---

name: speechbrain description: "Guides SpeechBrain package use, pretrained inference, recipes, audio pipelines, model components, metrics, and repo maintenance for speech and conversational-AI workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SpeechBrain repo skill

Use this skill when a task involves the SpeechBrain Python package, SpeechBrain recipes, pretrained SpeechBrain models, HyperPyYAML experiment files, audio/data pipelines, speech preprocessing, speech metrics, or maintaining the SpeechBrain repository.

SpeechBrain is a PyTorch-based speech and conversational-AI toolkit. It exposes both a library (speechbrain) and a large recipe collection. Start here to choose the right focused route, then read only the linked sub-skill/reference needed for the task.

Quick install and import check

For ordinary package use:

pip install speechbrain
python - <<'PY'
import speechbrain as sb
print(sb.__version__)
print(sb.Brain)
PY

For local recipe development from a source checkout, install the repository and its dependencies in an isolated environment:

pip install -r requirements.txt
pip install --editable .
python -m pip check

Run the bundled environment smoke script when diagnosing setup or optional dependency issues:

python scripts/check_speechbrain_install.py --json

Read references/installation-and-environment.md for dependency, Python/Torch, audio backend, Hugging Face, and CPU/GPU notes. Read references/troubleshooting.md for cross-cutting install, import, audio, network/cache, and runtime failures.

Route map

User task	Read
Use pretrained models for ASR, speaker verification, classification, enhancement, separation, VAD, TTS, G2P, or local/Hugging Face model loading.	sub-skills/pretrained-inference/SKILL.md
Run or adapt a recipe, create a new recipe from templates, debug HyperPyYAML overrides, use Brain, or launch CPU/GPU/DDP training.	sub-skills/recipe-training/SKILL.md
Load/save audio, build DynamicItemDataset/DataPipeline, train tokenizers, create audio features, apply augmentation, beamforming, or vocal-feature preprocessing.	sub-skills/data-audio-pipelines/SKILL.md
Choose neural blocks, decoders, losses, metric stats, WER scoring, checkpoint recovery, Pretrainer, streaming helpers, or component-level tests.	sub-skills/components-metrics/SKILL.md
Maintain the repository: focused tests, recipe CSV consistency, docs generation, performance tables, lint/pre-commit, or contributor review checks.	sub-skills/repo-maintenance/SKILL.md

Working model

• SpeechBrain experiments are usually a Python script plus a HyperPyYAML file: python train.py hparams.yaml --override value.
• speechbrain.Brain owns train/valid/test loops; recipes subclass it and override forward/objective hooks.
• Pretrained inference classes inherit speechbrain.inference.interfaces.Pretrained and are commonly loaded with Class.from_hparams(source=..., savedir=..., run_opts={...}).
• Audio tensors generally follow batch-time-channel conventions. Mono waveforms are often (batch, time); multi-channel waveforms use an extra channel dimension.
• CPU is enough for imports, small fixtures, and many debug examples. Full recipe training and profiling often need CUDA or another accelerator; do not treat a CPU smoke check as proof of GPU throughput.

Evidence, provenance, and routing metadata

• Read references/repo-provenance.md before checking staleness or refreshing this skill.
• references/repo-routing-metadata.json contains structured router metadata for managed repo-skill import tooling.
• This runtime skill is self-contained. It does not require opening the original SpeechBrain checkout; source-relative evidence paths appear only in provenance and review artifacts.