---

name: pretrained-inference description: "Guides SpeechBrain pretrained inference interfaces, Hugging Face or local model loading, audio normalization, G2P/text inference, profiling, and model-cache troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SpeechBrain pretrained inference

Use this sub-skill when the user wants to run or debug SpeechBrain pretrained models, local model folders, Hugging Face sources, direct inference APIs, or model-cache/download behavior.

First questions to resolve

1. Which task family: ASR, classifier, speaker verification, enhancement, separation, VAD, TTS/vocoder, G2P/text, or custom interface?
2. Is the model source a Hugging Face repo id, local directory, URL, or trusted custom foreign_class package?
3. Is network access allowed, and should the model revision/cache be pinned?
4. Is the target backend CPU or CUDA, and is throughput/profiling part of the task?
5. Is the input a file path, already-loaded waveform tensor, batch of waveforms, or text string?

Common route

1. Read references/inference-interfaces.md to choose the class and method.
2. Read references/workflows.md for standard loading, audio, batch, local/offline, and custom-model flows.
3. For G2P or text generation workflows, read references/text-and-g2p.md.
4. For throughput profiling, read references/profiling.md before running any benchmark.
5. For errors, read references/troubleshooting.md.
6. Run scripts/pretrained_interface_smoke.py when you only need to verify class availability and signatures without downloading models.

Safe minimal pattern

from speechbrain.inference.ASR import EncoderDecoderASR

asr = EncoderDecoderASR.from_hparams(
    source="speechbrain/asr-conformer-transformerlm-librispeech",
    savedir="pretrained_models/asr-conformer-transformerlm-librispeech",
    run_opts={"device": "cpu"},
)
text = asr.transcribe_file("audio.wav")

For a CUDA run, install a CUDA-capable Torch/Torchaudio pair first and set run_opts={"device": "cuda"} or an explicit device such as cuda:0. Validate CUDA separately; a CPU import check is not a CUDA verification.

Trust and self-containment notes

• from_hparams loads HyperPyYAML and checkpoint files. Treat untrusted hparams as executable configuration.
• foreign_class fetches and executes external Python code. Use it only for highly trusted sources.
• Future agents should not depend on original repository examples or tools. Use the bundled references and scripts in this sub-skill.