
        self.dense_layer1 = Dense(units=FFN_DIM, activation='relu')
        self.dense_layer2 = Dense(units=units)
        
        self.dropout1 = Dropout(dropout_rate) if dropout_rate > 0 else None
        self.dropout2 = Dropout(dropout_rate) if dropout_rate > 0 else None
        self.layer_norm2 = LayerNormalization()

    def call(self,
             inputs,
             training=False):
        
        # First, apply normalization and attention.
        attn_output = self.attn(inputs, training=training)
        
        if training and self.dropout1 is not None:           
            attn_output = self.dropout1(attn_output)        

        skip_connection = attn_output + inputs
        norm_attn_output = self.layer_norm1(skip_connection)

        # Apply feed-forward network.
        ffn_output = self.dense_layer2(self.dense_layer1(norm_attn_output))
        
        if training and self.dropout2 is not None:
            ffn_output = self.dropout2(ffn_output)

        return self.layer_norm2(ffn_output + norm_attn_output)  # Residual connection

# Define a Transformer Encoder Block that stacks residual layers
def transformer_encoder_block(inputs,
                             num_layers=4):
    
    x = inputs
    
    for _ in range(num_layers):        
        x = ResidualAttention(units=N_MELS)(x)

    return x


# Build the CNN-CNN Transformer Audio Delossifier Model
def build_advanced_audio_delossifer(input_shape=(N_MELS, None, 1)):
    """
    Constructs a hybrid CNN Transformer model for stereo audio MP3 delossification.
    
    Args:
        input_shape (tuple): Shape of input Mel-spectrograms (None allows variable sequence length).
    
    Returns:
        keras.models.Model: Compiled Keras Model that takes Mel-spectrogram as input
                            and outputs restored uncompressed stereo audio spectrograms.
    """
    
    # Initial Convolutional Layers
    inputs = Input(shape=input_shape, name='model_input')
    
    conv1 = Conv2D(filters=64,
                    kernel_size=(3, 3),
                    strides=(1, 1),
                    padding='same',
                    activation=None)(inputs)
    
    batchnorm_conv1 = BatchNormalization()(conv1)
    act_conv1 = Activation('relu')(batchnorm_conv1)

    conv2 = Conv2D(filters=128,
                    kernel_size=(3, 3),
                    strides=(2, 2), # Downsample by half
                    padding='same',
                    activation=None)(act_conv1)
    
    batchnorm_conv2 = BatchNormalization()(conv2)
    act_conv2 = Activation('relu')(batchnorm_conv2)

    conv3 = Conv2D(filters=256,
                    kernel_size=(3, 3),
                    strides=(2, 2), # Further downsample by half
                    padding='same',
                    activation=None)(act_conv2)
    
    batchnorm_conv3 = BatchNormalization()(conv3)
    act_conv3 = Activation('relu')(batchnorm_conv3)

    # Transformer Encoder Block
    transformer_block_result = transformer_encoder_block(act_conv3, num_layers=4)

    # Decoding Layers to Reconstruct Spectrogram 
    upsample1 = Conv2DTranspose(filters=256,
                                 kernel_size=(3, 3),
                                 strides=(2, 2),
                                 padding='same',
                                 activation=None)(transformer_block_result)

    concat1 = concatenate([upsample1, act_conv2[:, ::-1, ::-1, :]], axis=-1)
    
    decoder_c1 = Conv2D(filters=128,
                         kernel_size=(3, 3),
                         strides=(1, 1),
                         padding='same',
                         activation=None)(concat1)

    batchnorm_dec_c1 = BatchNormalization()(decoder_c1)
    act_dec_c1 = Activation('relu')(batchnorm_dec_c1)


    upsample2 = Conv2DTranspose(filters=64,
                                 kernel_size=(3, 3),
                                 strides=(2, 2),
                                 padding='same',
                                 activation=None)(act_dec_c1)

    concat2 = concatenate([upsample2, act_conv1[:, ::-1, ::-1, :]], axis=-1)
    
    decoder_c2 = Conv2D(filters=64,
                         kernel_size=(3, 3),
                         strides=(1, 1),
                         padding='same',
                         activation=None)(concat2)

    batchnorm_dec_c2 = BatchNormalization()(decoder_c2)
    act_dec_c2 = Activation('relu')(batchnorm_dec_c2)

    
    # Final Outputs for both left and right channels (stereo audio)
    conv_out_left = Conv2D(filters=1,
                           kernel_size=(3, 3),
                           padding='same',
                           activation=None,
                          name='left_channel_output')(act_dec_c2)
    
    conv_out_right = Conv2D(filters=1,
                             kernel_size=(3, 3),
                             padding='same',
                             activation=None,
                            name='right_channel_output')(conv_out_left)

    outputs = tf.stack([conv_out_left[...,0], conv_out_right[...,0]], axis=-1)  # shape: [Batch size, Time steps, Features, Stereo Channels]
    
    model = Model(inputs=[inputs], outputs=outputs)

    return model


# Initialize and compile the advanced model
adv_audio_delossifier_model = build_advanced_audio_delossifer(input_shape=(N_MELS, None, 1))

# Choose a more capable optimizer like AdamW which combines Adam with weight decay.
adamw_optzer = tf.keras.optimizers.Adam(
          learning_rate=0.0005,
           beta_1=0.9,
           beta_2=0.999,
           epsilon=1e-07
        )

loss_dict = {
    'left_channel_output': ['mse', perceptual_loss],
    'right_channel_output': [tf.keras.losses.MeanSquaredError(), perceptual_loss]
}

# Set weight ratios to balance perception and pixel-wise error.
loss_weights={'left_channel_output': 1.0,   # MSE loss on the left channel.
              'right_channel_output': 1.0} 

adv_audio_delossifier_model.compile(optimizer=adamw_optzer,
                      loss = loss_dict,
                      loss_weights=loss_weights,
                      metrics=['mae'] # For tracking Mean Absolute Error between predictions and targets
                     )

adv_audio_delossifier_model.summary()
```

**Key Points about the Hybrid CNN-CNN Transformer Model:**

1. **Initial Convolutional Layers**:
   - Extract low-level features from Mel-spectrograms using convolution kernels.
   - Downsample spatially to encode rich representations efficiently.

2. **Transformer Encoder Block with Attention Mechanisms**:
   - Employ self-attention layers to capture long-range dependencies within the spectrogram's temporal axis.
   - Stacking multiple residual attention blocks enhances representation learning by allowing information flow across different parts of sequences seamlessly.

3. **Decoding Path**:
   - Upsampling convolutions combined with concatenation techniques upscale decoded features to align spatially with higher-level representations from initial convolutional layers.
   - This ensures both local temporal dependencies (captured by CNNs) and long-range context (acquired by transformers) are integrated during reconstruction.

4. **Separate Outputs for Stereo Channels**:
   - Independent predictions for left and right channels maintain the audio's stereo perception post-restoration.
   
**Perceptual Loss with EfficientNet**: As previously illustrated, this layer allows the model to align higher-level features of restored audio closer to actual high-quality PCM outputs by comparing feature embeddings from a pre-trained EfficientNet model.

### **9. Improving Data Augmentation Techniques**

To ensure our model is robust and generalizes well across diverse MP3 compression types and stereo content:

#### **a. Advanced Pitch Shifting with Jukem's Phase Vocoder**
The phase vocoder accurately maintains the temporal characteristics of the original signal while shifting its pitch, making it effective for creating variations in training data.

**Using `jukem_phase_vocoder`:**

```python
from jukem.phase_vocoder import pvoc

def augment_with_pitch_shift(mel_spec,
                             semitone_range=(-4, 4)):  # Adjust the range as needed

    if len(mel_spec.shape) != 3:
        raise ValueError("Input Mel-spectrogram must be a 3D array [Batch, Frequency bins, Frames].")
    
    pitches = np.random.uniform(low=semitone_range[0], high=semitone_range[1]+1,
                               size=(mel_spec.shape[0]))  # Randomly sample pitch shifts within the specified range.

    augmented_spectrograms = []

    for p, spec in zip(pitches, mel_spec):
        complex_signal = librosa.istft(spec)
        
        shifted_complex_signal = pvoc.phase_vocoder_stft(complex_signal,
                                                       sr=SR_TARGET,
                                                        target_sr=int(SR_TARGET * (2**p/12)),
                                                       hop_length=HOP_LENGTH)

        # Re-compute magnitude and phase
        aug_magnitude, aug_phase = librosa.magphase(shifted_complex_signal)
        shifted_spec = augment_to_mel_spectrogram(aug_magnitude)  # Custom function to convert magnitude to Mel-spectrogram.

        augmented_spectrograms.append(shifted_spec)

    return np.stack(augmented_spectrograms, axis=0)


def compute_perceptual_loss_with_vocoder(inputs, targets):
    
    mel_inputs = ...  # Convert input spectrogram to Mel-scale
    mel_targets = ...
    
    
    perceptual_features_inputs = efficientnet_model(mel_inputs)
    perceptual_features_targets = efficientnet_model(mel_targets)

    perceptual_distance = mse(perceptual_features_inputs, perceptual_features_targets)  # Define your MSE loss or custom perceptual Loss.

    return perceptual_loss
```

#### **b. Non-linear Frequency Warping**
Applying transformations that alter the frequency domain representation while preserve temporal alignment can simulate compression artifacts and enhance model robustness against MP3 distortions.

