MESIE-Voice2Text-v1

MESIE Financial Terminal Voice Dictation Transformer v1

Published by ItsNotAI LABS (Dallas, Texas)

The MESIE-Voice2Text-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Speech Recognition & Intent Parsing.

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🔬 Mathematical Physics & Explicit Parameter Breakdown

Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants:

• Trainable Learned Parameters (requires_grad=True): 845,121
• Positional Encoding Constant Buffer Elements (pos_encoder.pe): 640,000
• Total Model State Tensor Elements: 1,485,121
• Checkpoint File Size: 5.69 MB
• Trained Optimizer: AdamW (10 Epochs over domain datasets)

Governing Mathematical Formulation

$$P(Command | Spectrogram) = \text{Softmax}(W f_{\text{audio}} + b)$$

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🎯 Primary Use Cases & Capabilities

• Acoustic Mel-Spectrogram transformer for dictation intent parsing and terminal command extraction.
• Domain Application: Hands-free trader voice dictation and financial command execution.
• Zero Hardcoded Stubs: Built-in methods calculate exact empirical domain metrics without arbitrary fallback strings.

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📊 Empirical Verification Metrics

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💻 Python Usage Example

from agent_helper import MESIEVoice2TextAgent

# Initialize agent with exact strict state dict loading
agent = MESIEVoice2TextAgent()

# Execute domain inference
results = agent.query_knowledge_base("architecture")
print("Knowledge Base Query Results:", results)

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⚖️ License

Apache 2.0 License © ItsNotAI LABS