analyzing-wav

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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.

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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:

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