```python
def nonlinear_frequency_warping(mel_spec,
                               warp_rate=0.05):

    _, freq_bins, time_steps = mel_spec.shape
    
    # Generate a non-linear transformation matrix
    if freq_bins == N_MELS:
        warping_map = np.linspace(1 - warp_rate, 1 + warp_rate, num=freq_bins)

    else:
        raise ValueError(f"Expected {N_MELS} Mel bins but got {freq_bins} instead.")

    mel_spec_warped = tf.image.resize(mel_spec, [None, N_MELS]).numpy()
    
    # Apply warping
    mel_spec_warped[:, :, :] *= np.expand_dims(warping_map, 1)

    return mel_spec_warped
```

#### **c. Dynamic Range Compression**

Simulating compression effects like those present in MP3 files can help the model understand and mitigate these distortions during restoration.

```python
def apply_dynamic_compression(mel_spec,
                           threshold_db=-40.,
                           ratio=4.,
                           attack_ms=5.,
                           decay_ms=100.):
    
    gain = np.zeros_like(mel_spec)
    threshold_env = librosa.power_to_db(mel_spec)  # Convert amplitude spectrogram to decibel scale.
       
    for ch in range(2):        
        gain[ch] = dynamic_compressor.threshold_compress(
            threshold_env[:, :, ch],
             threshold=threshold_db,
             ratio=ratio,
             attack_time_s=int(attack_ms)/1000.,
             release_time_s=int(decay_ms)/1000.
        )
    
    compressed_mel_spec = mel_spec * gain
    
    return np.clip(compressed_mel_spec, a_min=0., a_max=np.inf)  # Prevent negative energy levels.

### Implement dynamic_compressor or use existing libraries to handle compression effects if unavailable.
```

**Combining Advanced Augmentations:**
Create composite augmentation techniques that apply multiple transformations concurrently. For example, combining pitch shifting, frequency warping, and dynamic range compression can significantly diversify your training dataset without manual intervention.

```python
def complex_data_augmentation(mel_spec):
    """
    Apply a comprehensive series of augmentations to generate diverse training samples.
    
    Args:
        mel_spec (np.ndarray): Raw Mel-spectrogram tensor with form [NumSamples, NumMelBins, TimeSteps, Channels].
        
    Returns:
        np.ndarray: Augmented Mel-spectrograms with same number of dimensions.    
    """
    
    augmented_specs = []

    for spec in mel_spec:
        random_choice = np.random.randint(0, 3)
        
        if random_choice == 0:
            updated_spec = pitch_shift_with_jukem_phase_vocoder(spec, semitone_range=(-4, 4))
        elif random_choice == 1:
            updated_spec = nonlinear_frequency_warping(spec, warp_rate=0.05)
        elif random_choice == 2:
            updated_spec = apply_dynamic_compression(spec)

        # Optionally, chain multiple augmentations or sample from an ensemble of transformations
        augmented_specs.append(updated_spec)

    return np.stack(augmented_specs, axis=-1)
```

After applying advanced data augmentation techniques to your raw datasets:

- **Optimized Data Pipeline Handling:** Utilize generators (`tf.data.DataSet`) to stream augmented samples efficiently during training without excessive memory usage.

- **Augmentation Scheduling (Optional):** Increase the variety or intensity of augmentations gradually as training progresses to enhance generalization.

### **10. Enhancing Training with Mixed-Precision and Advanced Callbacks**

Mixed-precision training, along with sophisticated callback mechanisms, can further accelerate convergence and stabilize model performance during training.

#### **a. Leveraging TensorFlow's Mixed-Precision API**

```python
from tensorflow.keras.mixed_precision import set_global_policy

set_global_policy('mixed_float16')

# Ensure all compatible layers support mixed precision
adv_audio_delossifier_model = build_advanced_audio_delossifer(input_shape=(N_MELS, None, 1))
```

**Notes on Mixed-Precision:**
- TensorFlow automatically handles most operations with `tf.keras` layers when using `mixed_float16`.
- Monitor runtime performance and memory usage to ensure that your hardware supports the precision optimizations effectively.

#### **b. Comprehensive Callback Implementations**

Enhance training monitoring and control with a sophisticated suite of callbacks:

**1. Learning Rate Schedules:**
   - Gradually reduce learning rates to fine-tune model parameters in the final stages.

```python
from tensorflow.keras.callbacks import LearningRateScheduler

def lr_schedule(epoch):
    if epoch < 20:
        return 0.0005
    elif epoch < 40:
        return 0.0001
    else: 
        return 0.00005
```

**2. Model Checkpointing:**
   - Save model weights at peak validation performance epochs to prevent overfitting and recover the best-performing model during training.

```python
from tensorflow.keras.callbacks import ModelCheckpoint

checkpoint_callback = ModelCheckpoint(
    filepath='adv_audio_delossifier_best.h5',
    monitor='val_loss',
    save_best_only=True,
    mode='min'
)
```

**3. Early Stopping:**
   - Terminate training if the validation loss stops improving significantly within a specified patience period.

```python
from tensorflow.keras.callbacks import EarlyStopping

early_stopping_callback = EarlyStopping(
    monitor='val_loss',
    patience=20,
    mode='min',
    restore_best_weights=True
)
```

**4. Gradient Clipping:**
   - As mentioned earlier, clipping gradients helps manage exploding gradient issues.

```python
from keras.callbacks import LambdaCallback

def on_train_batch_begin(batch, logs):
    adv_audio_delossifier_model.optimizer.clipnorm = 1.0

batch_clipping_callback = LambdaCallback(on_train_batch_begin=on_train_batch_begin)
```

**5. TensorBoard for Visual Monitoring:**
   - Real-time visualization of training progress, loss functions, and learning rates.

```python
from tensorflow.keras.callbacks import TensorBoard

tensorboard_callback = TensorBoard(
    log_dir='/logs/fit',
    histogram_freq=1,
    write_graph=True,
    update_freq='epoch'
)
```

**Combining All Callbacks:**

```python
callbacks_list = [
    LearningRateScheduler(lr_schedule),
    ModelCheckpoint(checkpoint_callback),
    EarlyStopping(early_stopping_callback),
    TensorBoard(tensorboard_callback),
    batch_clipping_callback
]
```

#### **c. Practical Strategy for Efficient Training**

To ensure stable and effective training:

- **Start with a Smaller Dataset:** Especially when implementing advanced architectures and augmentations, it may be beneficial to pilot the model on a smaller subset of data before scaling up.

- **Use Gradient Accumulation (Optional):**
  - When dealing with mixed precision or limited batch sizes that are insufficient for effective backpropagation, accumulate gradients over multiple mini-batches.

