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Glossary

This glossary explains key technical terms used throughout the guide.

This glossary explains key technical terms used throughout the guides to help newcomers understand the terminology:

  • ASR (Automatic Speech Recognition): Technology that converts spoken language into written text; used for transcribing audio data.
  • Batch Size: The number of training examples processed together in one forward/backward pass; affects training speed and memory usage.
  • Checkpoint: A saved snapshot of a model's weights during or after training, allowing you to resume training or use the model for inference.
  • CUDA: NVIDIA's parallel computing platform that enables GPU acceleration for deep learning tasks.
  • dBFS (Decibels relative to Full Scale): A unit of measurement for audio levels in digital systems, where 0 dBFS represents the maximum possible level.
  • Diffusion Models: A class of generative models that gradually add and then remove noise from data; some recent TTS systems use this approach.
  • FFT (Fast Fourier Transform): An algorithm that converts time-domain signals to frequency-domain representations; fundamental for audio processing.
  • Fine-tuning: The process of taking a pre-trained model and further training it on a smaller, specific dataset to adapt it to a new voice or language.
  • LUFS (Loudness Units relative to Full Scale): A standardized measurement of perceived loudness, more representative of human hearing than peak measurements.
  • Manifest File: A text file that lists audio files and their corresponding transcriptions, used to tell the training script where to find the data.
  • Mel Spectrogram: A visual representation of audio that approximates human auditory perception by using the mel scale; commonly used as an intermediate representation in TTS systems.
  • Overfitting: When a model learns the training data too well, including its noise and outliers, resulting in poor performance on new data.
  • Sampling Rate: The number of audio samples per second (measured in Hz); higher rates capture more audio detail but require more storage and processing power.
  • STFT (Short-Time Fourier Transform): A technique that determines the frequency content of local sections of a signal as it changes over time.
  • TTS (Text-to-Speech): Technology that converts written text into spoken voice output.
  • Validation Loss: A metric that measures the error of a model on a validation dataset (data not used for training); helps detect overfitting.
  • VRAM (Video RAM): Memory on a graphics card; deep learning models and their intermediate calculations are stored here during training.
  • Vocoder: A component in some TTS systems that converts acoustic features (like mel spectrograms) into waveforms (actual audio).