Initial commit for TTS project
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scripts/analysis/analyze_local_accent_demos.py
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186
scripts/analysis/analyze_local_accent_demos.py
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#!/usr/bin/env python3
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"""
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Analyze only the local accent demos
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"""
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import os
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import numpy as np
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import soundfile as sf
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from scipy import signal
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from scipy.stats import skew, kurtosis
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def analyze_audio_file(file_path):
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"""Analyze audio file and return quality metrics"""
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if not os.path.exists(file_path):
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print(f"File not found: {file_path}")
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return None
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try:
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# Read audio file
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audio_data, sample_rate = sf.read(file_path)
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print(f"✓ Successfully loaded: {os.path.basename(file_path)}")
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print(f" Sample rate: {sample_rate} Hz")
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print(f" Duration: {len(audio_data)/sample_rate:.2f} seconds")
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print(f" Channels: {1 if len(audio_data.shape) == 1 else audio_data.shape[1]}")
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# Convert to mono if stereo
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if len(audio_data.shape) > 1:
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audio_data = np.mean(audio_data, axis=1)
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# Basic audio statistics
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rms_energy = np.sqrt(np.mean(audio_data**2))
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peak_amplitude = np.max(np.abs(audio_data))
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zero_crossing_rate = np.mean(np.abs(np.diff(np.sign(audio_data))))
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spectral_centroid = calculate_spectral_centroid(audio_data, sample_rate)
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skewness = skew(audio_data)
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kurt = kurtosis(audio_data)
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print(f"\n📊 Audio Statistics:")
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print(f" RMS Energy: {rms_energy:.4f}")
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print(f" Peak Amplitude: {peak_amplitude:.4f}")
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print(f" Zero Crossing Rate: {zero_crossing_rate:.4f}")
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print(f" Spectral Centroid: {spectral_centroid:.2f} Hz")
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print(f" Skewness: {skewness:.4f}")
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print(f" Kurtosis: {kurt:.4f}")
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# Quality assessment
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quality_score = assess_audio_quality({
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'rms_energy': rms_energy,
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'zero_crossing_rate': zero_crossing_rate,
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'spectral_centroid': spectral_centroid,
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'skewness': skewness,
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'kurtosis': kurt,
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'duration': len(audio_data)/sample_rate
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})
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quality = 'good' if quality_score > 60 else 'poor'
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print(f" Quality Score: {quality_score}/100 ({quality})")
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return {
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'file': file_path,
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'sample_rate': sample_rate,
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'duration': len(audio_data)/sample_rate,
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'rms_energy': rms_energy,
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'zero_crossing_rate': zero_crossing_rate,
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'spectral_centroid': spectral_centroid,
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'quality_score': quality_score,
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'quality': quality
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}
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except Exception as e:
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print(f"Error analyzing {file_path}: {e}")
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return None
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def calculate_spectral_centroid(audio_data, sample_rate):
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"""Calculate spectral centroid (brightness of sound)"""
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# Compute spectrogram
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frequencies, times, Sxx = signal.spectrogram(audio_data, sample_rate)
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# Calculate spectral centroid
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if np.sum(Sxx) == 0:
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return 0
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spectral_centroid = np.sum(frequencies[:, np.newaxis] * Sxx) / np.sum(Sxx)
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return spectral_centroid
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def assess_audio_quality(metrics):
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"""Assess audio quality based on metrics"""
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score = 0
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# RMS Energy: Good range for speech is 0.05-0.3
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rms = metrics['rms_energy']
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if 0.05 <= rms <= 0.3:
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score += 20
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elif 0.02 <= rms < 0.05 or 0.3 < rms <= 0.5:
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score += 10
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else:
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score += 0
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# Zero Crossing Rate: Good range for speech is 0.05-0.15
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zcr = metrics['zero_crossing_rate']
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if 0.05 <= zcr <= 0.15:
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score += 20
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elif 0.02 <= zcr < 0.05 or 0.15 < zcr <= 0.2:
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score += 10
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else:
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score += 0
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# Spectral Centroid: Good range for speech is 800-2500 Hz
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sc = metrics['spectral_centroid']
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if 800 <= sc <= 2500:
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score += 20
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elif 500 <= sc < 800 or 2500 < sc <= 3500:
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score += 10
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elif 200 <= sc < 500:
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score += 5
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else:
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score += 0
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# Duration: Speech should be reasonable length
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duration = metrics['duration']
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if 1.0 <= duration <= 10.0:
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score += 20
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elif 0.5 <= duration < 1.0 or 10.0 < duration <= 15.0:
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score += 10
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else:
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score += 0
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# Skewness and Kurtosis: Should be moderate for natural speech
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skewness = abs(metrics['skewness'])
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kurtosis = abs(metrics['kurtosis'])
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if skewness < 2 and kurtosis < 10:
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score += 20
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elif skewness < 5 and kurtosis < 20:
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score += 10
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else:
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score += 0
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return score
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def analyze_directory(directory):
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"""Analyze all audio files in a directory"""
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if not os.path.exists(directory):
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print(f"Directory not found: {directory}")
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return
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print(f"\n{'='*60}")
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print(f"ANALYZING LOCAL ACCENT DEMOS: {directory}")
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print(f"{'='*60}")
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audio_files = [f for f in os.listdir(directory) if f.endswith('.wav')]
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if not audio_files:
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print("No WAV files found")
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return
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results = []
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for audio_file in audio_files:
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file_path = os.path.join(directory, audio_file)
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result = analyze_audio_file(file_path)
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if result:
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results.append(result)
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print(f"{'='*60}")
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# Summary
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if results:
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good_files = [r['file'] for r in results if r['quality'] == 'good']
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poor_files = [r['file'] for r in results if r['quality'] == 'poor']
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print(f"\n📋 Summary:")
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print(f"Total files analyzed: {len(results)}")
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print(f"Good quality files: {len(good_files)}")
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print(f"Poor quality files: {len(poor_files)}")
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if good_files:
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print("\nGood quality examples:")
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for f in good_files[:3]:
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print(f" - {os.path.basename(f)}")
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if poor_files:
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print("\nPoor quality examples:")
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for f in poor_files[:3]:
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print(f" - {os.path.basename(f)}")
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if __name__ == "__main__":
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# Analyze only the local accent demos
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analyze_directory("accent_demos_local")
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