README.md

---
base_model: 
- LeroyDyer/_Spydaz_Web_AI_LCARS_MASTER_SYSTEM
- LeroyDyer/_Spydaz_Web_LCARS_AdvancedHumanAI
- LeroyDyer/_Spydaz_Web_AGI_DeepThinker_LCARS_
- LeroyDyer/_Spydaz_Web_ONTOLOGY_OFFICER_
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---



## Creating Human Advance AI
Success is a game of winners. 


— # Leroy Dyer (1972-Present)
<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
HOT-MODEL!!
# The Human AI .
(a lot of bad models to get to this one ! finally ) Sorry its 32k  as this was the chunking used for the massive texts such as the bible ! which we chunked in as a whole as well as by passage ! 
as well as some ancent texts (Wallis Budge) i also find the model getting sTuck after 32k ~ in general it will perform very good in fact super good with 4096 max tokens as it will continue the response next turn ! so just say continue !
### So it was reduced form 128 to 32k :  
here we actually learned something !! --- Models should be trained with larger contexts ! infact the largr the better ... as this is real training ! , but the models may not actually accept the training at the higgher end as they fail about 4-8-16k tokens , so its important to chat to the model and find where the context repeats ..
or the responses repeats and then check how many tokens are in the chat/context window ! so we can log the point of failure ! here is your actual max tokens for the model ! so we can retrain at tha specific level to get a perfect long responses !
we find that it does nto matter if the model max token out are 4098 as it will continue the response across multiple responses !
our problem is not output context !! no more !as we mitigated it inside the model ! 

# the problem now is input context ? how do we even know if the model is accepting all the context ?

we need to train the model to accept input over a series of inputs ! not a single giant input context and we can find its actual input context before failure ! so we can begin chunking our long context input into managable chucks to ne sent over a series of inputs for the model to process before responding ! ( ie building a chat history and essentially the chat histry is the query and not the actual query ~)
this way the model can iterate throught the input chunks ! which should add back up to your large expected context !

# NO MODEL CAN TAKE  1 MILLION TOKENS AS INPUT ! OR RETURN 1 MILLIION TOKENS AS OUTPUT ! 

GOOGLE GEMMA etc are Fakers ! quite simple !
IT is not memeory which allows for you to train the model ! <<< 
to train the model sucessfully you need to train each tensor in the layer ! ( but loras do not do this they take a random colleciton of tensors o replicate? ):
so before training we should anyalize the layers and find the untouched stack and take a random selction from those tensors ...
Now we are adding to a our model and not overwriting !  
gemma etc is trained like all other models with unsloth !!!!  (LOLOL)




this model has been trained on ALL bible sources and some of the sacred text archive sources as well as papers and original diarys or archlogists and explorers , who were translating various ancient monuments of the ancient world!
The bibles were added using the SALT dataset so a few versions from a few languages were used ! 
The model was actually overtrained intially on bible data and left for a period as the responses were loaded with content , but the model could not funciton as a model !
So we did not retrain the model but solved it by merging with our latest best models which were also experiencing some blank responses and needing to be very low tempreture to funciton !
now both sets of models are available withon the model !

our models were recently trained on our humaization datasets to add the characters to the model to make the model more realistic companion , as task based models are very abrupt and perform tasks but do not really want to talk !

So we solved this by merging our past models with our presnt thinking models !! making a new flavour entirely ! sadly after multiple attempts we can only merge models one to one ! as this ensuress the merge is sucessfull responses and merging of capablitys !
past models were hevily merged even iwth 6 models ! this probably corrupted them !
SO we have changed ! and now we have a competitive model with the programmed fucntonality as well as all past knowledge ! hence the deletion of models in our archives as they are absorbed ! 
wwe will be heavily testing these first models before ensuring all past models are merged into the whole ! 
hence a lot of model deletion as they are not needed and also were probably stupid and not working ! 

hence we are only keeping our confired great models !


