본문으로 건너뛰기
cccskills
무료GitHub에 공개

espnet

Use when working with ESPnet speech AI toolkit: installation, ESPnet2 recipes and training, pretrained inference/model zoo, ESPnet3 stage workflows, and repository testing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill
설치 방법 보기

포함된 파일(40)

  • SKILL.md3.9 KB
  • references/repo-provenance.md1.4 KB
  • references/repo-routing-metadata.json487 B
  • references/task-surface.md2.1 KB
  • references/troubleshooting.md2.9 KB
  • scripts/check_import_surface.py2.0 KB
  • sub-skills/development-and-testing/references/ci-command-map.md1.2 KB
  • sub-skills/development-and-testing/references/testing-and-contributing.md1.7 KB
  • sub-skills/development-and-testing/references/troubleshooting.md1.4 KB
  • sub-skills/development-and-testing/scripts/select_espnet_tests.py2.1 KB
  • sub-skills/development-and-testing/SKILL.md1.1 KB
  • sub-skills/espnet2-training/references/configuration-and-components.md1.3 KB
  • sub-skills/espnet2-training/references/gpu-distributed-training.md1.6 KB
  • sub-skills/espnet2-training/references/training-cli-reference.md2.1 KB
  • sub-skills/espnet2-training/references/troubleshooting.md1.6 KB
  • sub-skills/espnet2-training/scripts/inspect_training_cli.py1.7 KB
  • sub-skills/espnet2-training/scripts/make_dry_run_command.py1.5 KB
  • sub-skills/espnet2-training/SKILL.md1.2 KB
  • sub-skills/espnet3-workflows/references/configuration.md1.1 KB
  • sub-skills/espnet3-workflows/references/stage-runner.md1.2 KB
  • sub-skills/espnet3-workflows/references/troubleshooting.md1.2 KB
  • sub-skills/espnet3-workflows/scripts/inspect_espnet3_stages.py2.5 KB
  • sub-skills/espnet3-workflows/SKILL.md1.1 KB
  • sub-skills/inference-and-model-zoo/references/inference-api-reference.md2.2 KB
  • sub-skills/inference-and-model-zoo/references/model-zoo-and-packaging.md1.5 KB
  • sub-skills/inference-and-model-zoo/references/troubleshooting.md1.3 KB
  • sub-skills/inference-and-model-zoo/scripts/check_model_files.py2.2 KB
  • sub-skills/inference-and-model-zoo/scripts/inspect_inference_entrypoints.py3.1 KB
  • sub-skills/inference-and-model-zoo/SKILL.md1.1 KB
  • sub-skills/installation-and-diagnostics/references/install-matrix.md4.2 KB
  • sub-skills/installation-and-diagnostics/references/troubleshooting.md2.2 KB
  • sub-skills/installation-and-diagnostics/scripts/check_espnet_environment.py4.1 KB
  • sub-skills/installation-and-diagnostics/SKILL.md1.1 KB
  • sub-skills/recipes-and-data/references/espnet2-recipes.md2.3 KB
  • sub-skills/recipes-and-data/references/kaldi-data-formats.md1.8 KB
  • sub-skills/recipes-and-data/references/troubleshooting.md1.6 KB
  • sub-skills/recipes-and-data/references/utility-cli-reference.md1.5 KB
  • sub-skills/recipes-and-data/scripts/make_tiny_kaldi_fixture.py1.6 KB
  • sub-skills/recipes-and-data/scripts/validate_kaldi_data_dir.py3.8 KB
  • sub-skills/recipes-and-data/SKILL.md1.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

ESPnet Repo Skill

Use this operating skill when a task involves ESPnet, the end-to-end speech processing toolkit for ASR, TTS, speech translation, enhancement/separation, speaker tasks, diarization, SLU, SVS, SpeechLM, and related audio workflows.

This skill is self-contained. Do not assume the source checkout used to create it is available unless the user is explicitly editing an ESPnet checkout. Prefer the bundled references and scripts here over reopening original docs, examples, or tools.

First response pattern

  1. Classify the request: install/diagnose, prepare recipe data, train/configure, run inference/model-zoo, use ESPnet3 stages, or develop/test ESPnet.
  2. Read the matching sub-skill before detailed commands.
  3. Use bundled helper scripts when they fit. They are safe by default: no downloads, no package installs, no training, no uploads.
  4. Keep backend claims honest. CPU parser/import checks do not prove CUDA, distributed, FlashAttention, k2, or recipe-scale training.

Minimal package check

python -c "import espnet2, espnet3; print('ESPnet imports ok')"

For source development or editable use, install only the selected task extras, e.g. pip install -e ".[asr]" for ASR or pip install -e ".[tts]" for TTS. Avoid .[all] unless the user truly needs many optional task families.

Route map

User intentRead nextWhy
Install ESPnet, choose extras, diagnose imports or optional toolsinstallation-and-diagnosticsBase dependencies, extras, host tools, CUDA probes, safe environment checker.
Create/adapt ESPnet2 recipes, validate data/, understand wav.scp/segments, stage commands, tokenization utilitiesrecipes-and-dataKaldi-style data layouts, task script stage flow, validation helpers, utility CLIs.
Configure/train ESPnet2 models, use --print_config, dry-run configs, tune components, resume/fine-tune, GPU/distributed flagsespnet2-trainingTrain modules, Task config semantics, dry-run command generation, training troubleshooting.
Run pretrained or local inference, use from_pretrained, ModelDownloader, streaming ASR, enhancement, TTS, packaginginference-and-model-zooInference classes/CLIs, model file pairing, model zoo, packaging checks.
Use ESPnet3 System/Hydra stage runner, configs, --stages, demo/publication boundariesespnet3-workflowsESPnet3 stage order, required config flags, safe stage inspection.
Modify ESPnet source, choose tests, debug CI, add recipe/module tests, follow contribution conventionsdevelopment-and-testingFocused pytest/CI command selection, style, recipe PR policy, maintainer troubleshooting.

Shared runtime files

Safety boundaries

  • Ask before full recipes, dataset acquisition, model downloads, Gradio demos, uploads, long training, or broad CI.
  • CUDA is optional unless the user's selected workflow requires GPU runtime.
  • Model-zoo tasks are network/cache dependent; validate local file paths before downloads.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

3d-resnets-pytorch

무료

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

3ddfa

무료

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

3ddfa-v2

무료

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

ab3dmot

무료

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

accelerate

무료

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

acme

무료

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고