---

name: speech-to-text description: "Use PaddleSpeech ASR, speech translation, SSL, and Whisper CLIs/APIs with safe audio validation, model choices, and recipe boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Speech-to-Text

Use this sub-skill for PaddleSpeech speech recognition, speech translation, SSL speech models, Whisper transcription/translation, ASR recipe planning, audio validation, ASR job files, and transcript post-processing routes.

Choose the Workflow

User goal	Use
Mandarin or English ASR with PaddleSpeech models	paddlespeech asr and ASRExecutor guidance in references/cli-and-api.md
English-to-Chinese speech translation	paddlespeech st and ST recipe notes in references/asr-st-ssl-whisper-workflows.md
Wav2Vec2/Hubert/WavLM ASR or embeddings	`paddlespeech ssl --task asr
General Whisper transcribe/translate	paddlespeech whisper guidance
ASR training/evaluation recipe planning	recipe map in references/asr-st-ssl-whisper-workflows.md; ask before downloads/training
Add punctuation to ASR text	route to ../text-processing/SKILL.md after ASR output exists
Serve ASR over HTTP/WebSocket	route to ../deployment-serving/SKILL.md

Safe Workflow

1. Validate audio before model execution:

   python scripts/validate_audio_inputs.py --sample-rate 16000 audio.wav
   

2. Pick the command family:

   paddlespeech asr --input audio.wav --lang zh --sample_rate 16000
   paddlespeech st --input english_16k.wav
   paddlespeech ssl --task asr --model wav2vec2 --lang en --input audio.wav
   paddlespeech whisper --task transcribe --size tiny --input audio.wav
   

3. If using pretrained defaults, warn that the first run downloads model archives into the PaddleSpeech cache.

4. Use --device cpu for deterministic CPU runs or --device gpu:0 only when a compatible PaddlePaddle GPU build is installed.

5. For long audio, split before ASR rather than relying on a model-specific max-duration failure.

References and Helper

• references/cli-and-api.md lists ASR/ST/SSL/Whisper commands, options, and executor classes.
• references/asr-st-ssl-whisper-workflows.md distills training/evaluation, decoding, and recipe boundaries.
• references/troubleshooting.md covers audio, model tag, Kaldi, Whisper resource, and long-audio failures.
• scripts/validate_audio_inputs.py checks WAV properties and can build simple .job files.

Do Not Do by Default

• Do not run full ASR/ST recipe run.sh scripts, tests/unit/cli/test_cli.sh, or demo scripts without approval; they download data/models and can train or run for a long time.
• Do not install Kaldi/KenLM/OpenFST/MFA or mutate system tools unless the user explicitly asks for recipe/toolchain execution.
• Do not present SSL or Whisper stats display failures as proof that command execution is impossible; use help/import checks and direct tag references.