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name: pretrained-inference description: "Load Asteroid pretrained checkpoints and separate audio tensors, arrays, or files." metadata: disco-role: operating disable-model-invocation: true license: MIT

Pretrained inference

Use this sub-skill when the user wants to apply an existing Asteroid model rather than build or train one.

Typical triggers

• asteroid-infer
• BaseModel.from_pretrained(...)
• model.separate(...), separate(...), numpy_separate(...), torch_separate(...), or file_separate(...)
• Torch Hub, Hugging Face IDs, Zenodo URLs, or local model.pth files
• available_models() / show_available_models()
• long-file chunking with LambdaOverlapAdd

What to do first

1. Identify the checkpoint source:
   • local serialized dict or model.pth
   • public URL
   • Hugging Face model ID
   • Torch Hub usage
2. Decide whether the user wants:
   • a tensor/numpy result
   • files written next to the inputs
   • a temporary smoke check only
3. Choose the device explicitly when ambiguity matters.
   • asteroid-infer defaults to CUDA if available, otherwise CPU.
   • Long file inference can optionally be wrapped in LambdaOverlapAdd.

Standard workflow

• Read references/pretrained-models.md for the model-loading matrix and cache behavior.
• Read references/cli-reference.md for the public CLI flags and file-handling rules.
• Read references/troubleshooting.md when the model source, file type, or device choice looks suspicious.
• Use scripts/smoke_pretrained_inference.py for a tiny local round-trip on tensors and files.

Common path choices

• Local checkpoint: call BaseModel.from_pretrained(path_or_conf).
• Public model name: resolve through BaseModel.from_pretrained(...) or Torch Hub.
• Audio files: call model.separate(path) or the CLI with --files.
• Long files: wrap the model in LambdaOverlapAdd before separating.

Output expectations

• Tensor or numpy inputs return separated arrays with a source dimension.
• File inputs create *_estN.wav outputs unless an output directory is set.
• The CLI should print or save separated waveforms without requiring the original repository checkout.

Troubleshooting reminders

• requests missing usually means the bundled runtime bootstrap was skipped; run python scripts/install_runtime.py from the skill output.
• librosa missing usually means asteroid.data or audio-visual helpers were imported without the optional audio dependency.
• Sample-rate mismatches usually need --resample or a model whose sample_rate matches the input.
• Existing output files need --force-overwrite.

Inputs to inspect

• checkpoint source and format
• sample rate and channel count
• whether the request is tensor, numpy, file, or folder based
• whether the user wants a one-off smoke or a reusable separation recipe

Smoke sequence

1. Load or serialize a tiny model.
2. Round-trip it through from_pretrained(...).
3. Run a tiny tensor separation call.
4. Run a tiny file separation call if files are involved.
5. Add overlap-add only when the request mentions long files or chunking.

What to avoid

• Do not tell the user to rely on the original checkout at runtime.
• Do not imply that a remote model download is guaranteed to work offline.
• Do not hide a sample-rate mismatch behind a vague error message.
• Do not use model-sharing guidance unless the user is actually preparing an upload.

Output shapes to remember

• tensor and numpy paths keep a source dimension in the output
• file paths write WAV files next to the input or in the requested output directory
• overlap-add should preserve the source dimension while chunking long signals

Good questions to ask when unclear

• Is the checkpoint local or remote?
• Do you want files or in-memory arrays?
• Should the output be forced onto CPU or a specific CUDA device?
• Is this a short clip or a long-file inference problem?