docs/source/starthere/key_concepts.rst

.. _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) <https://arxiv.org/abs/1706.03762>`_ — 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) <https://arxiv.org/abs/2005.08100>`_ that combines self-attention with convolutions for both global and local patterns.

**FastConformer**
   A faster variant of Conformer (`Rekesh et al. (2023) <https://arxiv.org/abs/2305.05084>`_) 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:

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Overview of NeMo Speech
========================

NeMo models are PyTorch modules that also integrate with `PyTorch Lightning <https://lightning.ai/>`__ for training and `Hydra <https://hydra.cc/>`__ + `OmegaConf <https://omegaconf.readthedocs.io/>`__ 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.