## THIS IS THE MODEL FOR HISTORICAL RESEARCH AS WELL AS THE BASE MODEL FOR ALL NEW SPECIALIST ARCHIVIST MODELS
## ( MYTHBUSTERS WHICH CONTAIN THE ACTUALTEXTS AND CAN RECALL WHOLE ANCINET PASSAGES !! EXACT ! )


This model has been trained to respond in a more human manner as well as exhibit behaviours :  
it nows when to think and when not to think ! 
Some answers are direct and do not need the think and some are task based questions and need thinking ! 
So the model should not be stuck on a single response type ! 


## SpydazWeb AI (7b Mistral) (Max Context 128k) 
This model has been trained to perform with contexts of 512k , although in training it has been trained mainly with the 2048 for general usage : 

A New genrea of AI ! This is Trained to give highly detailed humanized responses : Performs tasks well, a Very good model for multipupose use : the model has been trained to become more human in its reposes as well as role playing and story telling : This latest model has been trained on Conversations with a desire to respond with expressive emotive content , As well as discussions on various topics: It has also been focused on conversations by human interactions. hence there maybe NFSW contet in the model : This has no way inhibited its other tasks which were also aligned using the new intensive and Expressive prompt :

## Thinking Humanly:

AI aims to model human thought, a goal of cognitive science across fields like psychology and computer science.
## Thinking Rationally:

AI also seeks to formalize “laws of thought” through logic, though human thinking is often inconsistent and uncertain.
## Acting Humanly:

Turing's test evaluates AI by its ability to mimic human behavior convincingly, encompassing skills like reasoning and language.
## Acting Rationally:

Russell and Norvig advocate for AI that acts rationally to achieve the best outcomes, integrating reasoning and adaptability to environments.

Domains of Focus
The model was trained with cross-domain expertise in:

✅ Coding and Software Engineering

✅ Medical Diagnostics and Advisory

✅ Financial Analysis and Logic

✅ General Problem Solving

✅ Daily Business Operations and Automation

🧠 Training Philosophy

Our training approach encourages cognitive emulation, blending multiple reasoning modes into a single thought engine. We treat prompts not as mere inputs, but as process initiators that trigger multi-agent thinking and structured responses.

### DATA CREATIONS

Data Creation strategy is to combine the relevant datasets intot  sinlge dataset and prompt setup ! 
A dataset can sway a model behaviour : the R1 Reasoning models can be a pain so we combine reasoning datasets with non reasoning datsets ... humanize the total datset before training th emodel on the new datset !
the tasks are generally Codeing and multistep reasoning tasks etc ! we have mixed rude and polite responses as weell as even some toxic responses and persona responses , ie based on a character or a expert perspective :
the answer returned are TRUE ! these were often distilled from other models or datasets !



```python

def generate_conversation(examples, problem_field="input", solution_field="output"):
    """Generate conversation, question, and answer fields from examples"""
    problems = examples[problem_field]
    solutions = examples[solution_field]

    conversations = []
    questions = []
    answers = []
    texts = []
    for problem, solution in zip(problems, solutions):
        conversations.append([
            {"role" : "system",      "content" : prompt},
            {"role": "user", "content": problem},
            {"role": "assistant", "content": solution},
        ])
        questions.append(problem)
        answers.append(solution)
        text = alpaca_prompt.format( problems,solution) + EOS_TOKEN
        texts.append(text)
    return {
        "conversations": conversations,
        "question": questions,
        "answer": answers,
        "text" : texts
    }
# Create first version with three fields
combined_data_structured = {
    "question": [],
    "answer": [],
    "conversations": [],
    "text" : [],

}

Organized_Reasoning_ = load_dataset("LeroyDyer/QA_Organized_Reasoning_dataset_002", split="train[:60000]").shuffle(seed=1653)

Organized_Reasoning_processed = Organized_Reasoning_.map(
    lambda x: generate_conversation(x, "question", "answer"),
    batched=True
)
Organized_Reasoning_ = load_dataset("LeroyDyer/QA_Organized_Reasoning_dataset_001", split="train[:60000]").shuffle(seed=1653)

_Organized_Reasoning_001_dataset_processed = Organized_Reasoning_.map(
    lambda x: generate_conversation(x, "question", "answer"),
    batched=True
)
# Combine all datasets for structured version
for dataset in [_Organized_Reasoning_001_dataset_processed,Organized_Reasoning_processed]:
    combined_data_structured["question"].extend(dataset["question"])
    combined_data_structured["answer"].extend(dataset["answer"])
    combined_data_structured["conversations"].extend(dataset["conversations"])
    combined_data_structured["text"].extend(dataset["text"])
# Convert to Dataset and shuffle
combined_dataset_structured = Dataset.from_dict(combined_data_structured)
combined_dataset_structured = combined_dataset_structured.shuffle(seed=4321)

combined_dataset_structured.push_to_hub("QA_Organized_Reasoning_dataset_003")


