_Spydaz_Web_SCIENCE_OFFICER_

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

# Function to convert a PIL Image to a base64 string
def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string


# Define a function to process each example in the dataset
def process_images_func(examples):

    texts = examples["text"]
    images = examples["image"]  # Assuming the images are in PIL format

    # Convert each image to base64
    base64_images = [image_to_base64(image) for image in images]

    # Return the updated examples with base64-encoded images
    return {
        "text": texts,
        "image_base64": base64_images  # Adding the Base64 encoded image strings
    }

# Load the dataset
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")

# Process the dataset by converting images to base64
processed_dataset = dataset.map(process_images_func, batched=True)

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

import numpy as np
import torch
import torchaudio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                             output_audio_griffin='griffin_reconstructed_audio.wav', 
                             output_audio_melgan='melgan_reconstructed_audio.wav',
                             extraction_method='pixel',  # 'pixel' or 'ifft'
                             decoding_method='griffin'):  # 'griffin' or 'melgan'
    """
    Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
    and decode it back to audio using the selected methods.
    
    Parameters:
    - audio_file: Path to the audio file to be processed.
    - output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
    - output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
    - output_audio_melgan: Path to save the MelGAN reconstructed audio.
    - extraction_method: Method for extraction ('pixel' or 'ifft').
    - decoding_method: Method for decoding ('griffin' or 'melgan').
    """
    # Step 1: Encode (Audio -> Mel-Spectrogram)
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Step 2: Convert Mel-Spectrogram to Image and save it
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Step 3: Extract Mel-Spectrogram from the image based on chosen method
    if extraction_method == 'pixel':
        extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
    elif extraction_method == 'ifft':
        extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
    else:
        raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
    
    # Step 4: Decode based on the chosen decoding method
    if decoding_method == 'griffin':
        decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
    elif decoding_method == 'melgan':
        decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
    else:
        raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )

if __name__ == "__main__":
    audio_file_path = 'your_audio_file.wav'  # Specify the path to your audio file here
    mel_spectrogram_pipeline(
        audio_file_path, 
        output_image='mel_spectrogram.png',
        output_audio_griffin='griffin_reconstructed_audio.wav',
        output_audio_melgan='melgan_reconstructed_audio.wav',
        extraction_method='pixel',  # Choose 'pixel' or 'ifft'
        decoding_method='griffin'  # Choose 'griffin' or 'melgan'
    )