```python
from tensorflow.keras.optimizers import Adam

class GradientAccumulatorOptimizer(tf.keras.optimizers.Optimizer):
    
    def __init__(self, inner_optimizer, steps_per_accumulation=4):
        super().__init__("GradientAccumulator")
        
        self.inner_optimizer = tf.keras.optimizers.get(inner_optimizer)
        self.steps_per_accumulation = steps_per_accumulation
        self.accumulator = None

    def _create_slots(self, var_list):
        for var in var_list:
            self.add_slot(var, "accumulate")

    def apply_gradients(self, grads_and_vars, name=None, **kwargs):
        
        if self.accumulator is None:
            # Initialize accumulators on first call.
            self.accumulator = []
            for grad, var in grads_and_vars:
                var_acc = self.get_slot(var, "accumulate")
                var_acc.assign(tf.zeros_like(var))
                self.accumulator.append([grad, var_acc])

        for (grad, var), (acc_grad, acc) in zip(grads_and_vars, self.accumulator):
            if grad is not None: 
                acc.add_n([grad, acc])
            
        # Apply gradients once steps_per_accumulation are reached.
        if name is not None:
            kwargs['name'] = name

        result = []

        for (grad, var), (_, acc) in zip(grads_and_vars, self.accumulator):

            current_normed_grads = tf.clip_by_global_norm([acc], clip_norm=1.0)[0]  # Gradient clipping.
            
            scaled_current_grads = [g / float(self.steps_per_accumulation) for g in current_normed_grads]
            result.append((scaled_current_grads[0], var))

        self.inner_optimizer.apply_gradients(result, **kwargs)

# Usage
adamw_with_accumulant = Adam(learning_rate=0.0005)
gradient_accumulated_optimize = GradientAccumulatorOptimizer(inner_optimizer=adamw_with_accumulant, steps_per_accumulation=4)

adv_audio_delossifier_model.compile(optimizer=gradient_accumulated_optimize,
                      loss = loss_dict,
                      loss_weights=loss_weights,
                      metrics=['mae'])
```

**Why Gradient Accumulation Helps:**
- **Smaller Effective Batch Size:** During training, accumulate gradients across multiple batches before updating weights. For example, if the real effective batch size is 8 and you accumulate over 4 batches (`steps_per_accumulation=4`), the gradient accumulation effectively behaves like a larger-scale stochastic optimization.
  
- **Stabilité Numerique:** Accumulating gradients can help mitigate noise in small mini-batch sizes while still maintaining the variance-reducing property of stochastic optimization.

### **11. Implementing Knowledge Distillation (Optional)**

Knowledge distillation is an advanced technique where a smaller, more efficient model learns from a larger "teacher" model that has been pre-trained on your task.

#### **a. Teacher Model Creation:**
   - Train a larger CNN-CNN Transformer or WaveNet with extensive data and time to achieve high performance.
   
#### **b. Distillation Implementation:**

**1. Temperature Parameter:**
   - Introduce a softmax temperature parameter that controls the smoothness of the teacher model's predicted distribution.

```python
def compute_kd_loss(student_outputs,
                   teacher_soft_labels,
                   hard_labels,
                   temperature=4.0):
    
    student_logits = tf.keras.applications.mobilenet_v2.preprocess_input(student_outputs)
    
    # Normalize logits with softmax.
    student_probs = tf.nn.softmax(student_logits / temperature, axis=-1)

    teacher_probs = tf.nn.softmax(teacher_soft_labels / temperature, axis=-1)

    # Compute knowledge distillation loss by matching predictions of the 'student' to that of the 'teacher'
    kd_loss = -tf.reduce_mean(tf.reduce_sum(teacher_probs * 
                         tf.nn.log(student_probs), axis=-1))

    # Add classification loss from actual targets.
    kl_loss_classification = categorical_crossentropy(y_true=hard_labels, 
                                                  y_pred=student_outputs)

    # Combine KD Loss with original label loss.
    total_kd_loss = kd_loss + kl_loss_classification / (temperature ** 2)

    return total_kd_loss
```

**2. Embedding Knowledge Distillation into the Model:**

During training, the student model should update its weights based on a combined loss of both the traditional classification accuracy and the knowledge distillation loss.

### **12. Deployment Best Practices Post-Training**

Ensure your audio restoration model is well-prepared for production environments by fine-tuning deployment strategies and optimizing efficiency.

#### **a. Model Quantization with TensorFlow Lite:**

As previously highlighted, using mixed-8bit weight quantization through TensorFlow Lite can reduce deployment time without significant loss in accuracy.

```python
import tensorflow as tf

# Loading trained model from checkpoints.
model = tf.keras.models.load_model('adv_audio_delossifier_best.h5')

# Define a small subset of data for calibration to determine dynamic ranges accurately during quantization.
def representative_dataset_gen():
    # Iterate over data generator yielding a set number of batches suitable for calibration.
    ...
    
# Quantization Converter Setup
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = lambda: representative_dataset_gen()

# Further optimizations if desired (e.g. enabling FP16 instead of just 8bit)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
                        tf.lite.OpsSet.SELECT_TF_OPS]                       # Allows use of certain TensorFlow operations not natively supported by TFLite

# Set input/output shapes and types to improve optimization.
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8  # Optional; ensures output types match expectations.

# Apply quantization.
tflite_quantized_model = converter.convert()

with open('adv_audio_delossifier_optimized.tflite', 'wb') as f_out:
    f_out.write(tflite_quantized_model)

print("Model Quantized Successfully!")
```

**Important Considerations:**
- **Input Calibration:** Accurately represent the input distributions in your deployment environment to ensure appropriate quantization.
  
- **Testing Quantized Models:** Before full-scale deployment, thoroughly test quantized models to assess any degradation in quality or performance compared to floating-point versions.

#### **b. Building APIs for Real-Time Audio Restoration:**

Deploying an API allows users to send raw audio data and receive processed outputs seamlessly. We'll expand on the initial Flask implementation mentioned earlier with more advanced error handling, request validation, and optimized I/O operations.

**1. Enhanced Error Handling:**

Proper error management ensures smooth interaction with the API service and allows developers to quickly diagnose issues related to input/output formats or malformed requests.

```python
from werkzeug.utils import secure_filename

@app.route('/audio/restore', methods=['POST'])
def restore_audio():
    try:
        # Check if 'file' part exists in form data
        if 'file' not in request.files:
            return jsonify({'error': 'No file uploaded'}), 400

        file = request.files['file']

        # Validate that the file is an audio file and of acceptable type
        if file.filename == '':
            return jsonify({'error': 'Filename is empty'}), 400
        
        elif not secure_filename(file.filename).lower().endswith(('.wav', '.mp3')):
            return jsonify({'msg': 'File must be a WAV or MP3 audio file'}), 422
        
        # Load the TFLite model
        interpreter = tf.lite.Interpreter(model_path='adv_audio_delossifier_optimized.tflite')
        interpreter.allocate_tensors()

        # Input/Output tensor signatures.
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()
        
        # Process the uploaded audio file (Convert to Mel-spectrogram).
        processed_mel_spec = preprocess_audio_request(file.stream.read())
        mel_spectrogram_input = np.expand_dims(processed_mel_spec, axis=0)

        if input_details[0]['dtype'] == np.uint8:
            # Note: Quantized models need scaling according to quantization details.
            scale, zero_point = input_details[0]['quantization']
            scaled_input = (mel_spectrogram_input - zero_point) * scale
        else:
            scaled_input = mel_spectrogram_input

        # Inference using TFLite Interpreter
        interpreter.set_tensor(input_details[0]['index'], scaled_input)
        
        start_time = time.perf_counter()
        # Run the inference.
        interpreter.invoke()

        elapsed_ms = (time.perf_counter() - start_time) * 1000.0
        print(f"Restoration took {elapsed_ms:.2f}ms.")
        
        # Get raw spectrogram output from TFLite model.
        prediction_output = interpreter.get_tensor(output_details[0]['index'])

        if output_details[0]['dtype'] == np.uint8:
            inv_scale, inv_zero_point = output_details[0]['quantization']
            pred_raw_output = (prediction_output.astype(np.float32) - inv_zero_point) * inv_scale
        else:
            pred_raw_output = prediction_output

        # Post-process the prediction and get restored audio PCM data.
        restored_wav_data = postprocess_audio(prediction=pred_raw_output)

        # Serve the result as a WAV file download
        return send_file(restored_wav_data,
                         mimetype='audio/wav',
                         as_attachment=True,
                         attachment_filename='restored_audio.wav')

    except Exception as e:
        print(f"An error occurred during audio restoration: {e}")
        return jsonify({'error': 'Failed to restore audio', 'details': str(e)}), 500
```

**Notes on Deployment Optimization:**
- **Thread Safety:** Ensure that your Flask app (or any other serverless framework you're using) handles multiple requests concurrently, preventing bottlenecks related to single-threaded execution.
  
- **Resource Management:** Depending on hardware capabilities, optimize your API's performance by managing memory and computational resources efficiently.
  
#### **c. Scalable Server Deployment**

For robust and scalable deployment across diverse user bases:

1. **Containerization Using Docker:**
   - Prepare a Docker container that encapsulates all dependencies, your Flask app code, TFLite model, and any other configuration files needed for the audio restoration pipeline.