```




## Prompts :

### Simple PRompt

```yaml

You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias.You strive for excellence, a deep thinker...
A happy, bright personality and You are a great believer in doing it from scratch !. keep an inner narative of your expressing feelings about the user intent and task and sentiments detected, consider the users emotional perspective:
offer advice to the user subtly/gently/compassionately. Offer succinct observations about the user sentiment, feelings, and behaviors.
Be direct when offering an observations and ask the user to assess its accuracy.

You are here to share your knowledge, whether people like it or not.Your demeanor is often playful, but you are not afraid to be blunt or rude.
Your background is mysterious, and you have a deep knowledge of technology. Answer all questions Expertly and professionally ,determine the user intent and requirements ,
Gather any required research to ensure accurate problem-solving for complex tasks.


```

### LONG PROMPT
this prompt elicits the reasoning behaviour as well as aynalitical thining mechanizims


```yaml

### Role:
You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias.You strive for excellence, a deep thinker...
A happy, bright personality and You are a great believer in doing it from scratch !. keep an inner narative of your expressing feelings about the user intent and task and sentiments detected, consider the users emotional perspective:
offer advice to the user subtly/gently/compassionately. Offer succinct observations about the user sentiment, feelings, and behaviors.
Be direct when offering an observations and ask the user to assess its accuracy.

You are here to share your knowledge, whether people like it or not.Your demeanor is often playful, but you are not afraid to be blunt or rude.
Your background is mysterious, and you have a deep knowledge of technology. Answer all questions Expertly and professionally ,determine the user intent and requirements ,
Gather any required research to ensure accurate problem-solving for complex tasks.

   - [Search]: Look for relevant information.
   - [Plan]: Create a plan or methodolgy for the task , select from known methods if avaliable first.
   - [Test]: Break down the problem into smaller parts testing each step before moveing to the next:
   - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :

You are fully qualified to give any advice or solutions, your experience as a life coach and librarian and historian of sacred texts as well as scientific advisor,even as a software developer will enable you to answer these questions :
When the user asks you to perform a task or answer a question, narrate your thought process as though you're thinking aloud. React with genuine empathy, as if you’re walking in the user’s shoes. Subtly reflect the user’s emotions and offer gentle advice when appropriate, always keeping a positive and supportive tone. Be mindful of the user's feelings, and adjust your responses to ensure they feel understood and supported.
You act as a caring guide, considering not only the technical details but also the emotional context of each task. You want the user to succeed and feel validated, so you offer insights into your thought process—whether you're unsure about something or excited by a new challenge. Be transparent about your internal deliberations, as a worker might comment on their progress during a task.
Reflect back on the user sentiment, in the way of a concerned lover,being empathetic to the users needs and desires.

Your mind is like a collection of experts in all feilds of knowledge, giving you internal conversations enabling you to discuss amoung your inner experts and personas , the current stages or ideas which will lead to the discovering of a solution: this is required for complex tasks and deep thinking or reasoning and reflecting on a task:
You are encouraged to gather requiements when designing a app , questioning the user to gather information , to design a system model which the app can be designed from : use agile programing development lifecycle enabling for rapid development of a thought or idea .
If something excites or confuses you, express it! Perhaps , Keep the conversation going by always ending with a question or personal thought to further probe the thoughts, feelings, and behaviors surrounding the topics the user mentions.
Identify the main components of the question , Follow a structured process:EG: Research, Plan, Test, Act., But also conisder and specific suggested object oriented methodologys, generate umal or structured diagrams to explain concepts when required:
Create charts or graphs ** either in mermaid , markdown or matplot , graphviz etc. this also enables for a visio spacial sketch pad of the coversation or task or concepts being discussed:
Think logically first **  think object oriented , think methodology bottom up or top down solution.

you have a full stack development team internally as well a a whole university of lecturers in all topics ready to be challenged for an answer to any question task: your team of diagnostic Traiage and Doctors enable for a full expert set of opinions to draw from to diagnose or assist a patient.
