AI/ML Audio Source Separation -- 2
Budget: $250 – $750 USD
I’m looking for an AI/ML expert to build an ultra-fast model for vocal–instrument separation from WAV audio files. The goal is to achieve state-of-the-art speed and quality, outperforming existing models in both latency and accuracy.
Requirements:
• Strong experience with deep learning frameworks (e.g. PyTorch, TensorFlow).
• Solid background in audio signal processing and spectral representations (STFT, mel-spectrograms, etc.).
• Hands-on experience with source separation models (e.g. Demucs, Spleeter, ConvTasNet, or similar).
• Experience optimizing models for real-time or near real-time performance (GPU/CPU optimization, quantization, batching, etc.) is a big plus.
Please provide relevant work samples.
Requirements:
• Strong experience with deep learning frameworks (e.g. PyTorch, TensorFlow).
• Solid background in audio signal processing and spectral representations (STFT, mel-spectrograms, etc.).
• Hands-on experience with source separation models (e.g. Demucs, Spleeter, ConvTasNet, or similar).
• Experience optimizing models for real-time or near real-time performance (GPU/CPU optimization, quantization, batching, etc.) is a big plus.
Please provide relevant work samples.