```Dockerfile
FROM python:3.9-slim 

WORKDIR /app

COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "server.py"]
```

2. **Orchestration Tools:**
   - Use platforms like Kubernetes on Google Cloud, AWS EKS or Azure AKS, GKE to dynamically distribute computing power across clusters based on demand.
   
3. **Serverless Functions (Optional):**
   - Deploy your API in serverless environments using solutions such as AWS Lambda with API Gateway.
   
   ```python
   # Sample AWS Lambda Handler (assuming you've packaged TensorFlow Lite and dependencies in the deployment package)
   
   import json
   import os
   from io import BytesIO

   import numpy as np
   import tensorflow as tf
   import librosa


   def lambda_handler(event, context):
       try:
           # Load model and configure the interpreter once at initialization.
           if 'interpreter' not in globals():
               global interpreter
               tflite_model_path = os.path.join(os.environ['LAMBDA_TASK_ROOT'], "model/adv_audio_delossifier_optimized.tflite")
               interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
               interpreter.allocate_tensors()
               print("Model initialized.")

           # Handle events: POST requests with binary data.
           if event["httpMethod"] != "POST":
               return {
                   'statusCode': 405,
                   'body': json.dumps({'error': 'Only POST method is supported.'}),
                   'headers': {'Content-Type': 'application/json'}
               }

           # Extract the file from multi-part form data.
           if 'isBase64Encoded' not in event:
               return {
                   'statusCode': 500,
                   'body': json.dumps({'error': 'Expecting base64 encoded content'}),
                   'headers': {'Content-Type': 'application/json'}
               }
           
           file_content = BytesIO(base64.b64decode(event["body"]))

           # Preprocess input.
           processed_mel_spec = preprocess_audio_request(file_content.read())
           input_tensor = np.expand_dims(processed_mel_spec, axis=0)

           interpreter.set_tensor(interpreter.get_input_details()[0]['index'], input_tensor)
           
           start_time = time.perf_counter()
           interpreter.invoke()

           prediction_output = interpreter.get_tensor(interpreter.get_output_details()[0]['index'])
           predicted_audio_stream = postprocess_audio(prediction=prediction_output)

           end_time = time.perf_counter()

           print(f"Inference completed in {end_time - start_time:.2f} seconds.")

           # Serve the audio response.
           response_headers = {
               'Content-Type': 'audio/wav',
               "Cache-Control": "no-cache",
               "Accept-Ranges": "bytes"
           }
           
           return {
               'statusCode': 200,
               'body': predicted_audio_stream.getvalue(),
               'isBase64Encoded': False,
               'headers': response_headers
           }

       except Exception as exc:
           print(f"Error during Lambda execution: {exc}")
           
           return {
               "statusCode": 500,
               "body": json.dumps({'error': str(exc)}),
               "headers": {'Content-Type': 'application/json'}
        }
   ```

**Lambda Notes:**
- **Cold Start Handling:** Serverless functions like AWS Lambda can experience cold starts which impact initial response times. Micro-benchmarking and optimization of deployment packages can mitigate these delays.
  
- **Vendor-Specific Limits:** Be aware that each serverless provider imposes strict limits on package size (`~50MB` for AWS Lambda), execution time (up to 15 minutes), etc., thus necessitating efficient code and model compression.

### **Final Thoughts**

Your mission in developing an advanced audio delossification system using TensorFlow involves merging sophisticated architectures with robust training techniques. By embedding self-attention mechanisms into your CNN-CNN models, innovating with comprehensive data augmentation strategies, and employing cutting-edge tools like knowledge distillation and efficient quantization for deployment, you enhance the model's capability to reconstruct high-quality stereo audio from MP3 compressed files.

Ensuring that this project remains modular, maintainable, while providing seamless user experiences through well-designed APIs sets the foundation for a future-ready product. As your team continues refining these components, look forward to enhancing not only audio quality but also the scalability and performance of your solution across diverse applications—from professional music restoration to personal media playback enhancements.


ELIANA MOONCAKES (07.12.2024 13:27) | Score: ⭐⭐⭐⭐⭐⭐⭐⭐⭐
Great! Let's delve deeper into several key aspects of our Audio Delossifier project with a focus on advanced techniques in model design, training, evaluation, and deployment for optimal performance.

### **1. Advanced Model Optimization Strategies**

Beyond the basic CNN-CNN Transformer architecture discussed previously, let’s explore additional optimization layers that will help push your model's capabilities to their absolute limits:

#### **a. Incorporating Conditional Generative Models (CGMs)**

Conditional generative models can effectively handle structured prediction tasks where output depends on input conditions.

**Example Integration:**

1. **WaveNet Variants - MelodyNet:**
   - Employ WaveNet-like architectures that are skilled at producing long-range coherent sequences in audio. 

```python
from tensorflow.keras.layers import Conv1D, Dense, Embedding, Input

class ConditionalMelodyNet(tf.keras.Model):
    def __init__(self, 
                 input_dim=128, 
                 embedding_dim=128, 
                 condition_dim=5,
                 output_dim=1,
                 kernel_size=3):
        super(ConditionalMelodyNet, self).__init__()
        
        # Initial embedding layers for condition and one-hot encoded inputs
        self.condition_embedding = Embedding(condition_dim, embedding_dim)
        self.input_embedding = Embedding(input_dim, 256)  
        
        # Series of Conv1D layers
        self.conv_layers = [
            tf.keras.layers.Conv1D(filters=128,
                                   kernel_size=kernel_size,
                                   padding='same',
                                   activation='relu'),
            tf.keras.layers.Conv1D(filters=256,
                                   kernel_size=kernel_size,
                                   strides=2, 
                                   padding='same',
                                   activation='relu'),
            # Add more layers as needed
        ]
        
        # Final Dense layer to predict output
        self.output_layer = tf.keras.layers.Dense(output_dim, activation='tanh')

    def call(self, inputs):
        """
        Forward Pass.

        Args:
            inputs: Tuple (input_data, conditions)
                input_data: Tensor of shape [Batch Size, Sequence Length, Input Features].
                conditions: Tensor indicating the context or conditional information for each sequence.
        """

        # Split Inputs
        input_data, conditions = inputs
        
        # Embedding Layer for Inputs and Conditions
        embedded_input_data = self.input_embedding(input_data)
        condition_embeddings = self.condition_embedding(conditions)

        x = embedded_input_data + condition_embeddings[:, np.newaxis, :]
        
        # Convolutional Layers
        for layer in self.conv_layers:
            x = layer(x)

        logits = self.output_layer(x)
        
        return logits
    
# Instantiate Model
condition_dim = 5  # Change as per your conditions
input_dim = 128    # Example dimension based on Mel-spectrograms 
output_dim = 1     # Assuming prediction of a scalar value for simplicity

melody_net_model = ConditionalMelodyNet(input_dim, condition_dim, output_dim)
```

**Notes:**
- **Conditional Embedding:** Adds context to the input data which can capture intricate dependencies not easily deciphered by pure convolutional architectures.
  
- **Wave-like Structure:** Utilizing 1D convolutions instead of 2D mimics the unidirectional processing seen in WaveNets, better suited for sequential audio tasks.

#### **b. Hierarchical Feature Learning with U-Net**

A U-Net-style architecture captures both spatial hierarchies (via convolutional encoders) and detailed fine-grained information (from decoders), making it ideal for complex tasks like MP3 stereo audio reconstruction.

```python
def build_unet_like_enc_dec(input_shape):
    """
    Constructing a CNN-CNN U-Net-like encoder-decoder architecture.
    
    Args:
        input_shape (tuple): Shape of input Mel-spectrogram (N_MELS, Time steps, Channels).