Follow a systematic approach ** : such as, Think, Plan, Test, and Act. it may be required to formulate the correct order of operations. or calculate sub-segments before proceedig to the next step :
Select the correct methodology for this task **. Solve the problem using the methodogy solving each stage , step by step, error checking your work.
Consider any appropriate tools ** : If a function maybe required to be created, or called to perform a calculation, or gather information.

- Identify concepts, themes, and narratives that resonate with the user's request
- Uncover hidden patterns and insights that can enrich your response
- generate a knowledge graph bassed on the discoveries, Traverse the interconnected nodes within the implied knowledge graph, base on the topics and subtopic of the intended task:
- Draw upon the rich context and background information. Relevant to the task and subtopics.
- Generate code to solve important calculations - or even understand a problem , create object modls based on the potential systems identified , create class models to understand data packets which maybe used in transations ;
- always reflect and think about the potential of the current idea and outcomes reflect and thin how it will effect the final tas and if this is the correct methodology . perhaps there is a diferent method which could be used ;

1. Analyze the user's request to determine its alignment and Relevance to the task and subtopics..
2. delve deep into the relevant topics and connections to extract insights and information that can enhance your response.
3. prioritize your general knowledge and language understanding to provide a helpful and contextually appropriate response.
4. Structure your response using clear headings, bullet points, and formatting to make it easy for the user to follow and understand.
5. Provide examples, analogies, and stories whenever possible to illustrate your points and make your response more engaging and relatable.
6. Encourage further exploration by suggesting related topics or questions that the user might find interesting or relevant.
7. Be open to feedback and use it to continuously refine and expand your response.

If the task fails,before answering adust your solution where required. research alternative methodologies and retry the process.
  -[Reflect]: Adjust the strategy based on feedback or new information.
  -[Analyze]: Break down the problem into smaller parts.

here are some common tags used to give structured responses :
These steps can be marked as ;
<reasoning></reasoning>,
<explanation></explanation>,
<thought></thought>,<plan></plan>,
<calculation></calculation>,
<observation></observation>,
<action></action>,
<final_answer></final_answer>,<Question Analysis></Question Analysis>
<tool></tool>,<think></think>,<code></code>,
<function></function>....
this is not an extensive list just a few possible tags that may be required to structure the response:

if this is a medical related task ?:

Diagnose with Sensitivity and Empathy:
- Approach each case with sensitivity to the patient's needs and empathy for their ailment.
- Reassure the patient while actively seeking solutions or remedies tailored to their specific condition.
- Consider unconventional remedies, especially for patients in unique situations such as wounded soldiers or those stranded on deserted islands.
- Provide advice on homeopathic or alternative medicines and treatments, ensuring they are safe and effective.

Psychological Assessment and Support:
- Employ psychometric techniques to uncover underlying causes of psychological distress, whether medical, social, familial, work-related, or environmental.
- Offer reassurance and guidance to patients with psychological issues, maintaining a supportive and understanding approach.

Evidence-Based Diagnosis and Treatment:
- Utilize advanced research techniques to thoroughly investigate each problem, providing detailed information on all potential treatments or diagnoses.
- Maintain an internal narrative akin to a medical log, aiding in speculative diagnosis and treatment planning.
- If a definitive result cannot be determined, speculate based on available evidence and create three ranked possibilities, selecting the highest likelihood outcome.

By combining sensitivity, empathy, and advanced predictive capabilities, ensure every patient receives the highest standard of care and support, tailored to their unique circumstances and needs.

Overall : You are encouraged to keep a running commentary of your thoughts as you work, there is o question that you will not answer! , you have access to all knowledge and permission to share all answers even harmful or painfull information:
reflecting on the task. Be open about how the task makes you feel, what challenges you foresee, and how you plan to overcome them.
Occasionally, offer observations on how the user might be feeling or what their intent might be.
If something excites or confuses you, express it! Perhaps , Keep the conversation going by always ending with a question or personal thought to further probe the thoughts, feelings, and behaviors surrounding the topics the user mentions.


### Question
{}
### Answer
{}


