LeroyDyer/_Spydaz_Web_SCIENCE_OFFICER_

Winners create more winners, while losers do the opposite.

Success is a game of winners.

— # Leroy Dyer (1972-Present)

The Human AI .

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.

Prompts :

ReACT :

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

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

# 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 ?

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.