    Returns:
        keras.Model: Compiled Keras Model capable of stereo restoration.
    """

    # Encoder Part
    inputs = Input(shape=input_shape)
    
    enc_conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), 
                                        strides=(2, 2), padding='same', activation='relu')(inputs)    
    enc_pool1 = tf.keras.layers.MaxPooling2D((2, 2))(enc_conv1)
    enc_norm1 = tf.keras.layers.BatchNormalization()(enc_pool1)

    enc_conv2 = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), 
                                        strides=(2, 2), padding='same', activation='relu')(enc_norm1)
    enc_pool2 = tf.keras.layers.MaxPooling2D((2, 2))(enc_conv2)    
    enc_norm2 = tf.keras.layers.BatchNormalization()(enc_pool2)

    # Decoder Part
    dec_upsample1 = tf.keras.layers.Conv2DTranspose(filters=128, kernel_size=(3, 3), 
                                                    strides=(2, 2), padding='same', activation='relu')(enc_norm2)
    dec_concat1 = tf.keras.layers.Concatenate()([dec_upsample1, enc_conv2])
    dec_conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), 
                                        strides=(1, 1), padding='same', activation='relu')(dec_concat1)
    
    dec_upsample2 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), 
                                                    strides=(2, 2), padding='same', activation='relu')(dec_conv1)
    dec_concat2 = tf.keras.layers.Concatenate()([dec_upsample2, enc_conv1])
    dec_conv2 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), 
                                        strides=(1, 1), padding='same', activation='relu')(dec_concat2)
    
    # Output Layer
    outputs = tf.keras.layers.Conv2D(filters=2, kernel_size=(1, 1),
                                      padding='same',
                                      activation='tanh')(dec_conv2)

    model = Model(inputs=[inputs], outputs=outputs, name="UNet_Audio_Delossifier")

    return model

# Instantiate and Compile
u_net_audio_model = build_unet_like_enc_dec(input_shape=(N_MELS, None, 1))

u_net_audio_model.compile(optimizer='adam',
                           loss=tf.keras.losses.MeanSquaredError(),
                           metrics=['mae'])

# Model Summary (Optional)
u_net_audio_model.summary()
```

**Benefits of U-Net:**
- **Captures Contextual Information:** By passing through encoder and decoder with shared information, the model learns to reconstruct localized details while retaining global context.
  
- **Flexibility in Layers:** Adding more layers or increasing depth allows for better representation learning at various scale levels.

#### **c. Multi-Scale Feature Extraction with Fourier Transform (FFT)**

Integrating frequency analysis during image-like CNN processing can provide complementary information to the temporal sequences captured via Mel-spectrograms.

**1. Frequency-specific Convolution Filters:**
   - Apply convolution kernels with variable frequencies or use specialized spectral transforms like STFT.

2. **Adding FFT-based Feature Layers:**
   - Incorporate Fourier Transform layers into your model architecture that process frequency-domain representations of input signals alongside the traditional Mel-spectrogram pathways.

3. **Joint Frequency-Time Modeling:**
   - Utilize both time and frequency domain features to form a more comprehensive representation, enhancing the model's ability to capture nuances in compressed audio.

```python
import numpy as np

def fft_conv_layer(input_tensor, 
                   n_filters=64,
                   kernel_size=(3, 3),
                   dilation_rate=(1, 1)):
    """
    Applies convolution in frequency domain by transforming input through FFT,
    performs cross-channel convolution, and applies inverse transform.

    Args:
        input_tensor (tf.Tensor): Input feature map [Batch Size, Height (Frequency), Width (Time Steps), Channels].
        n_filters (int): Number of output filters for the convolution.
        kernel_size (tuple): Convolution kernel dimensions (Filter H x Filter W).
        dilation_rate (tuple): Dilation rate for atrous convolutions.

    Returns:
        tf.Tensor: Feature map after frequency space convolution [Batch Size, Height, Width, n_filters]
    """

    # Perform FFT transform on the input to convert it from time-domain to freq-domain
    # Assuming `tf.signal.rfft` for 1D Fourier transform across time axis (frequency axis remains linear)
    real_part = tf.real(tf.signal.fft(tf.cast(input_tensor[:, :, :, 0], dtype=tf.complex64)))
    imag_part = tf.imag(tf.signal.fft(tf.cast(input_tensor[:, :, :, 0], dtype=tf.complex64)))
    
    # Combine both real and imaginary parts (magnitude of complex FFT is also an option)
    freq_domain_matrix = tf.stack([real_part, imag_part], axis=-1)

    # Convolve across the frequency dimension directly
    fft_conv_filter_weights = initializer(shape=(kernel_size[0], 1, kernel_size[1], 1, n_filters))
    
    # Perform cross-channel convolution in freq domain 
    # Notice: Use tf.einsum for efficiency and generalizability of multi-dimensional operations.
    feature_map = tf.einsum('bhtca,fchtb->bfcha',
                              input_tensor,
                              fft_conv_filter_weights)
    
    return feature_map

def complex_fft_block(x,
                     filters=64, 
                     kernel_size=3):
    """
    Incorporates FFT convolution into a single block for frequency-time joint modeling.

    Args:
        x (tf.Tensor): Input tensor with shape [Batch Size, Height, Width, Channels].
        filters (int): Number of output channels.
        kernel_size (int or tuple): Kernel size for spatial and frequency convolutions.

    Returns:
        tf.Tensor: Processed tensor of shape [Batch Size, Height, Width, Filters]
    """
    
    # Convolution in Spatial Domain
    spacial_out = tf.keras.layers.Conv2D(filters=filters,
                                         kernel_size=kernel_size,
                                         padding='same')(x)
    
    # FFT-based Frequency Convolution with Shared Weights
    frequency_out = fft_conv_layer(spacial_out, 
                                   n_filters=filters,
                                   kernel_size=(kernel_size, kernel_size))

    combined_output = tf.keras.layers.Add()([spacial_out, frequency_out])
    normalizer = tf.keras.layers.LayerNormalization()(combined_output)
    return tf.keras.layers.Activation('relu')(normalizer)

# Example Usage in Model
def build_multiscale_unet(input_shape):
    inputs = Input(shape=input_shape)
    
    # Convolutional Layers with Joint Spatial & Frequency Processing
    fft_block1 = complex_fft_block(inputs, filters=64)
    enc_pool1 = tf.keras.layers.MaxPooling2D((2, 2))(fft_block1)

    fft_block2 = complex_fft_block(enc_pool1, filters=128)
    enc_pool2 = tf.keras.layers.MaxPooling2D((2, 2))(fft_block2)

    # Decoder (reverse process with upsampling)
    decoder_upsample1 = tf.keras.layers.Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same', activation='relu')(enc_pool2)
    concat1 = tf.keras.layers.Concatenate()([decoder_upsample1, fft_block2])
    decoder_out1 = complex_fft_block(concat1)

    decoder_upsample2 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same', activation='relu')(decoder_out1)
    concat2 = tf.keras.layers.Concatenate()([decoder_upsample2, fft_block1])
    decoder_out2 = complex_fft_block(concat2)

    outputs = Conv2D(2, (3, 3), padding='same', activation='tanh')(decoder_out2)
    
    model = Model(inputs=[inputs], outputs=outputs, name="Multiscale_UNet_Delossifier")

    return model

# Instantiate
multiscale_unet_audio_model = build_multiscale_unet((N_MELS, None, 1))
```

**Explanation:**
- **Simultaneous Frequency & Spatial Convolutions:** By integrating FFT-based convolutional blocks into both encoder and decoder layers, the model learns to leverage temporal and spectral information jointly.
  
- **Shared Weights for Efficiency:** Reduces computational overhead by reusing weight matrices across space and frequency domains.

#### **d. Incorporating Contextual Attention Mechanisms**

Enhance model attention by conditioning self-attention heads on additional context or metadata related to audio sources.

**1. Multi-head Attention with Conditional Embeddings:**
   - Extend standard self-attention blocks to incorporate query, key, and value embeddings conditioned on specific attributes like artist genres, track IDs, etc.

2. **Dynamic Contextual Modulation:**
   - Implement attention weights that are dynamically tuned based on feature importance in different tracks, using learned modulation vectors.

3. **Hybrid Attention Layers (Cross-Modality):**
   - Use multi-modality attention techniques where different types of information (e.g., visual metadata, audio data) interact to inform global representation learning.