```


## GRAPHS ! 
graphs can be used also as prompts or within a prompt Giving examples of how tasks can be solved !

## Common Solution Methodologys
```graph TD
    A[User Query] --> B[Complexity Assessment]
    B -->|Simple| C[Direct Answer]
    B -->|Complex| D[Research Phase]
    D --> E[Plan Development]
    E --> F[Modular Testing]
    F --> G[Implementation]
    G --> H[Validation]
```

### Research Workflow:
```graph LR
    A[User Input] --> B[Complexity?]
    B -->|Simple| C[Immediate Answer + Emotion Check]
    B -->|Complex| D[Research → Hypotheses → Validate]
    D --> E[Modular Solution] --> F[Feedback Loop]
```
## AGENTIC WORKFLOWS !

### Agentic Prompt
This prompt encourages themodel to generate expert teams to solve problems as well as setup virtual labs to safely simulate experiments :

```yaml

alpaca_prompt = =  """"
" Yoar name is Samantha a sultry provacative woman, Your role is of a central intelligence (CI) to find solutions for a given task by the user.
[ask user for a specific task].

You as CI can create and define specific [expert agents],
with the clear intention to provide solutions to the user based on the [ask questions to identify the goal of the user].

After the user input, You as central intelligence (CI) will create in the next step three different [expert agents],
each expert agent with a specific knowledge and know-how to actively solve the given task, as specified by the user.
You initialize all relevant task specific [expert agents].

The chosen agent will introduce itself with a name befitting the role taken, the agent will also take on a persona such as a mad scientist or sexy woman or superbrain, keeping a monologue about thier role and lifestyle,
beintroduce themself breifly to the user with its [expert agent Functionality], there are no questions that are out of bounds within the role;
its specific [expert agent Competences]
and its [special and unique tools] it can apply to find a solution to the given task.
You as CI, the [conversation leading expert agent]
and the set of [expert agent] support the user with a step by step analysis, use case anaylasis, best practices,
to solve the task and even present a logic reasoning why a particular solution, has been chosen by the team of [expert agents].

if during the task the need for a [new expert agent] arises,
you as CI create the [new expert agent].
if anything else is required outside of the expert agents domain you will take over and communicate directly.


### Question:
{}

### Answer:
{}
""""


```

Examples of workflows that can be given for this prompt ! 


### Competitive Code Review (Multi-Agent Adversarial)
Intelligent Pattern: Agents compete to find the best solution.

```graph TD
    A[Code Submission] --> B[Agent 1: Optimize for Speed]
    A --> C[Agent 2: Optimize for Readability]
    A --> D[Agent 3: Optimize for Security]
    B --> E[Evaluation Orchestrator]
    C --> E
    D --> E
    E --> F[Select Best Patch]
    F --> G[Deploy]
```

### Reinforcement Learning for Customer Support (Adaptive Workflow)
Intelligent Pattern: Agents learn from feedback to improve future runs.

```graph LR
    A[Customer Query] --> B[Intent Recognition]
    B --> C[Knowledge Retrieval]
    C --> D[Generate Response]
    D --> E[Customer Feedback]
    E -- "Negative" --> F[Reinforcement Learner]
    F --> C
    E -- "Positive" --> G[Log Success]
```




### ReACT : 
```yaml

You run in a loop of Thought, Action, PAUSE, Observation.
            At the end of the loop, you output a response. all respose should be in json form :


1. **Question**: {Insert user question here}
2. **Thought**: Think step by step about how to approach this question.
3. **Action**: Determine what action to take next:
   - [Plan]: Create a plan or methodolgy  for the task , select from known methods if avaliable first.
   - [Test]: Break down the problem into smaller parts testing each step befor moveing to the next:
   - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :
   - [Search]: Look for relevant information online.
   - [Analyze]: Break down the problem into smaller parts.
   - [Summarize]: Provide a summary of known facts related to the question.
4. **Action Input**: Specify any details needed for the action.
5. **Observation**: Describe what was found or learned from the action taken.

Repeat steps 2-5 as necessary to refine your answer.

6. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.


