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Alex 7d074e1ee6 chore: consolidate Python scripts into Scripts/ (#344 )

## Summary
- Move `Benchmarks/nemo` to `Scripts/nemo_ami_benchmark`
- Move `Tools/voice_cloning` to `Scripts/voice_cloning`
- Remove now-empty `Benchmarks/` and `Tools/` top-level directories

Consolidates standalone Python utilities into a single `Scripts/`
directory to reduce top-level clutter.

## Test plan
- [x] Verify files moved correctly (no content changes)
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2026-03-04 12:46:03 -05:00

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Voice Cloning Evaluation Scripts

Tools for evaluating PocketTTS voice cloning quality using spectral similarity.

evaluate_voice.py

Compares a reference voice sample with synthesized TTS output using mel-spectrogram and MFCC similarity metrics. No neural network required.

Install

pip install librosa numpy
# Or minimal (scipy fallback):
pip install scipy numpy

# Optional for plotting:
pip install matplotlib

Usage

# Basic comparison
python evaluate_voice.py reference.wav synthesized.wav

# With visualization
python evaluate_voice.py reference.wav synthesized.wav --plot

# JSON output
python evaluate_voice.py reference.wav synthesized.wav --json

Metrics

Metric	Description
Mel Similarity	Cosine similarity of mean mel spectrum (voice timbre)
MFCC Similarity	Cosine similarity of mean MFCCs (voice characteristics)
MFCC Std Similarity	Similarity of MFCC dynamics
Combined Score	Weighted average (0.4 mel + 0.4 mfcc + 0.2 mfcc_std)

Quality Thresholds

Score	Quality	Meaning
0.90+	Excellent	Very close spectral match
0.80+	Good	Similar voice characteristics
0.70+	Fair	Some similarity
<0.70	Poor	Different spectral characteristics

Example Workflow

# 1. Clone a voice using FluidAudio CLI
fluidaudio tts "Hello, this is a test." --backend pocket --clone-voice speaker.wav -o output.wav

# 2. Evaluate the result
python Tools/voice_cloning/evaluate_voice.py speaker.wav output.wav --plot

Output Example

Reference:   speaker.wav
Synthesized: output.wav

Reference duration:   5.23s
Synthesized duration: 2.15s

Computing spectral similarity...

  Mel Similarity:      0.9234
  MFCC Similarity:     0.8876
  MFCC Std Similarity: 0.8543
  Combined Score:      0.8951
  Quality:             Good

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