---

name: paddlespeech description: "Use PaddleSpeech for ASR, speech translation, Whisper, SSL, TTS, punctuation restoration, audio classification, speaker vectors, keyword spotting, and PaddleSpeech server/client workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PaddleSpeech

Use this skill when the task names PaddleSpeech, paddlespeech, paddlespeech_server, paddlespeech_client, PP-ASR, PP-TTS, PP-VPR, speech recognition, speech translation, Whisper, SSL speech models, text-to-speech, punctuation restoration, audio classification, speaker embedding or verification, keyword spotting, or PaddleSpeech Serving.

Start Here

1. Decide whether the user wants package use, recipe planning, service deployment, or repository maintenance.
2. For package use, prefer the public CLIs and executor classes before reaching for internal model modules.
3. Treat pretrained model execution, sample-audio downloads, dataset downloads, server warmup, Docker services, C++/mobile builds, and GPU training as side effects. Ask before running them unless the user already approved downloads/services/long jobs.
4. Run scripts/check_paddlespeech_environment.py for a safe local preflight. It checks imports, metadata, and command availability without downloading models.
5. Read references/troubleshooting.md when import, optional dependency, cache, audio-format, model tag, CLI stats, or server-port failures appear.

Route by Task

• ASR, speech translation, SSL, and Whisper: use sub-skills/speech-to-text/SKILL.md for paddlespeech asr, st, ssl, whisper, ASRExecutor, STExecutor, SSLExecutor, WhisperExecutor, audio sample-rate checks, ASR recipes, and transcript post-processing routes.
• Text-to-speech and vocoders: use sub-skills/text-to-speech/SKILL.md for paddlespeech tts, acoustic/vocoder pair selection, ONNX/static inference, multi-speaker and multilingual options, TTS training/synthesis recipes, voice cloning orientation, and TTS frontend interactions.
• Text processing and frontends: use sub-skills/text-processing/SKILL.md for paddlespeech text --task punc, punctuation restoration models, text normalization, G2P, MFA/rhythm-tag prep, and SentencePiece tokenizer recipes.
• Audio classification, speaker vectors, and KWS: use sub-skills/audio-analysis/SKILL.md for paddlespeech cls, vector, kws, SSL vector extraction, ESC-50, VoxCeleb, HeySnips, audio augmentation, and audio-search planning.
• Server, clients, streaming, and deployment: use sub-skills/deployment-serving/SKILL.md for paddlespeech_server start, paddlespeech_client, HTTP and WebSocket APIs, application.yaml, streaming ASR/TTS configs, Paddle Inference, ONNX, C++ runtime, Android/ARM, and service troubleshooting.

Install and Minimal Checks

Install the package and a compatible PaddlePaddle runtime in the same environment. For a CPU package-user baseline:

python -m pip install paddlepaddle
python -m pip install paddlespeech
python -I -c "import paddle, paddlespeech; print(paddle.__version__)"

Then verify the command surfaces without downloading models:

paddlespeech help
paddlespeech_server help
paddlespeech_client help

Package Orientation

PaddleSpeech is distributed as paddlespeech and exposes these console scripts:

paddlespeech help
paddlespeech_server help
paddlespeech_client help

The main paddlespeech commands in this checkout are help, version, stats, asr, cls, st, text, tts, vector, kws, ssl, and whisper. The server command exposes start and stats; the client exposes tts, tts_online, asr, asr_online, cls, text, vector, and acs.

For direct Python use, prefer executor classes from the command modules, for example paddlespeech.cli.asr.ASRExecutor, paddlespeech.cli.tts.TTSExecutor, paddlespeech.cli.text.TextExecutor, and server/client executors under paddlespeech.server.bin.

References and Helpers

• references/install-and-environment.md covers install modes, PaddlePaddle runtime, optional dependencies, cache roots, and backend limits.
• references/cli-reference.md lists safe command patterns, batch/stdin/job behavior, and model-stat caveats.
• references/model-and-resource-overview.md explains model tags, aliases, resource download/cache behavior, and core released model families.
• references/troubleshooting.md covers cross-cutting install, import, dependency, audio, cache, CLI, and server failures.
• references/repo-provenance.md records the source revision and evidence baseline.
• scripts/check_paddlespeech_environment.py performs safe import and CLI preflight checks.

Safety Boundaries

• Do not run full demo run.sh, training recipes, benchmark/TIPC scripts, server warmup, Docker Compose apps, or model-download examples unless the user explicitly accepts the download/runtime side effects.
• Do not assume CUDA is required. CPU is sufficient for package inspection, command construction, static config checks, and selected safe unit tests; CUDA is optional for faster training/inference.
• Do not assume model quality from a help/import check. Actual ASR/TTS/Whisper/CLS/vector/KWS outputs require model archives, input audio, and runtime checks.
• If a current checkout differs from references/repo-provenance.md, refresh this skill before relying on fine-grained paths, model tags, or CLI behavior.