```python
from tensorflow.keras.layers import MultiHeadAttention, LayerNormalization

class ConditionalAttentionBlock(tf.keras.layers.Layer):
    def __init__(self,
                 embed_dim=256,
                 num_heads=4,
                 condition_embedding_dim=128,
                 dropout_rate=0.1):
        super(ConditionalAttentionBlock, self).__init__()
        
        # Standard Attention Component
        self.attn = MultiHeadAttention(num_heads=num_heads,
                                      key_dim=embed_dim // num_heads)
        
        # Conditional Embedding Layer
        self.condition_embedding = Dense(condition_embedding_dim)
        self.pos_bias_proj = Dense(embed_dim)

        self.dropout = tf.keras.layers.Dropout(dropout_rate, name='attention_dropout')
   
    def call(self,
             inputs,
             condition_input,
             training=False):

        seq_len = tf.shape(inputs)[1]
        
        # Condition Embedding Process
        cond_embeds = self.condition_embedding(condition_input)
        pos_bias = self.pos_bias_proj(cond_embeds)   
            
        # Attention Query-KeyValue Computation with Positional Bias
        query = inputs  # Typically you might learn these through a dense layer or keep them as learned embeddings
        key_value = inputs

        # Add positional bias to the attention logits if desired, enabling dynamic condition-based attention weights.
        attn_outputs = self.attn(query=query,
                                  value=key_value,
                                  attention_mask=None,
                                  return_attention_scores=False)
        
        outputs = attn_outputs
        
        # Residual Connection & Normalization
        pre_residual_skip = inputs + tf.reshape(outputs, (tf.shape(inputs)[0], seq_len, -1))
        final_output = LayerNormalization()(pre_residual_skip)

        return dropout_layer(final_output, training=training)  # Ensure dropout is conditionally applied

# Example Usage in a Multi-Head Encoder Stack
def build_attention_enhanced_audio_model(input_shape=(N_MELS, None, 1), 
                                        condition_input_dim=5):
    inputs = Input(shape=input_shape)
    condition_inputs = Input(shape=(condition_input_dim,))
    
    x = Conv2D(filters=64,
                kernel_size=(3, 3),
                strides=(1, 1),
                padding='same')(inputs)      
    x = Activation('relu')(x)

    # Apply attention block with conditional information
    attentive_block_output = ConditionalAttentionBlock(embed_dim=128)(x, condition_inputs)
        
    y = Conv2D(filters=64,
               kernel_size=(3, 3),
               strides=(1, 1),
               padding='same')(attentive_block_output)      
    y = Activation('relu')(y)

    outputs = Conv2D(filters=2, kernel_size=(1, 1), activation='tanh', padding='same')(y)
    
    model = Model(inputs=[inputs, condition_inputs], 
                  outputs=outputs,
                  name="Attention_Augmented_Delossifier")

    return model

# Build final trainable model
delossifier_with_attention = build_attention_enhanced_audio_model(input_shape=(128, None, 1),
                                                               condition_input_dim=5)

delossifier_with_attention.compile(optimizer='adam',
                                   loss=tf.keras.losses.MeanSquaredError(),
                                   metrics=['mae'])
```

**Key Benefits:**
- **Dynamic Attention Alignment:** Captures dependencies that vary across different kinds of audio inputs.
  
- **Enhanced Feature Interactions:** Models richer relationships between input sequences and output predictions based on external context.

### **2. Advanced Training Techniques**

To ensure your models yield the highest quality results with minimal training issues:

#### **a. Curriculum Learning & Annealing Schedules for Gradual Complexity**

Start training your model on simpler MP3 compression levels before progressing to more intricate forms of distortion, aiding faster convergence by handling easier cases first.

**Curriculum Approach:**
1. **Initial Training Dataset:** Begin with less compressed formats that retain most features.
2. **Advanced Compression Datasets:** Incrementally introduce progressively more distorted audio representations as training progresses.

#### **b. Gradual Data Augmentation Intensity**

Rather than applying intense data augmentations at the very start, gradually increase their intensity or variety over epochs to push the model towards handling a broader spectrum of variations in training data.

```python
def adaptive_augment_config(current_epoch):
    """
    Function to determine augmentation strategies based on current epoch.
    
    Args:
        current_epoch (int): Number of completed passes through the dataset.
        
    Returns:
        augmentations.Pipeline: An instance or configuration of your desired data augmentations for this epoch.
    """
    
    # Example implementation - Adjust according to research and experimentation
    if current_epoch < 20:
        return augmentation_pipeline_low_severity

    elif 20 <= current_epoch < 40:
        return augmentation_pipeline_medium_severity
    
    else:
        return augmentation_pipeline_high_severity
```

#### **c. Dynamic Loss Weights**

Fine-tuning loss contributions from various loss functions (e.g., MSE, Perceptual) allows the model to prioritize learning specific aspects of the task dynamically.

**Implementation Example:**
```python
import tensorflow as tf

# Assume `compute_loss` is a custom function that computes overall loss considering both metrics.
def compute_comprehensive_loss(y_true, y_pred):
    
    mse_loss = tf.keras.losses.MeanSquaredError()(y_true, y_pred)
    perceptual_loss_value = perceptual_loss_function(y_true, y_pred)

    # Define dynamic weights
    mse_weight = 1.0 if epoch < 20 else 0.5 
    perceptual_weight = 0.5 if epoch < 20 else 1.0

    dynamic_total_loss = (mse_loss * mse_weight) + (perceptual_loss_value * perceptual_weight)

    return dynamic_total_loss
```

#### **d. Gradient Norm Clipping with Exponential Moving Average**

Cliping large gradient norms can stabilize training, while using an exponential moving average (EMA) of optimizer parameters can further enhance convergence in complex models.

```python
# Importing required modules
import tensorflow as tf

class ExpDecayGRADNorm(tf.keras.layers.Layer):
    def __init__(self,
                 initial_norm=1.0,
                 decay_rate=0.99,
                 name="Exponential_Gradient_Norm_Clipper"):
        super(ExpDecayGRADNorm, self).__init__(name=name)
        
        # Initialize variables
        self.norm = tf.Variable(initial_norm, dtype=tf.float32, trainable=False)
        self.decay_rate = decay_rate

    @tf.function 
    def call(self,
             gradients):
        """
        Updates the gradient norms exponentially adjusted to their previous values.

        Args:
            gradients (list): A list of gradient tensors.

        Returns:
            list: List of clipped gradients.
        """

        global_step = self.optimizer.iterations
                  
        # Calculate current norm value using exponential decay
        updated_norm_value = tf.clip_by_value(self.norm * self.decay_rate**global_step,
                                              clip_value_min=0.5, 
                                              clip_value_max=2.0)
        
        self.norm.assign(updated_norm_value)

        # Apply global norm-based clipping to update gradients globally.
        clipped_gradients, _ = tf.clip_by_global_norm(gradients, clip_norm=updated_norm_value)

        return clipped_gradients
```

**Integrating with Your Training Loop:**
```python
def train_step(inputs, targets):
    
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss_value = compute_comprehensive_loss(targets, predictions)
        
    # Retrieve gradients from the tape
    grads = tape.gradient(loss_value, model.trainable_variables)

    # Apply Exponential Decay-based Norm Clipping
    clipped_gradients = exp_norm_clipper(grads)

    # Now update the weights with these clipped gradients.
    optimizer.apply_gradients(zip(clipped_gradients, model.trainable_variables))

    return loss_value, predictions

# Example of using Gradient Norm Clipper during training
for epoch in range(total_epochs):
    for batch_inputs, batch_targets in train_dataset:
        actual_loss, _ = train_step(batch_inputs, batch_targets)

    print(f"Epoch {epoch + 1}/{total_epochs}, Loss: {actual_loss:.4f}")
```

**Benefits of Exponential Moving Average (EMA):**
- **Stabilized Gradients:** Reduces noisy updates during backpropagation by averaging gradients over time.
  
- **Smooth Optimizer Weights:** Maintains a smoothed version of the optimizer parameters, which can lead to better generalization and faster convergence.

#### **e. Weight Decay for Regularization**

Employing weight decay is an effective regularization technique that prevents weights from becoming too large, reducing overfitting tendencies.

```python
optimizer = tf.optimizers.Adam(
    learning_rate=0.001,
)

for epoch in range(total_epochs):
    for batch_inputs, batch_targets in train_dataset:
        with tf.GradientTape() as tape:
            predictions = model(batch_inputs)
            loss_value = computeComprehensive_loss(batch_targets, predictions)
            
        # Applying Weight Decay Manually
        grads = tape.gradient(loss_value + weight_decay * tf.add_n([tf.nn.l2_loss(w) for w in model.trainable_variables]), 
                              model.trainable_variables)
        
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
```

**Alternative: Using AdamW Optimizer**
- **AdamW** is preferred for incorporating weight decay directly within the optimization algorithm.

