.. _key-concepts: Key Concepts in Speech AI ========================= This page introduces the fundamental concepts you'll encounter when working with speech models in NeMo. No prior NeMo experience is required — we start from the basics of audio and work up to how NeMo structures its models. Audio Conventions in NeMo ------------------------- **Sampling rate** — ASR models often use **16 kHz**; TTS and audio processing models may use higher rates (e.g. 22.05 kHz, 44.1 kHz). Check each model's or preprocessor's config for the expected sample rate. **Channels** — Most models use mono input, but some support **multi-channel** audio (e.g. for spatial or multi-mic setups). See the model and preprocessor documentation for your use case. **Preprocessing** — NeMo models typically include a **preprocessor** that converts waveform input into features (e.g. mel-spectrogram). For most setups, you should provide audio that already matches the model's expected **sample rate** and **channel layout** (often mono); automatic resampling or stereo→mono is not guaranteed and depends on the collection, dataset, and preprocessor config. Check the model and preprocessor documentation for your use case. **Mel-spectrogram** — For models that use it, the preprocessor turns raw waveform into mel-spectrogram features; this is handled inside the model, not as a separate offline step. Speech AI Tasks --------------- NeMo supports several speech AI tasks, each solving a different problem: .. list-table:: :widths: 20 40 40 :header-rows: 1 * - Task - What it does - Example use case * - **ASR** (Automatic Speech Recognition) - Converts spoken audio to text - Transcribing meetings, voice interfaces * - **TTS** (Text-to-Speech) - Generates natural speech from text - Audiobooks, voice interfaces * - **Speaker Diarization** - Determines "who spoke when" - Multi-speaker segmentation and transcription * - **Speaker Recognition** - Identifies or verifies a speaker's identity - Voice authentication, speaker search * - **Speech Enhancement** - Improves audio quality (removes noise) - Preprocessing noisy recordings * - **SpeechLM** - Augments LLMs with audio understanding - Audio-aware agents, speech translation, reasoning about audio Encoder Architectures --------------------- The *encoder* converts audio features into a sequence of high-level representations: **Transformer** The standard encoder from `Vaswani et al. (2017) `_ — stacked self-attention and feed-forward layers with no convolutions. Used in NeMo as an encoder or decoder in encoder-decoder models (e.g. Canary). **Conformer** The original architecture from `Gulati et al. (2020) `_ that combines self-attention with convolutions for both global and local patterns. **FastConformer** A faster variant of Conformer (`Rekesh et al. (2023) `_) with 8× subsampling and optimized attention. NeMo's default choice for ASR; recommended for new projects. How NeMo Models Work --------------------- Every NeMo model wraps these components into a single, cohesive unit: .. raw:: html

Overview of NeMo Speech ======================== NeMo models are PyTorch modules that also integrate with `PyTorch Lightning `__ for training and `Hydra `__ + `OmegaConf `__ for configuration. Configuration with YAML ------------------------ NeMo experiments are configured with YAML files. A typical config has three main sections: .. code-block:: yaml model: # Model architecture, data, loss, optimizer encoder: _target_: nemo.collections.asr.modules.ConformerEncoder feat_in: 80 n_layers: 17 ... train_ds: manifest_filepath: /path/to/train_manifest.json batch_size: 32 optim: name: adamw lr: 0.001 trainer: # PyTorch Lightning trainer settings devices: 4 accelerator: gpu max_steps: 100000 precision: bf16-mixed exp_manager: # Experiment logging and checkpointing exp_dir: /path/to/experiments name: my_asr_experiment You can override any value from the command line: .. code-block:: bash python train_script.py \ model.optim.lr=0.0005 \ model.train_ds.manifest_filepath=/data/train.json \ trainer.devices=8 Manifest Files -------------- NeMo uses **manifest files** (JSONL format) to describe datasets. Each line is one training example: .. code-block:: json {"audio_filepath": "/data/audio/001.wav", "text": "hello world", "duration": 2.5} {"audio_filepath": "/data/audio/002.wav", "text": "how are you", "duration": 1.8} Key fields: - ``audio_filepath`` — path to the audio file - ``text`` — the transcript (for ASR) or input text (for TTS) - ``duration`` — audio duration in seconds See :doc:`../asr/datasets` for details on preparing datasets. Model Checkpoints ----------------- NeMo models are saved as ``.nemo`` files — tar archives containing model weights, configuration, and tokenizer files. You can load models in two ways: .. code-block:: python # From a pretrained checkpoint (downloads from HuggingFace/NGC) model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2") # From a local .nemo file model = nemo_asr.models.ASRModel.restore_from("path/to/model.nemo") See :doc:`../checkpoints/intro` for more details on checkpoint formats.