File size: 6,282 Bytes
de01f6a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """Feature extraction into a compact, streamable binary store.
Layout (written under ``features/``):
* ``train.bin`` — a flat little-endian float32 blob. Audio samples and the
flattened mel spectrogram for every clip are appended back to back.
* ``train.dat`` — a JSONL index/manifest. One record per clip giving byte
offsets and shapes into ``train.bin`` plus ``duration`` and ``sample_rate``.
The writer streams one clip at a time, so memory usage is independent of the
dataset size (works for datasets far larger than RAM).
"""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable, Optional
import numpy as np
from video_vec2wav2_tokenizer.audio.extractor import load_wav
from video_vec2wav2_tokenizer.utils.io import ensure_dir
from video_vec2wav2_tokenizer.utils.logging import get_logger
logger = get_logger(__name__)
_FLOAT_BYTES = 4 # float32
def compute_mel_spectrogram(
samples: np.ndarray,
sample_rate: int,
*,
n_mels: int = 80,
n_fft: int = 400,
hop_length: int = 160,
) -> np.ndarray:
"""Return a log-mel spectrogram of shape ``(n_mels, n_frames)`` (float32).
Uses librosa when available; otherwise falls back to a NumPy STFT + a
triangular mel filterbank so the feature path has no hard dependency.
"""
try:
import librosa # type: ignore
mel = librosa.feature.melspectrogram(
y=samples, sr=sample_rate, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length
)
log_mel = librosa.power_to_db(mel, ref=np.max)
return log_mel.astype(np.float32)
except ImportError:
return _numpy_log_mel(
samples, sample_rate, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length
)
def _hz_to_mel(hz: np.ndarray) -> np.ndarray:
return 2595.0 * np.log10(1.0 + hz / 700.0)
def _mel_to_hz(mel: np.ndarray) -> np.ndarray:
return 700.0 * (10.0 ** (mel / 2595.0) - 1.0)
def _mel_filterbank(sr: int, n_fft: int, n_mels: int) -> np.ndarray:
n_freqs = n_fft // 2 + 1
fft_freqs = np.linspace(0, sr / 2, n_freqs)
mel_min, mel_max = _hz_to_mel(np.array([0.0])), _hz_to_mel(np.array([sr / 2]))
mel_points = np.linspace(mel_min[0], mel_max[0], n_mels + 2)
hz_points = _mel_to_hz(mel_points)
fb = np.zeros((n_mels, n_freqs), dtype=np.float32)
for m in range(1, n_mels + 1):
f_left, f_center, f_right = hz_points[m - 1], hz_points[m], hz_points[m + 1]
left = (fft_freqs - f_left) / max(f_center - f_left, 1e-9)
right = (f_right - fft_freqs) / max(f_right - f_center, 1e-9)
fb[m - 1] = np.clip(np.minimum(left, right), 0.0, None)
return fb
def _numpy_log_mel(
samples: np.ndarray, sr: int, *, n_mels: int, n_fft: int, hop_length: int
) -> np.ndarray:
if samples.size < n_fft:
samples = np.pad(samples, (0, n_fft - samples.size))
window = np.hanning(n_fft).astype(np.float32)
n_frames = 1 + (len(samples) - n_fft) // hop_length
stft = np.empty((n_fft // 2 + 1, n_frames), dtype=np.complex64)
for i in range(n_frames):
frame = samples[i * hop_length : i * hop_length + n_fft] * window
stft[:, i] = np.fft.rfft(frame)
power = (np.abs(stft) ** 2).astype(np.float32)
fb = _mel_filterbank(sr, n_fft, n_mels)
mel = fb @ power
log_mel = 10.0 * np.log10(np.maximum(mel, 1e-10))
return log_mel.astype(np.float32)
@dataclass(slots=True)
class FeatureExtractor:
"""Stream clips into ``train.bin`` / ``train.dat``."""
sample_rate: int = 16000
n_mels: int = 80
n_fft: int = 400
hop_length: int = 160
split: str = "train"
def extract_dataset(
self,
clip_paths: Iterable[str | Path],
features_dir: str | Path,
) -> Path:
"""Write features for all ``clip_paths``. Returns the manifest path."""
features_dir = ensure_dir(features_dir)
bin_path = features_dir / f"{self.split}.bin"
dat_path = features_dir / f"{self.split}.dat"
offset = 0
n = 0
with bin_path.open("wb") as bin_fh, dat_path.open("w", encoding="utf-8") as dat_fh:
for clip_path in clip_paths:
clip_path = Path(clip_path)
samples, sr = load_wav(clip_path, sample_rate=self.sample_rate)
mel = compute_mel_spectrogram(
samples, sr, n_mels=self.n_mels, n_fft=self.n_fft, hop_length=self.hop_length
)
audio_f32 = samples.astype("<f4", copy=False)
mel_f32 = mel.astype("<f4", copy=False)
audio_bytes = audio_f32.tobytes()
mel_bytes = mel_f32.tobytes()
bin_fh.write(audio_bytes)
bin_fh.write(mel_bytes)
audio_offset = offset
mel_offset = offset + len(audio_bytes)
record = {
"id": clip_path.stem,
"audio": clip_path.name,
"sample_rate": sr,
"duration": round(samples.shape[0] / sr, 4),
"audio_offset": audio_offset,
"audio_length": int(audio_f32.size),
"mel_offset": mel_offset,
"mel_shape": list(mel_f32.shape),
"dtype": "float32",
}
dat_fh.write(json.dumps(record))
dat_fh.write("\n")
offset = mel_offset + len(mel_bytes)
n += 1
logger.info("Extracted features for %d clips -> %s", n, bin_path)
return dat_path
def read_feature_record(
bin_path: str | Path, record: dict
) -> tuple[np.ndarray, np.ndarray]:
"""Read one clip's ``(audio, mel)`` arrays back from the binary store."""
with Path(bin_path).open("rb") as fh:
fh.seek(record["audio_offset"])
audio = np.frombuffer(
fh.read(record["audio_length"] * _FLOAT_BYTES), dtype="<f4"
).copy()
fh.seek(record["mel_offset"])
mel_count = int(np.prod(record["mel_shape"]))
mel = np.frombuffer(
fh.read(mel_count * _FLOAT_BYTES), dtype="<f4"
).reshape(record["mel_shape"]).copy()
return audio, mel
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