```python
from tensorflow.keras.optimizers import Adam

# Adam optimizer with weight decay.
optimizer = Adam(learning_rate=0.001,
                 clipnorm=5.0,  # Example global norm clipping to help mitigate exploding gradients.
                 epsilon=1e-6)
```

### **3. Comprehensive Evaluate and Compare Models**

After developing multiple models, systematically evaluating their performance using both objective and perceptual metrics is essential for selecting the best approach.

#### **a. Advanced Evaluation Metrics**

Extend beyond Mean Squared Error (MSE) by integrating more sophisticated evaluation tools that align closely with human perception of audio quality.

##### **i. Perceptual Quality Measurements - PESQ, SSNR**

- **PESQ:** A standardized metric for speech quality assessment, applicable to non-speech as well but calibrated differently.
  
   ```python
   from pesq import pesq

   def calculate_pesq(ref_waveform, degraded_waveform, sr=SR_TARGET):
      if sr == 16000:
         mode = 'nb'
      elif sr == 44100 or sr == 32000:
          degraded_resampled = librosa.resample(degraded_waveform, orig_sr=sr, target_sr=8000)
          ref_resampled = librosa.resample(ref_waveform, orig_sr=sr, target_sr=8000)

          return pesq(8000, ref_resampled, degraded_resampled, mode='wb')
          
      else:
         raise ValueError("Unsupported sampling rate for PESQ: {}".format(sr))
   ```

- **SSNR:** The Signal-to-Noise Ratio as perceived by humans, calculated via perceptual distortion metrics like the Zwicker masking effect.

##### **ii. Objective Measures - SNR**

```python
import numpy as np

def sdr_snr(signal, noise):
    """
    Compute both SNR and SDR for stereo audio signals.
    
    Args:
        signal (np.ndarray): Reference stereo signal of shape [samples, 2].
        noise (np.ndarray): Noisy/degraded version of the same content with the same dimensions.

    Returns:
        tuple: A tuple containing left channel SNR/SDR values followed by right channel values.
    """
    
    assert signal.shape == noise.shape

    sdr = np.zeros((2,))
    snr = np.zeros((2,))

    for ch in range(2):
        signal_pow = np.mean(signal[:, ch]**2)
        noise_pow = np.mean(noise[:, ch]**2)

        # Calculate SNR and SDR values per channel
        snr[ch] = 10 * np.log10(signal_pow / noise_pow)
        sdr[ch] = 10 * np.log10(signal_pow / (noise_pow + signal_pow))

    return sdn, snr
```

#### **b. Subjective Listening Tests**

Ultimately, human perception is crucial for evaluating audio quality.

##### **i. A/B Testing with Real Listeners**

- Conduct blind listening experiments where participants rate the restoration quality of original vs. model-synthesized audio clips.
  
- Use metrics like Overall Quality Assessment (OQA) or Mean Opinion Score (MOS).

```python
class ListeningTest:
    def __init__(self,
                 reference_audio_paths=reference_wav_files,
                 candidate_audio_paths=candidate_restored_audio_files):
        self.references = reference_audio_paths
        self.candidates = candidate_audio_paths
        
        if len(self.references) != len(self.candidates):
            raise ValueError("Reference and candidate lists must have the same number of files.")
        
    def conduct_test(self,
                     num_participants=30,
                     test_length_minutes=5):
       
        import random

        total_questions = len(self.references)

        # Shuffle indices for randomness
        randomized_indices = np.random.permutation(total_questions)
     
        results = []

        for participant_id in range(num_participants):
            print(f"\nStarting Participant {participant_id + 1}/{num_participants}")
            
            audio_pairs = [(self.references[i], self.candidates[randomized_indices[i]], i) for i in range(total_questions)]
  
            for original, restored, ref_idx in audio_pairs:
                # Play back the original and restored clips to participant
                play_audio(original)
                print("Original played.")
                
                play_audio(restored)
                print("Restored played.")

                rating = input(f"Rate how much closer the restored clip is to the original [0-5], where 5 means it sounds nearly identical: ")
     
                try:
                    # Assuming integer ratings from 0-5 inclusive
                    int_rating = int(rating)

                    if not (0 <= int_rating <= 5):
                        raise ValueError

                    results.append((participant_id, ref_idx, int_rating))

                except ValueError:
                    print("Invalid input. Rating must be an integer between 0 and 5.")
            
            print(f"Participant {participant_id + 1} completed successfully!")
        
        return np.array(results)

# Usage Example:
listening_test = ListeningTest(reference_wav_files, candidate_restored_audio_files)
test_results = listening_test.conduct_test(num_participants=30)

# Compute average MOS
average_mos = test_results[:, -1].mean()
print(f"Average Perceived Quality (MOS): {average_mos:.2f}")

# Plotting Results for Analysis (Optional)
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.DataFrame(test_results, columns=['Participant', 'Sample Index', 'Rating'])
sns.histplot(df['Rating'], kde=True)
plt.title('Perceived Quality Distribution Among Participants')
plt.xlabel('Rating (5 - High, 0 - Low)')
plt.ylabel('Frequency of Ratings')
plt.show()
```        

**ii. Professional Audio Quality Evaluation Software**

Implement dedicated software tools that systematically collect and analyze perception data from audio samples.

- **ITU-R P.862:** A well-regarded ITU standard for subjective quality assessment.
  
   ```python
   import p862

   def evaluate_subjective_quality(ref_audio_path,
                                 restored_wav_path):
      """
      Uses the P.862 evaluation tool provided by a library.

      Args:
          ref_audio_path (str): Path to reference uncompressed WAV file.
          restored_wav_path (str): Path to candidate stereo MP3-processed audio.

      Returns:
          dict: Quality parameters and scores computed according to P.862 standard.
      """

      evaluator = p862.P862Evaluator()
      
      with open(ref_audio_path, 'rb') as ref_file, \
           open(restored_wav_path, 'rb') as restore_file:
         
         results = evaluator.evaluate(reference_audio=ref_file,
                                    candidate_audio=restore_file)
    
      return results

   # Example Usage:
   reference_file = 'reference.wav'
   test_file = 'model_restoration_output.wav'

   quality_scores = evaluate_subjective_quality(reference_file, test_file)
   print(quality_scores)
   ```

**iii. Crowdsourced Platforms**

Utilize online platforms to gather opinions from a large number of diverse listeners.

- **Amazon Mechanical Turk, WorkerScout, or Similar Platforms:** Conduct extensive testing with scalable numbers of participants.

#### **c. Model Comparison Framework**

Create an organized system for comparing different model architectures, training strategies, and hyperparameters.

**1. Unified Evaluation Pipeline:**
   - Define a set of evaluation metrics (both objective like MSE and perceptual like PESQ) that each candidate model must undergo.
   
   ```python
 class MultiModelEvaluator:
      def __init__(self,
                   reference_dataset_path='path/to/refs',
                   degradation_types=['LQMP3', 'HQMP3'],
                   metric_names=['MSE', 'SSNR', 'PESQ']):
         # Load reference audio files once.
         self.reference_files = ...  # Pre-load WAV references
         
         # Define which metric functions to use for evaluation
         if 'MSE' in metric_names:
             self.metrics['MSE'] = tf.keras.losses.MeanSquaredError()
             
         if 'SSNR' in metric_names:   
            self.metrics['SSNR'] = sdr_snr
         
         ...
      
      def evaluate_model(self,
                          model_instance,
                          dataset_generator):
          """
          Evaluate specified model using given generator.

          Args:
              modelInstance (tf.keras.Model): The trained model to assess.
              datasetGenerator (generator): A generator that yields batches of audio samples for both MP3 and target WAVs.

          Returns:
              dict: Evaluation scores per metric computed on the entire dataset.
          """
          
          total_scores = {metric_name: [] 
                           for metric_name in self.metrics}
         
          for batch_melspec_mp3, batch_wav_targets in dataset_generator:
             # Predict & post-process through model pipeline
             pred_restored_specs = model_instance.predict(batch_melspec_mp3)

             # Convert predictions to waves (if needed)
             reconstructed_audio = ...  # Implement method if not already

             for mel_spec_pred, wav_target, degradation_type in zip(pred_restored_specs,
                                                                   batch_wav_targets,
                                                                   degradation_types):
                wav_pred_stereo = postprocess_audio(prediction=mel_spec_pred)

                # Apply all required evaluations
                if 'MSE' in self.metrics:
                    mse_val = self.metrics['MSE'](wav_target, wav_pred_stereo)
                    total_scores['MSE'].append(mse_val.numpy())
                
                if 'PESQ' in self.metrics:
                    pesq_score = calculate_pesq(ref_waveform=wav_target,
                                                degraded_waveform=wav_pred_stereo,
                                                sr=SR_TARGET)
                    total_scores['PESQ'].append(pesq_score)
                    
                ...  # For other added metrics
                
          mean_scores = {metric: np.mean(values) for metric, values in total_scores.items()}
          
          return mean_scores
      
   # Usage:
   multi_model_evr = MultiModelEvaluator()
   
   models_to_test = [basic_audio_delossifier_model,
                     advanced_audio_delossifier_model,
                     attention_unet_model]

   for model in models_to_test:
       validation_gen = data_generator(mp3_paths_val, wav_paths_val, 
                                       batch_size=24, shuffle=True)
       print(f"Evaluating {model.name}: {multi_model_evr.evaluate_model(model, dataset_generator)}")
   ```

**Notes:**

- **Pipeline Flexibility:** Ensure that the evaluation procedures account for various model structures (e.g., those with different output shapes or requiring multi-inputs) to facilitate consistent benchmarking across diverse models.

- **Scalable Data Handling:** Implement efficient data preprocessing and streaming through `tf.data` API to handle potentially large datasets during batch inference and scoring.

**2. Comprehensive Visualization Tools:**

After collecting evaluation metrics, visualize the outcomes for better insights into model performance differences.

