# Analyzing Wav

> Analyzing Wav is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Analyze WAV audio files for debugging TTS and audio pipelines. Use when checking audio quality, validating WAV format, inspecting waveform patterns, or diagnosing generated speech issues.

- Skill name: `analyzing-wav`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/analyzing-wav/SKILL.md
- Repository: majiayu000/claude-skill-registry (498 GitHub stars when collected)

## SKILL.md

# WAV Audio Analysis Skill

## Description

Analyze WAV audio files to debug audio generation pipelines. Provides statistical analysis, format validation, and quality metrics for diagnosing issues with generated speech.

**Triggers:** wav, audio, waveform, samples, amplitude, audio analysis, sound quality, audio debug

## Analysis Capabilities

### Basic Statistics

- Sample count and duration
- Min/max amplitude
- Standard deviation (expected ~3000-8000 for speech)
- Near-silent sample percentage

### Quality Indicators

- Zero crossing rate (speech typically 50-200 per 1000 samples)
- Clipping detection (samples at ±32767)
- NaN/Inf detection (if processing raw floats)
- DC offset analysis

### Format Validation

- Sample rate verification (24kHz for Qwen3-Omni TTS)
- Bit depth check
- Channel count
- RIFF header validation

## Usage

To analyze a WAV file, provide the path and I'll run comprehensive diagnostics:

```python
import numpy as np

with open("audio.wav", "rb") as f:
    header = f.read(44)
    data = f.read()

samples = np.frombuffer(data, dtype=np.int16)
print(f"Samples: {len(samples)}")
print(f"Duration: {len(samples)/24000:.2f} sec")
print(f"Min/Max: {samples.min()} / {samples.max()}")
print(f"Std dev: {np.std(samples):.1f}")

# Quality check
near_silent = np.sum(np.abs(samples) < 100)
print(f"Near-silent: {100*near_silent/len(samples):.1f}%")

# Zero crossings (voice activity indicator)
if len(samples) > 1000:
    zc = np.sum(np.diff(np.sign(samples[:1000])) != 0)
    print(f"Zero crossings (first 1000): {zc}")
```

## Typical Values for Good Speech Audio

| Metric         | Expected Range | Meaning                  |
| -------------- | -------------- | ------------------------ |
| Std dev        | 3000-8000      | Audio energy level       |
| Near-silent    | <5%            | Minimal silent padding   |
| Zero crossings | 50-200/1000    | Voice frequency activity |
| Min/Max        | ±20000-32000   | Healthy amplitude range  |

## Common Issues

### 99% Near-Silent

- Cause: NaN values converted to zeros
- Fix: Check for numerical overflow in pipeline

### Low Std Dev (<1000)

- Cause: Values too quiet before output normalization
- Fix: Check gain stages, ensure proper scaling

### Constant Value Runs

- Cause: Chunked processing with context overlap issues
- Fix: Verify chunk stitching logic

### Clipping (values at ±32767)

- Cause: Overflow or missing tanh/clamp
- Fix: Add output clamping before int16 conversion

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Source: https://www.openparable.com/skill/documents/analyzing-wav (OpenParable). Cite the canonical URL above.