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name: audio-asr description: "Routes AXLearn audio feature extraction, ASR, Conformer, LibriSpeech, and WER workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

audio-asr

Use this sub-skill for AXLearn's speech and ASR workflows.

Typical triggers:

• Conformer, LibriSpeech, ASREncoder, ASRModel, or speech feature extraction.
• LogMelFrontend, SpeechFeatureLayer, speech_input, text_input, or WordErrorRateMetricCalculator.
• Streaming layer helpers or ASR decode / evaluation utilities.

If the task is only about the shared trainer runtime, use ../training-core/ first. If the task is about axlearn gcp ..., use ../cli-cloud/.

What to read

• references/workflows.md for LibriSpeech and ASR workflow structure.
• references/troubleshooting.md for tokenizer, fake-data, and feature-extraction failures.
• scripts/inspect_audio_configs.py for a safe config-inspection helper.

Common routes

Inspect the LibriSpeech trainer catalog

python scripts/inspect_audio_configs.py --module axlearn.experiments.audio.conformer.librispeech_trainer --config conformer-test-ctc --data-dir FAKE

Run a CPU-safe fake-data probe

Set DATA_DIR=FAKE so the LibriSpeech helpers use synthetic speech/text examples instead of TFDS-backed data.

Inspect ASR building blocks

The core pieces are:

• LogMelFrontend for log-mel feature extraction.
• SpeechFeatureLayer for feature extraction + subsampling.
• ASREncoder and ASRModel for the encoder-decoder stack.
• WordErrorRateMetricCalculator for decoding-based evaluation.

Decision points

• Choose this sub-skill when the task names speech, ASR, Conformer, LibriSpeech, or WER.
• Keep shared trainer mechanics in training-core.
• Do not route GPT or vision questions here just because they also use SpmdTrainer.