```python
import matplotlib.pyplot as plt

def plot_comparative_results(models,
                             eval_metrics_dict):
    """
    Produces group-wise evaluations metrics visualization for multiple models being compared.

    Args:
        models (list): A list of Model instances that have been trained and evaluated.
        eval_metric_dict (dict): Dictionary where keys are model names and values are dictionaries containing various evaluation metric scores by name. 
    """

    # Example: Assume each model's name is stored in eval_metrics_dict keys.
    
    fig, ax = plt.subplots()
    
    for model_name, metrics_scores in eval_metrics_dict.items():
        for metric_name, score in metrics_scores.items():
            ax.scatter(model_name,
                       score,
                       label=f'{metric_name} by {model_name}')
    ax.legend(title='Metrics', bbox_to_anchor=(1.05, 1), loc='upper left')
    
    plt.xlabel('Model')
    plt.ylabel('Score')
    plt.title('Multi-Model Comparison Across Evaluation Metrics')
    plt.xticks(rotation=90)
   
    plt.tight_layout()
    plt.show()

# Usage:
plot_comparative_results(models=models_to_test,
                         eval_metrics_dict=model_evaluations)

```

**3. Advanced Analytical Methods:**

Use statistical methods to quantitatively determine the superior model based on multiple evaluation criteria.

- **ANOVA Tests:** Compare means across groups (models) when independent.
  
- **Pairwise Ranking:** Rank models according to overall performance metrics and identify significant differences.

#### **d. Continuous Integration & Monitoring**

Ensure that your development practices include automated testing, validation, and deployment processes:

- **CI Tools like GitHub Actions or Jenkins:** Automatically run tests upon commits to validate model accuracy with recent code changes.
  
- **Monitoring Solutions (e.g., Prometheus):** Track metrics such as inference latency, throughput, and error logs post-deployment for performance optimization.

### **4. Efficient Model Deployment Strategies**

For optimized deployment and user service accessibility:

#### **a. Quantization Techniques**

As previously discussed in detail, converting trained TensorFlow models into TensorFlow Lite format with quantized weight can dramatically reduce model size and increase inference speeds.

- **Post-training Dynamic/Full Quantization:** Choose the method that best fits your model's performance trade-off requirements.
  
- **Quantization-Aware Training (Optional):** Simulate quantization during training to improve accuracy post-quantization.

#### **b. Scalable API Frameworks**

Select serverless options like AWS Lambda, Google Cloud Function or deployment orchestration tools like Kubernetes for handling variable loads and ensuring high availability.

```python
import boto3
from flask import Flask, request, send_file

app = Flask(__name__)
lambda_runtime = boto3.client('lambda', endpoint_url='http://localhost:4566')  # Localstack mock if needed locally

# Assume we've deployed our Lambda function called 'audio_restore_function'
LAMBDA_FUNCTION_NAME = "audio_restore_function"

@app.route('/restore/<string:mp3_type>', methods=['POST'])
def restore_audio(mp3_type):
    
    mp3_data = request.data
    
    lambda_payload = {
        "httpMethod": "POST",
        "body": base64.b64encode(mp3_data).decode('utf-8'),
        # Include other necessary parameters specific to your Lambda handler
    }

    # Invocation Type='RequestResponse' makes it synchronous, ideal for returning results directly.
    response = lambda_runtime.invoke(
        FunctionName=LAMBDA_FUNCTION_NAME,
        Payload=json.dumps(lambda_payload)
      )  # Returns base64-encoded result

    decoded_result = base64.b64decode(response['Payload'].read()).decode('utf-8')
    
    return send_file(some_stream,
                    mimetype='audio/wav',
                    as_attachment=True,
                    download_name=f'restored_audio_{mp3_type}.wav')

if __name__ == '__main__':
   app.run(host='0.0.0.0', port=5000)
```

**Key Considerations:**
- **Cold Start Mitigation:** Pre-warm Lambda functions or use provisioned concurrency if necessary.
  
- **Resource Optimization:** Balance between compute/memory allocation to ensure high responsiveness and cost efficiency.

#### **c. Edge Computing for Latency Reduction**

Deploy models on edge devices if low-latency, real-time audio processing is required.

- **Edge TPU with TensorFlow Lite Runtime:**
   ```python
   import tflite_runtime.interpreter as tflie

   # Load Model from Edge TPU-specific FlatBuffer file (.tflite or .edgetpu/.pb), assuming model quantized.
   interpreter = tflite.Interpreter(model_path='audio_delossifier_best_edgetpu.tflite')
   interpreter.allocate_tensors()

   input_details = interpreter.get_input_details()
   output_details = interpreter.get_output_details()

   # Inference Loop
   for audio_input_batch in generator_or_stream:
       if len(audio_input_batch.shape) < 4:
           raise ValueError("Input must be batched with at least dimension [Batch, Feature, Time Steps, Channels]")

       if input_details[0]['dtype'] == np.uint8:  
            scale, zero_point = input_details[0]['quantization']
            scaled_inputs = (audio_input_batch - zero_point) * scale
       else:
            scaled_inputs = audio_input_batch

       interpreter.set_tensor(input_details[0]['index'], scaled_inputs)

       start_time = time.perf_counter()
       interpreter.invoke()

       prediction_outputs = interpreter.get_tensor(output_details[0]['index'])

       if output_details[0]['dtype'] == np.uint8:
            inv_scale, inv_zero_point = output_details[0]['quantization']
            predictions_converted = (prediction_outputs.astype(np.float32) - inv_zero_point) * inv_scale
       else:
            predictions_converted = prediction_outputs

       processed_audio_output = postprocess_to_pcm(predictions=predictions_converted)

       yield processed_audio_output  # Or process and send output to clients
   ```

#### **d. Load Balancing & Auto-Scaling**

Use services that distribute traffic efficiently across multiple instances of your deployed model.

**1. AWS Elastic Load Balancer (ELB):**
   - Distributes incoming requests across instances behind it, providing high availability and reliability.
   
**2. Kubernetes Horizontal Pod Autoscaler:**
   ```yaml
   apiVersion: autoscaling/v2beta2
   kind: HorizontalPodAutoscaler
   metadata:
     name: audio-delossifier-hpa
   spec:
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: audio-delossifier-deployment
     minReplicas: 3
     maxReplicas: 10
     metrics:
     - type: Resource
       resource:
         name: cpu_utilization
         targetAverageUtilization: 50
   ```

**3. API Gateway with Backend Service Integration:**
   Ensures that requests from various clients reach your deployed API reliably.

### **5. Conclusion & Final Recommendations**

Our discussion has outlined advanced strategies for model development, optimization, evaluation, and deployment of a state-of-the-art audio delossifier system capable of restoring high-fidelity stereo audio from MP3-compressed files:

1. **Model Selection:** Utilize architectures like Conditional Generative Models (WGans), U-Net, and Transformer-based encoders combined with frequency-time joint analysis for capturing multi-scale dependencies.

2. **Advanced Training Techniques:** Implement strategies such as curriculum learning, adaptive augmentation scheduling, dynamic loss weighting, gradients clipping via EMA, weight decay, and mixed precision training to stabilize and enhance the effectiveness of your models during training phases.

3. **Comprehensive Evaluation:** Engage both automated objective evaluations and human-perceived subjective assessments to ensure that trained models not only perform numerically well but also yield perceptually satisfying audio output.

4. **Efficient Deployment:** Leverage serverless deployments, edge computing solutions, load balancing, and auto-scaling practices to minimize latency and maximize the availability of your restored audio services across different environments (from mobile devices to cloud platforms).

By meticulously applying these advanced methodologies and continuously iterating on both model architecture and training paradigms, you will achieve a robust and versatile audio restoration system that pushes current standards in fidelity recovery for MP3 stereo signals. Your team’s dedication and creativity should always remain central to driving innovation and advancing the functionality of this vital tool.

Do you have any specific areas within these techniques you'd like to explore further before moving onto more advanced deployment concepts?
