Audio Processing (Iyeque)

Audio ingestion, analysis, transformation, and generation (Transcribe, TTS, VAD, Features).

Audio Processing Skill

A comprehensive toolset for audio manipulation and analysis with security validations.

Security

• File paths are validated to prevent path traversal attacks
• Access to system directories (/etc, /proc, /sys, /root) is blocked
• TTS text input is limited to 10,000 characters
• All file operations use resolved absolute paths

Tool API

audio_tool

Perform audio operations like transcription, text-to-speech, and feature extraction.

• Parameters:
  • action (string, required): One of transcribe, tts, extract_features, vad_segments, transform.
  • file_path (string, optional): Path to input audio file.
  • text (string, optional): Text for TTS (max 10,000 chars).
  • output_path (string, optional): Path for output file (default: auto-generated).
  • model (string, optional): Whisper model size (tiny, base, small, medium, large). Default: base.
  • ops (string, optional): JSON string of operations for transform action.

Usage:

bash

# Transcribe audio file
uv run --with "openai-whisper" --with "pydub" --with "numpy" skills/audio-processing/tool.py transcribe --file_path input.wav

# Transcribe with specific model
uv run --with "openai-whisper" skills/audio-processing/tool.py transcribe --file_path input.wav --model small

# Text-to-speech
uv run --with "gTTS" skills/audio-processing/tool.py tts --text "Hello world" --output_path hello.mp3

# Extract audio features
uv run --with "librosa" --with "numpy" --with "soundfile" skills/audio-processing/tool.py extract_features --file_path input.wav

# Voice activity detection (find speech segments)
uv run --with "pydub" skills/audio-processing/tool.py vad_segments --file_path input.wav

# Transform audio (trim, resample, normalize)
uv run --with "pydub" skills/audio-processing/tool.py transform --file_path input.wav --ops '[{"op": "trim", "start": 10, "end": 30}, {"op": "normalize"}]'

Actions

transcribe

Convert speech to text using OpenAI Whisper.

• Returns: { "text": "...", "segments": [...] }
• Models: tiny, base, small, medium, large (larger = more accurate, slower)

tts

Generate speech from text using Google TTS.

• Returns: { "file_path": "output.mp3", "status": "created" }
• Language: English (default)

extract_features

Extract audio features for analysis.

• Returns: duration, sample_rate, mfcc_mean, rms_mean
• Useful for audio classification, quality analysis

vad_segments

Detect speech segments using silence detection.

• Returns: { "segments": [{ "start": 0.5, "end": 3.2 }, ...] }
• Uses FFmpeg silencedetect filter
• Aggressiveness: 1-3 (default: 2)

transform

Apply transformations to audio files.

• Operations: trim, resample, normalize
• Returns: { "file_path": "output.wav" }

Requirements

• ffmpeg: Required for VAD and transform operations
• Python 3.8+: All operations
• Disk Space: Whisper models range from 100MB (tiny) to 3GB (large)

Error Handling

• Returns JSON error object on failure
• Validates all file paths before processing
• Gracefully handles missing dependencies