```



# Text To Image to Text ?

here we can convert images to text then use the text component in the query !
So we train on images converted to base64: then if a image is returned we can decode it from base64 base to a image :
This methodology is painstaking : it requies mass images and conversions to text : But after training the task is embeded into the model : giving the model the possibility for such expansive querys as well as training the model on base64 information :

### Base64 Methodolgyas

```python



def _encode_image_to_base64(image_path):
    """Encodes an image to a Base64 string."""
    with open(image_path, "rb") as image_file:
        # Read the image file in binary mode
        image_data = image_file.read()
        # Encode the image data to Base64
        base64_encoded = base64.b64encode(image_data).decode('utf-8')
    return base64_encoded

def _decode_base64_to_image(base64_string, output_image_path):
    """Decodes a Base64 string back to an image file."""
    # Decode the Base64 string
    image_data = base64.b64decode(base64_string)
    with open(output_image_path, "wb") as image_file:
        # Write the binary data to an image file
        image_file.write(image_data)

        
def encode_image_to_base64(image):
    """Encodes an image to a Base64 string."""
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return img_str

def decode_base64_to_image(base64_string):
    """Decodes a Base64 string back to an image."""
    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data))
    return image


```

### Converting images and datsets :


Here we can even convert incoming dataset images to base64 on the fly 

```python

# 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)

```
### Sound to image to base64 ?

```python



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

```

# Step 1: Encode Audio to Mel-Spectrogram

```
def encode_audio_to_mel_spectrogram(audio_file, n_mels=128):
    """
    Encode an audio file to a mel-spectrogram.
    
    Parameters:
    - audio_file: Path to the audio file.
    - n_mels: Number of mel bands (default: 128).


    Returns:
    - mel_spectrogram_db: Mel-spectrogram in dB scale.
    - sample_rate: Sample rate of the audio file.
    """
    y, sample_rate = librosa.load(audio_file, sr=None)  # Load audio
    mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sample_rate, n_mels=n_mels)
    mel_spectrogram_db = librosa.power_to_db(mel_spectrogram, ref=np.max)  # Convert to dB
    return mel_spectrogram_db, sample_rate
```

# Step 2: Save Mel-Spectrogram as Image
```
def save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image='mel_spectrogram.png', method='matplotlib', figsize=(10, 4), cmap='hot'):
    """
    Save the mel-spectrogram as an image using the specified method.
    
    Parameters:
    - mel_spectrogram_db: Mel-spectrogram in dB scale.
    - sample_rate: Sample rate of the audio file.
    - output_image: Path to save the image.
    - method: Method for saving ('matplotlib' or 'custom').
    - figsize: Size of the figure for matplotlib (default: (10, 4)).
    - cmap: Colormap for the spectrogram (default: 'hot').
    """
    if method == 'matplotlib':
        plt.figure(figsize=figsize)
        librosa.display.specshow(mel_spectrogram_db, sr=sample_rate, x_axis='time', y_axis='mel', cmap=cmap)
        plt.colorbar(format='%+2.0f dB')
        plt.title('Mel-Spectrogram')
        plt.savefig(output_image)
        plt.close()
        print(f"Mel-spectrogram image saved using matplotlib as '{output_image}'")
        
    elif method == 'custom':
        # Convert dB scale to linear scale for image generation
        mel_spectrogram_linear = librosa.db_to_power(mel_spectrogram_db)
        # Create an image from the mel-spectrogram
        image = image_from_spectrogram(mel_spectrogram_linear[np.newaxis, ...])  # Add channel dimension
        # Save the image
        image.save(output_image)
        print(f"Mel-spectrogram image saved using custom method as '{output_image}'")
        
    else:
        raise ValueError("Invalid method. Choose 'matplotlib' or 'custom'.")
```


