Human Voice Classification Model
Budget: $10 – $30 USD
I have a fully-labeled collection of WAV recordings and need a compact solution that can 1) tell whether an input clip is a human voice and 2) identify which speaker from the dataset is talking.
My preference is to work with MFCC features fed into a Convolutional Neural Network; if you believe another architecture will reliably hit the 90 % accuracy mark, feel free to explain why.
Scope of work
• Write a clean training script that reads the WAV files, extracts MFCCs, and trains the model.
• Validate on a held-out set and document the accuracy; target ≥ 90 %.
• Provide the final weights, an inference script that accepts a single audio file and prints the predicted speaker name (or “unknown” if outside the set), and a short README with setup and run commands.
Please keep the code lightweight—TensorFlow, PyTorch, or similar mainstream libraries are fine—and emphasize reproducibility so I can retrain with future data.
My preference is to work with MFCC features fed into a Convolutional Neural Network; if you believe another architecture will reliably hit the 90 % accuracy mark, feel free to explain why.
Scope of work
• Write a clean training script that reads the WAV files, extracts MFCCs, and trains the model.
• Validate on a held-out set and document the accuracy; target ≥ 90 %.
• Provide the final weights, an inference script that accepts a single audio file and prints the predicted speaker name (or “unknown” if outside the set), and a short README with setup and run commands.
Please keep the code lightweight—TensorFlow, PyTorch, or similar mainstream libraries are fine—and emphasize reproducibility so I can retrain with future data.