| .. _key-concepts: |
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| Key Concepts in Speech AI |
| ========================= |
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| 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. |
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| Audio Conventions in NeMo |
| ------------------------- |
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| **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. |
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| **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. |
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| **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. |
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| **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. |
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| Speech AI Tasks |
| --------------- |
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| NeMo supports several speech AI tasks, each solving a different problem: |
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| .. list-table:: |
| :widths: 20 40 40 |
| :header-rows: 1 |
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| * - 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 |
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| Encoder Architectures |
| --------------------- |
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| The *encoder* converts audio features into a sequence of high-level representations: |
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| **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). |
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| **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. |
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| **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. |
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| How NeMo Models Work |
| --------------------- |
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| Every NeMo model wraps these components into a single, cohesive unit: |
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| .. raw:: html |
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| <div style="margin: 24px 0; overflow-x: auto;"> |
| <svg viewBox="0 0 820 130" xmlns="http://www.w3.org/2000/svg" style="max-width:820px; width:100%; height:auto; font-family:'NVIDIA Sans',sans-serif;"> |
| <defs> |
| <marker id="arrow" viewBox="0 0 10 10" refX="10" refY="5" markerWidth="8" markerHeight="8" orient="auto"><path d="M0,0 L10,5 L0,10 z" fill="#76b900"/></marker> |
| </defs> |
| <!-- Preprocessor --> |
| <rect x="0" y="20" width="140" height="70" rx="8" fill="#76b900" opacity="0.15" stroke="#76b900" stroke-width="2"/> |
| <text x="70" y="48" text-anchor="middle" font-weight="700" font-size="13" fill="#333">Preprocessor</text> |
| <text x="70" y="66" text-anchor="middle" font-size="10" fill="#555">Audio → Mel-spectrogram</text> |
| <!-- Arrow 1 --> |
| <line x1="140" y1="55" x2="170" y2="55" stroke="#76b900" stroke-width="2" marker-end="url(#arrow)"/> |
| <!-- Encoder --> |
| <rect x="170" y="20" width="140" height="70" rx="8" fill="#76b900" opacity="0.15" stroke="#76b900" stroke-width="2"/> |
| <text x="240" y="48" text-anchor="middle" font-weight="700" font-size="13" fill="#333">Encoder</text> |
| <text x="240" y="66" text-anchor="middle" font-size="10" fill="#555">Features → Hidden repr.</text> |
| <!-- Arrow 2 --> |
| <line x1="310" y1="55" x2="340" y2="55" stroke="#76b900" stroke-width="2" marker-end="url(#arrow)"/> |
| <!-- Decoder --> |
| <rect x="340" y="20" width="140" height="70" rx="8" fill="#76b900" opacity="0.15" stroke="#76b900" stroke-width="2"/> |
| <text x="410" y="48" text-anchor="middle" font-weight="700" font-size="13" fill="#333">Decoder</text> |
| <text x="410" y="66" text-anchor="middle" font-size="10" fill="#555">Hidden repr. → Output</text> |
| <!-- Arrow 3 --> |
| <line x1="480" y1="55" x2="510" y2="55" stroke="#76b900" stroke-width="2" marker-end="url(#arrow)"/> |
| <!-- Loss --> |
| <rect x="510" y="20" width="140" height="70" rx="8" fill="#76b900" opacity="0.15" stroke="#76b900" stroke-width="2"/> |
| <text x="580" y="48" text-anchor="middle" font-weight="700" font-size="13" fill="#333">Loss Function</text> |
| <text x="580" y="66" text-anchor="middle" font-size="10" fill="#555">Measures quality</text> |
| <!-- Arrow 4 --> |
| <line x1="650" y1="55" x2="680" y2="55" stroke="#76b900" stroke-width="2" marker-end="url(#arrow)"/> |
| <!-- Optimizer --> |
| <rect x="680" y="20" width="140" height="70" rx="8" fill="#76b900" opacity="0.15" stroke="#76b900" stroke-width="2"/> |
| <text x="750" y="48" text-anchor="middle" font-weight="700" font-size="13" fill="#333">Optimizer</text> |
| <text x="750" y="66" text-anchor="middle" font-size="10" fill="#555">Updates weights</text> |
| </svg> |
| </div> |
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| Overview of NeMo Speech |
| ======================== |
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| 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. |
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| Configuration with YAML |
| ------------------------ |
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| NeMo experiments are configured with YAML files. A typical config has three main sections: |
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| .. code-block:: yaml |
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| 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 |
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| trainer: |
| # PyTorch Lightning trainer settings |
| devices: 4 |
| accelerator: gpu |
| max_steps: 100000 |
| precision: bf16-mixed |
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| exp_manager: |
| # Experiment logging and checkpointing |
| exp_dir: /path/to/experiments |
| name: my_asr_experiment |
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| You can override any value from the command line: |
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| .. code-block:: bash |
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| python train_script.py \ |
| model.optim.lr=0.0005 \ |
| model.train_ds.manifest_filepath=/data/train.json \ |
| trainer.devices=8 |
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| Manifest Files |
| -------------- |
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| NeMo uses **manifest files** (JSONL format) to describe datasets. Each line is one training example: |
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| .. code-block:: json |
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| {"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} |
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| Key fields: |
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| - ``audio_filepath`` — path to the audio file |
| - ``text`` — the transcript (for ASR) or input text (for TTS) |
| - ``duration`` — audio duration in seconds |
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| See :doc:`../asr/datasets` for details on preparing datasets. |
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| Model Checkpoints |
| ----------------- |
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| NeMo models are saved as ``.nemo`` files — tar archives containing model weights, configuration, and tokenizer files. You can load models in two ways: |
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| .. code-block:: python |
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| # From a pretrained checkpoint (downloads from HuggingFace/NGC) |
| model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2") |
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| # From a local .nemo file |
| model = nemo_asr.models.ASRModel.restore_from("path/to/model.nemo") |
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| See :doc:`../checkpoints/intro` for more details on checkpoint formats. |
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