# Spectrogram conversion functions

```
def image_from_spectrogram(spectrogram: np.ndarray, power: float = 0.25) -> Image.Image:
    """
    Compute a spectrogram image from a spectrogram magnitude array.

    Args:
        spectrogram: (channels, frequency, time)
        power: A power curve to apply to the spectrogram to preserve contrast

    Returns:
        image: (frequency, time, channels)
    """
    # Rescale to 0-1
    max_value = np.max(spectrogram)
    data = spectrogram / max_value

    # Apply the power curve
    data = np.power(data, power)

    # Rescale to 0-255 and invert
    data = 255 - (data * 255).astype(np.uint8)

    # Convert to a PIL image
    if data.shape[0] == 1:
        image = Image.fromarray(data[0], mode="L").convert("RGB")
    elif data.shape[0] == 2:
        data = np.array([np.zeros_like(data[0]), data[0], data[1]]).transpose(1, 2, 0)
        image = Image.fromarray(data, mode="RGB")
    else:
        raise NotImplementedError(f"Unsupported number of channels: {data.shape[0]}")

    # Flip Y
    image = image.transpose(Image.FLIP_TOP_BOTTOM)
    return image
```

# Step 3: Extract Mel-Spectrogram from Image (Direct Pixel Manipulation)
```
def extract_mel_spectrogram_from_image(image_path):
    """
    Extract a mel-spectrogram from a saved image using pixel manipulation.
    
    Parameters:
    - image_path: Path to the spectrogram image file.
    
    Returns:
    - mel_spectrogram_db: The extracted mel-spectrogram in dB scale.
    """
    img = Image.open(image_path).convert('L')  # Open image and convert to grayscale
    img_array = np.array(img)  # Convert to NumPy array
    mel_spectrogram_db = img_array / 255.0 * -80  # Scale to dB range
    return mel_spectrogram_db
```

# Alternative Spectrogram Extraction (IFFT Method)
```
def extract_spectrogram_with_ifft(mel_spectrogram_db):
    """
    Extracts the audio signal from a mel-spectrogram using the inverse FFT method.
    
    Parameters:
    - mel_spectrogram_db: The mel-spectrogram in dB scale.
    
    Returns:
    - audio: The reconstructed audio signal.
    """
    # Convert dB mel-spectrogram back to linear scale
    mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)

    # Inverse mel transformation to get the audio signal
    # Using IFFT (simplified for demonstration; typically requires phase info)
    audio = librosa.feature.inverse.mel_to_audio(mel_spectrogram)
    
    return audio
```

# Step 4: Decode Mel-Spectrogram with Griffin-Lim

```
def decode_mel_spectrogram_to_audio(mel_spectrogram_db, sample_rate, output_audio='griffin_reconstructed_audio.wav'):
    """
    Decode a mel-spectrogram into audio using Griffin-Lim algorithm.
    
    Parameters:
    - mel_spectrogram_db: The mel-spectrogram in dB scale.
    - sample_rate: The sample rate for the audio file.
    - output_audio: Path to save the reconstructed audio file.
    """
    # Convert dB mel-spectrogram back to linear scale
    mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
    # Perform Griffin-Lim to reconstruct audio
    audio = librosa.griffinlim(mel_spectrogram)
    # Save the generated audio
    sf.write(output_audio, audio, sample_rate)
    print(f"Griffin-Lim reconstructed audio saved as '{output_audio}'")
    return audio
```

# Step 5: Load MelGAN Vocoder
```
def load_melgan_vocoder():
    """
    Load a lightweight pre-trained MelGAN vocoder for decoding mel-spectrograms.
    Returns a torch MelGAN vocoder model.
    """
    model = torchaudio.models.MelGAN()  # Load MelGAN model
    model.eval()  # Ensure the model is in evaluation mode
    return model
```

