copilot.. explain.
Frequency-Adaptive Momentum optimizer with parameter-specific handlers. + FAMScheduler. Work in progress.
This optimizer is specifically for ASR type models but works well without the FAM which can be controlled (turned on) by step count. fam_start_step=100
An experimental approach specifically designed for speech recognition tasks, FAM adapts momentum based on the frequency characteristics of gradient updates.
- Speech signals possess an inherent frequency structure, with different parts of the model responding to various frequency bands. This frequency structure remains preserved, albeit transformed, when converted to log-mel spectrograms, with model parameters adapting to capture this structure.
- The Chain of Frequency Information: Original Audio → Log-Mel Spectrogram → Encoder Parameters → Gradient Updates.
- Empirical observations reveal that transformer-based speech models develop:
- Lower encoder layers with filters responsive to specific frequency bands in the mel spectrogram.
- Attention heads tracking particular acoustic patterns over time.
- A hierarchical representation from acoustic features to phonetic units to words.
- FAM aims to integrate a momentum scheme that adapts based on the "frequency signature" of gradient updates.
FAM acknowledges the frequency structure within the optimization process itself, recognizing that:
- Gradient Frequencies Matter: The Fourier transform of gradient updates reveals patterns linked to the model's current learning phase.
- Different Parameters Process Different Bands: Similar to how our ears have frequency-specific receptors, different parts of the model specialize in various acoustic frequencies.
- Temporal Structure in Learning: Speech learning progresses through stages - from basic acoustics to phonetic patterns to linguistic structures.
By applying distinct momentum factors to different frequency bands in parameter space, FAM provides the optimizer with domain-specific audio information that it otherwise wouldn't have.
FAMOptimizer (Frequency-Adaptive Momentum Optimizer) is a state-of-the-art optimization algorithm, specifically designed for models that process sound, such as Automatic Speech Recognition (ASR) systems. Its frequency-adaptive approach makes it uniquely suited for handling the challenges prevalent in audio-based models, where parameter updates often vary across frequency domains.
This optimizer is particularly effective in training neural networks for tasks involving sound and speech, leveraging frequency-domain insights to dynamically adjust momentum and learning rates. FAMOptimizer enables robust, efficient, and stable training for niche audio-related applications.
- Tailored for Audio Models: Designed with ASR and sound-processing models in mind, addressing their unique optimization challenges.
- Frequency-Adaptive Momentum: Dynamically adjusts momentum for each parameter based on its update frequency, ensuring smoother and more efficient convergence.
- Parameter-Specific Handlers: Supports fine-grained control over parameter groups, enabling tailored learning strategies for different parts of an audio model.
- Enhanced Stability: Reduces oscillations and overshooting, which are common in non-convex optimization problems, especially in sound-related tasks.
- Broad Compatibility: Integrates seamlessly with popular machine learning frameworks and audio-processing pipelines.
To use FAMOptimizer in your project, you can install it via pip:
pip install FAMOptimizerOr clone this repository for the latest updates:
git clone https://github.com/sine2pi/FAMOptimizer.git
cd FAMOptimizerHere’s a quick example to get started with FAMOptimizer in an ASR or sound-related model:
import torch
from FAMOptimizer import FAMOptimizer
# Define your ASR or sound-processing model
model = MyASRModel() # Replace with your model
# Define your loss function
criterion = torch.nn.CTCLoss() # Example: CTC loss for ASR models
# Initialize the optimizer
optimizer = FAMOptimizer(model.parameters(), lr=0.01, beta=0.9)
# Training loop
for epoch in range(num_epochs):
for audio_inputs, targets in dataloader:
optimizer.zero_grad()
outputs = model(audio_inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()FAMOptimizer introduces an innovative approach to optimization by adapting momentum and learning rates based on the frequency of parameter updates. This is especially beneficial for sound-related tasks, where frequency-domain characteristics play a critical role:
- Frequency Analysis: Tracks parameter update patterns to identify high- and low-frequency components.
- Dynamic Momentum Adjustment: Modulates momentum values to stabilize updates for high-frequency components while accelerating low-frequency ones, aligning perfectly with the needs of audio models.
- Parameter-Specific Control: Enables unique optimization strategies for different parameter groups, allowing for precise fine-tuning in complex ASR architectures.
FAMOptimizer excels in scenarios involving sound and speech processing, including:
- Automatic Speech Recognition (ASR): Handles the unique frequency characteristics of speech signals, improving convergence and accuracy.
- Audio Classification: Enhances performance on tasks such as music genre classification or sound event detection.
- Speech Synthesis: Stabilizes and accelerates training for models like Tacotron or WaveNet.
- General Sound Processing: Ideal for reinforcement learning models or neural networks operating on audio signals.
FAMOptimizer provides several hyperparameters for customization:
lr(float): Base learning rate. Default:0.001beta(float): Frequency-adaptive momentum factor. Default:0.9weight_decay(float): Weight decay (L2 penalty). Default:0.0eps(float): Small value to prevent division by zero. Default:1e-8
These hyperparameters can be adjusted to suit the specific requirements of your ASR or sound-processing model.
FAMOptimizer supports advanced configurations for parameter grouping and custom update rules. For example, you can define parameter-specific learning rates and momentum as follows:
optimizer = FAMOptimizer([
{'params': model.encoder.parameters(), 'lr': 0.01, 'beta': 0.8}, # Encoder for ASR model
{'params': model.decoder.parameters(), 'lr': 0.001, 'beta': 0.9} # Decoder for ASR model
])FAMOptimizer has been benchmarked against popular optimizers like Adam, SGD, and RMSProp in sound-related tasks. Below are highlights from ASR and audio-processing benchmarks:
- ASR on LibriSpeech Dataset: Achieved faster convergence and higher word error rate (WER) improvements compared to Adam and SGD optimizers.
- Audio Event Detection: Demonstrated superior stability and performance on datasets like UrbanSound8K.
- Speech Synthesis: Outperformed standard optimizers in training Tacotron-based TTS systems, reducing training time significantly.
We are actively working on adding new features and improvements, including:
- Support for distributed training in audio models.
- Integration with additional audio-specific machine learning frameworks.
- Advanced visualization tools for frequency analysis in sound data.
We welcome contributions! If you'd like to contribute to FAMOptimizer, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bugfix.
- Submit a pull request with a detailed description of your changes.
For more information, see our Contributing Guidelines.
This project is licensed under the MIT License. See the LICENSE file for details.
This project was inspired by recent advancements in frequency-domain optimization techniques and aims to bring these innovations to the niche field of sound and speech processing. Special thanks to all contributors and researchers whose work has made this project possible.
For questions, feedback, or support, please reach out to:
- Author: sine2pi
- GitHub: sine2pi/FAMOptimizer