speech-to-text
by NoizAIThe speech-to-text skill transcribes supported audio files into plain text, with options for timestamps, speaker labels, and JSON output. It is designed for practical speech-to-text usage in repeatable workflows, including interviews, meetings, podcasts, lectures, and automation tasks where consistent transcription matters.
This skill scores 78/100, which means it is a solid directory listing candidate: users can likely trigger it correctly and understand the intended workflow without much guesswork, though they should expect a few adoption gaps around setup and edge cases. The repository provides enough real operational detail to justify installation for transcript-focused agents.
- Strong triggerability: the SKILL.md explicitly lists transcription-related triggers, including speech-to-text, transcript, subtitle generation, and multilingual requests.
- Concrete workflow value: Quick Start examples show direct CLI usage for audio files, language selection, file output, and JSON output with timestamps/speaker labels.
- Operational implementation exists: the included scripts/stt.py suggests this is a working skill rather than a placeholder, with API-key handling and format validation.
- Setup is only partially documented in the visible evidence: there is no install command in SKILL.md, so users may need to infer dependencies and environment setup.
- The skill appears API-dependent and size-limited (NOIZ_API_KEY, max 50 MB, max 10 min), which may restrict some real-world transcription jobs.
Overview of speech-to-text skill
What this speech-to-text skill does
The speech-to-text skill turns supported audio files into plain text transcripts, with options for timestamps, speaker labels, and JSON output. It is best for users who want a practical speech-to-text workflow rather than a generic prompt that guesses at transcription steps.
Who should install it
Install the speech-to-text skill if you regularly need to transcribe interviews, meetings, podcasts, lectures, voice notes, or short video audio tracks. It is especially useful for workflow automation where transcription is a repeatable step and you want a consistent command-style process.
What matters before you adopt it
The main decision points are file limits, language handling, and output format. The repo supports common audio types and exposes a clear CLI path, which makes the speech-to-text guide easy to operationalize. If you need large batches, long recordings, or highly custom diarization, check whether your use case fits the script’s constraints before relying on it.
How to Use speech-to-text skill
Install and confirm the runtime
Use the documented install path: npx skills add NoizAI/skills --skill speech-to-text. This speech-to-text install is only useful if you can also run the helper script, so confirm Python, the requests package, and a valid NOIZ_API_KEY are available in your environment.
Feed the skill the right input
The script expects a real audio file, not a vague request. Strong inputs name the file, language if known, desired output, and any formatting needs. For example: “Transcribe meeting.wav in English, include timestamps, and save JSON to result.json.” That is better than “transcribe this” because it removes ambiguity from the speech-to-text usage.
Read these files first
Start with SKILL.md for triggers, arguments, and output patterns, then inspect scripts/stt.py for actual validation rules, file handling, and API behavior. If you are adapting speech-to-text for Workflow Automation, the script matters more than the prose because it reveals what the skill can and cannot accept in production-like use.
Best-practice prompt shape
A good invocation should specify:
- the source file path
- whether language is known or should be auto-detected
- whether you want plain text, JSON, or saved output
- whether timestamps or speaker labels matter
A practical speech-to-text prompt might be: “Use the speech-to-text skill on podcast.m4a. Auto-detect language, return a clean transcript, and include timestamps in JSON because I need to publish captions later.”
speech-to-text skill FAQ
Is this only for audio files?
The core speech-to-text skill is built for audio transcription, and the repository examples focus on files such as MP3, WAV, M4A, OGG, FLAC, AAC, and WEBM. If your source is video, you usually need audio extraction first unless your own workflow already handles that step.
What is the main limit to know before install?
The biggest practical limits are file size and duration. If your workflow often exceeds those limits, the speech-to-text install may still be fine for small jobs, but it will not be the right default for long-form archival transcription.
How is this different from a normal transcription prompt?
A normal prompt can describe the task, but the speech-to-text skill gives you a repeatable operational path: install, required key, supported inputs, output modes, and a script-driven workflow. That makes it more reliable for repeated speech-to-text usage than a one-off instruction.
Is it beginner-friendly?
Yes, if you can run a basic Python command and set an API key. The speech-to-text guide is straightforward, but beginners should still read the script so they do not assume unsupported file types, output options, or language behavior.
How to Improve speech-to-text skill
Specify the transcription target clearly
Better results start with clearer intent. Say whether you need verbatim text, readable cleaned-up transcript, timestamps, speaker labels, or machine-readable JSON. The speech-to-text skill can support several outputs, but you need to choose the one that matches the downstream job.
Use file and language details
If you know the language, provide it. If the recording has multiple speakers, say so. If the audio is noisy, mention that too. These details improve speech-to-text output quality because they reduce guesswork in decoding accents, switching languages, and segmenting speakers.
Match the output to the next step
For editing, ask for plain text. For captioning or automation, ask for JSON or timestamped output. For search indexing, ask for a transcript that preserves speaker turns. This is where speech-to-text for Workflow Automation becomes useful: the output should be shaped for the next tool, not just for reading.
Iterate from the first transcript
If the first pass is close but not usable, refine the input instead of restarting broadly. Common fixes are: provide the correct language, trim silence or background noise, split long files, or request a different output format. That is the fastest way to improve a speech-to-text skill without changing your whole workflow.