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fb18897 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | """Extract F0 (pyin) + RMS from WAVs, tokenize text, compute durations."""
import argparse
import json
import os
import numpy as np
import torch
import librosa
# Character vocabulary: PAD=0, UNK=1, space=2, a-z=3-28, 0-9=29-38, punct 39+
VOCAB = {chr(0): 0} # PAD
VOCAB['<UNK>'] = 1
VOCAB[' '] = 2
for i, c in enumerate('abcdefghijklmnopqrstuvwxyz'):
VOCAB[c] = 3 + i
for i, c in enumerate('0123456789'):
VOCAB[c] = 29 + i
PUNCT = ".,;:!?'-\"()/"
for i, c in enumerate(PUNCT):
VOCAB[c] = 39 + i
VOCAB_SIZE = max(VOCAB.values()) + 1
def tokenize(text):
"""Lowercase text → char ID list."""
text = text.lower()
return [VOCAB.get(c, VOCAB['<UNK>']) for c in text]
def proportional_durations(n_chars, n_frames):
"""Split n_frames proportionally across n_chars."""
if n_chars == 0:
return []
base = n_frames // n_chars
remainder = n_frames % n_chars
durations = [base + (1 if i < remainder else 0) for i in range(n_chars)]
return durations
def extract_one(wav_path, text, sr=24000, hop_length=2400):
"""Extract features for a single sample."""
y, _ = librosa.load(wav_path, sr=sr, mono=True)
# F0 via pyin
f0, voiced_flag, _ = librosa.pyin(
y, fmin=50, fmax=600, sr=sr, hop_length=hop_length
)
# Clip to 300 Hz to suppress octave jumps; mark >300 as unvoiced
too_high = ~np.isnan(f0) & (f0 > 300)
voiced_flag[too_high] = False
f0 = np.where(np.isnan(f0), 0.0, np.clip(f0, 50, 300))
# RMS
rms = librosa.feature.rms(y=y, hop_length=hop_length, frame_length=hop_length)[0]
# Align lengths
n_frames = min(len(f0), len(rms))
f0 = f0[:n_frames]
voiced_flag = voiced_flag[:n_frames]
rms = rms[:n_frames]
# Log space
voiced_mask = voiced_flag.astype(bool)
log_f0 = np.zeros_like(f0)
log_f0[voiced_mask] = np.log(f0[voiced_mask])
log_rms = np.log(rms + 1e-8)
# Tokenize
char_ids = tokenize(text)
# Duration alignment
durations = proportional_durations(len(char_ids), n_frames)
return {
'char_ids': np.array(char_ids, dtype=np.int64),
'durations': np.array(durations, dtype=np.int64),
'log_f0': log_f0.astype(np.float32),
'log_rms': log_rms.astype(np.float32),
'voiced_mask': voiced_mask,
'n_frames': n_frames,
'text': text,
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--audio_dir', required=True)
parser.add_argument('--transcripts', required=True)
parser.add_argument('--output', default='features.pt')
args = parser.parse_args()
with open(args.transcripts) as f:
transcripts = json.load(f)
# Filter to sample_*.wav keys only
keys = sorted([k for k in transcripts if k.startswith('sample_') and k.endswith('.wav')])
print(f"Processing {len(keys)} samples...")
samples = []
all_voiced_f0 = []
all_log_rms = []
for i, key in enumerate(keys):
wav_path = os.path.join(args.audio_dir, key)
if not os.path.exists(wav_path):
print(f" SKIP {key}: file not found")
continue
feat = extract_one(wav_path, transcripts[key])
samples.append(feat)
# Collect for normalization
if feat['voiced_mask'].any():
all_voiced_f0.append(feat['log_f0'][feat['voiced_mask']])
all_log_rms.append(feat['log_rms'])
if (i + 1) % 200 == 0:
print(f" {i+1}/{len(keys)} done")
# Global normalization stats
all_voiced_f0 = np.concatenate(all_voiced_f0)
all_log_rms = np.concatenate(all_log_rms)
norm_stats = {
'f0_mean': float(np.mean(all_voiced_f0)),
'f0_std': float(np.std(all_voiced_f0)),
'rms_mean': float(np.mean(all_log_rms)),
'rms_std': float(np.std(all_log_rms)),
}
print(f"Norm stats: {norm_stats}")
# Z-score normalize
for s in samples:
voiced = s['voiced_mask']
s['log_f0'][voiced] = (s['log_f0'][voiced] - norm_stats['f0_mean']) / norm_stats['f0_std']
s['log_rms'] = (s['log_rms'] - norm_stats['rms_mean']) / norm_stats['rms_std']
# Print stats
voiced_ratios = [s['voiced_mask'].mean() for s in samples]
frame_counts = [s['n_frames'] for s in samples]
print(f"Samples: {len(samples)}")
print(f"Voiced ratio: {np.mean(voiced_ratios):.3f} ± {np.std(voiced_ratios):.3f}")
print(f"Frame counts: {np.mean(frame_counts):.1f} ± {np.std(frame_counts):.1f} "
f"(min={np.min(frame_counts)}, max={np.max(frame_counts)})")
# Convert to tensors for saving
for s in samples:
s['char_ids'] = torch.from_numpy(s['char_ids'])
s['durations'] = torch.from_numpy(s['durations'])
s['log_f0'] = torch.from_numpy(s['log_f0'])
s['log_rms'] = torch.from_numpy(s['log_rms'])
s['voiced_mask'] = torch.from_numpy(s['voiced_mask'])
torch.save({'samples': samples, 'norm_stats': norm_stats, 'vocab_size': VOCAB_SIZE}, args.output)
print(f"Saved to {args.output}")
if __name__ == '__main__':
main()
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