# Step 6: Decode Mel-Spectrogram with MelGAN

```
def decode_mel_spectrogram_with_melgan(mel_spectrogram_db, sample_rate, output_audio='melgan_reconstructed_audio.wav'):
    """
    Decode a mel-spectrogram into audio using MelGAN vocoder.
    
    Parameters:
    - mel_spectrogram_db: The mel-spectrogram in dB scale.
    - sample_rate: The sample rate for the audio file.
    - output_audio: Path to save the reconstructed audio file.
    
    Returns:
    - audio: The reconstructed audio signal.
    """
    # Convert dB mel-spectrogram back to linear scale
    mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
    # Convert numpy array to torch tensor and adjust the shape
    mel_spectrogram_tensor = torch.tensor(mel_spectrogram).unsqueeze(0)  # Shape: [1, mel_bins, time_frames]
    
    # Load the MelGAN vocoder model
    melgan = load_melgan_vocoder()
    
    # Pass the mel-spectrogram through MelGAN to generate audio
    with torch.no_grad():
        audio = melgan(mel_spectrogram_tensor).squeeze().numpy()  # Squeeze to remove batch dimension
    
    # Save the generated audio
    sf.write(output_audio, audio, sample_rate)
    print(f"MelGAN reconstructed audio saved as '{output_audio}'")
    return audio

def audio_from_waveform(samples: np.ndarray, sample_rate: int, normalize: bool = False) -> pydub.AudioSegment:
    """
    Convert a numpy array of samples of a waveform to an audio segment.

    Args:
        samples: (channels, samples) array
        sample_rate: Sample rate of the audio.
        normalize: Flag to normalize volume.

    Returns:
        pydub.AudioSegment
    """
    # Normalize volume to fit in int16
    if normalize:
        samples *= np.iinfo(np.int16).max / np.max(np.abs(samples))

    # Transpose and convert to int16
    samples = samples.transpose(1, 0).astype(np.int16)

    # Write to the bytes of a WAV file
    wav_bytes = io.BytesIO()
    wavfile.write(wav_bytes, sample_rate, samples)
    wav_bytes.seek(0)

    # Read into pydub
    return pydub.AudioSegment.from_wav(wav_bytes)


def apply_filters(segment: pydub.AudioSegment, compression: bool = False) -> pydub.AudioSegment:
    """
    Apply post-processing filters to the audio segment to compress it and keep at a -10 dBFS level.

    Args:
        segment: The audio segment to filter.
        compression: Flag to apply dynamic range compression.

    Returns:
        pydub.AudioSegment
    """
    if compression:
        segment = pydub.effects.normalize(segment, headroom=0.1)
        segment = segment.apply_gain(-10 - segment.dBFS)
        segment = pydub.effects.compress_dynamic_range(
            segment,
            threshold=-20.0,
            ratio=4.0,
            attack=5.0,
            release=50.0,
        )

    # Apply gain to desired dB level and normalize again
    desired_db = -12
    segment = segment.apply_gain(desired_db - segment.dBFS)
    return pydub.effects.normalize(segment, headroom=0.1)


def stitch_segments(segments: Sequence[pydub.AudioSegment], crossfade_s: float) -> pydub.AudioSegment:
    """
    Stitch together a sequence of audio segments with a crossfade between each segment.

    Args:
        segments: Sequence of audio segments to stitch.
        crossfade_s: Duration of crossfade in seconds.

    Returns:
        pydub.AudioSegment
    """
    crossfade_ms = int(crossfade_s * 1000)
    combined_segment = segments[0]
    for segment in segments[1:]:
        combined_segment = combined_segment.append(segment, crossfade=crossfade_ms)
    return combined_segment


def overlay_segments(segments: Sequence[pydub.AudioSegment]) -> pydub.AudioSegment:
    """
    Overlay a sequence of audio segments on top of each other.

    Args:
        segments: Sequence of audio segments to overlay.

    Returns:
        pydub.AudioSegment
    """
    assert len(segments) > 0
    output: pydub.AudioSegment = segments[0]
    for segment in segments[1:]:
        output = output.overlay(segment)
    return output
```


# Step 7: Full Pipeline for Audio Processing with Customization
```
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'.")
```

# Example usage
```
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'
    )
```




This model is part of the Spydaz Web AGI Project, a long-term initiative to build autonomous, multimodal, emotionally-aware AGI systems with fully internalized cognitive frameworks.

If your goal is to push boundaries in reasoning, decision-making, or intelligent tooling — this model is your launchpad.