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bbb0e68 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 | """Audio processing utilities for KugelAudio."""
import os
from typing import Optional, Union, List, Dict, Any
import numpy as np
import torch
from transformers.feature_extraction_utils import FeatureExtractionMixin
from transformers.utils import logging
logger = logging.get_logger(__name__)
class AudioNormalizer:
"""Normalize audio to target dB FS level.
This ensures consistent input levels for the model while
maintaining audio quality and avoiding clipping.
"""
def __init__(self, target_dB_FS: float = -25, eps: float = 1e-6):
self.target_dB_FS = target_dB_FS
self.eps = eps
def normalize_db(self, audio: np.ndarray) -> tuple:
"""Adjust audio to target dB FS level."""
rms = np.sqrt(np.mean(audio**2))
scalar = 10 ** (self.target_dB_FS / 20) / (rms + self.eps)
return audio * scalar, rms, scalar
def avoid_clipping(self, audio: np.ndarray) -> tuple:
"""Scale down if necessary to avoid clipping."""
max_val = np.max(np.abs(audio))
if max_val > 1.0:
scalar = max_val + self.eps
return audio / scalar, scalar
return audio, 1.0
def __call__(self, audio: np.ndarray) -> np.ndarray:
"""Normalize audio: adjust dB FS then avoid clipping."""
audio, _, _ = self.normalize_db(audio)
audio, _ = self.avoid_clipping(audio)
return audio
class AudioProcessor(FeatureExtractionMixin):
"""Processor for audio preprocessing and postprocessing.
Handles:
- Audio format conversion (stereo to mono)
- Normalization
- Loading from various file formats
- Saving to WAV files
Example:
>>> processor = AudioProcessor(sampling_rate=24000)
>>> audio = processor("path/to/audio.wav")
>>> processor.save_audio(generated_audio, "output.wav")
"""
model_input_names = ["input_features"]
def __init__(
self,
sampling_rate: int = 24000,
normalize_audio: bool = True,
target_dB_FS: float = -25,
eps: float = 1e-6,
**kwargs,
):
super().__init__(**kwargs)
self.sampling_rate = sampling_rate
self.normalize_audio = normalize_audio
self.normalizer = AudioNormalizer(target_dB_FS, eps) if normalize_audio else None
self.feature_extractor_dict = {
"sampling_rate": sampling_rate,
"normalize_audio": normalize_audio,
"target_dB_FS": target_dB_FS,
"eps": eps,
}
def _ensure_mono(self, audio: np.ndarray) -> np.ndarray:
"""Convert stereo to mono if needed."""
if len(audio.shape) == 1:
return audio
elif len(audio.shape) == 2:
if audio.shape[0] == 2:
return np.mean(audio, axis=0)
elif audio.shape[1] == 2:
return np.mean(audio, axis=1)
elif audio.shape[0] == 1:
return audio.squeeze(0)
elif audio.shape[1] == 1:
return audio.squeeze(1)
else:
raise ValueError(f"Unexpected audio shape: {audio.shape}")
else:
raise ValueError(f"Audio should be 1D or 2D, got shape: {audio.shape}")
def _process_single(self, audio: Union[np.ndarray, List[float]]) -> np.ndarray:
"""Process a single audio array."""
if not isinstance(audio, np.ndarray):
audio = np.array(audio, dtype=np.float32)
else:
audio = audio.astype(np.float32)
audio = self._ensure_mono(audio)
if self.normalize_audio and self.normalizer:
audio = self.normalizer(audio)
return audio
def _load_from_path(self, audio_path: str) -> np.ndarray:
"""Load audio from file path."""
ext = os.path.splitext(audio_path)[1].lower()
if ext in [".wav", ".mp3", ".flac", ".m4a", ".ogg"]:
import librosa
audio, _ = librosa.load(audio_path, sr=self.sampling_rate, mono=True)
return audio
elif ext == ".pt":
tensor = torch.load(audio_path, map_location="cpu", weights_only=True).squeeze()
return tensor.numpy().astype(np.float32)
elif ext == ".npy":
return np.load(audio_path).astype(np.float32)
else:
raise ValueError(f"Unsupported format: {ext}")
def __call__(
self,
audio: Union[str, np.ndarray, List[float], List[np.ndarray], List[str]] = None,
sampling_rate: Optional[int] = None,
return_tensors: Optional[str] = None,
**kwargs,
) -> Dict[str, Any]:
"""Process audio input(s).
Args:
audio: Audio input - path, array, or list of either
sampling_rate: Input sampling rate (for validation)
return_tensors: Return format ("pt" for PyTorch, "np" for NumPy)
Returns:
Dictionary with processed audio
"""
if audio is None:
raise ValueError("Audio input is required")
if sampling_rate is not None and sampling_rate != self.sampling_rate:
logger.warning(
f"Input sampling rate ({sampling_rate}) differs from expected ({self.sampling_rate}). "
"Please resample your audio."
)
# Handle different input types
if isinstance(audio, str):
audio = self._load_from_path(audio)
is_batched = False
elif isinstance(audio, list):
if all(isinstance(item, str) for item in audio):
audio = [self._load_from_path(p) for p in audio]
is_batched = True
else:
is_batched = isinstance(audio[0], (np.ndarray, list))
else:
is_batched = False
# Process
if is_batched:
processed = [self._process_single(a) for a in audio]
else:
processed = [self._process_single(audio)]
# Convert to tensors
if return_tensors == "pt":
if len(processed) == 1:
features = torch.from_numpy(processed[0]).unsqueeze(0).unsqueeze(1)
else:
features = torch.stack([torch.from_numpy(a) for a in processed]).unsqueeze(1)
elif return_tensors == "np":
if len(processed) == 1:
features = processed[0][np.newaxis, np.newaxis, :]
else:
features = np.stack(processed)[:, np.newaxis, :]
else:
features = processed[0] if len(processed) == 1 else processed
return {"audio": features}
def save_audio(
self,
audio: Union[torch.Tensor, np.ndarray, List],
output_path: str = "output.wav",
sampling_rate: Optional[int] = None,
normalize: bool = False,
batch_prefix: str = "audio_",
) -> List[str]:
"""Save audio to WAV file(s).
Args:
audio: Audio data to save
output_path: Output path (directory for batched audio)
sampling_rate: Sampling rate (defaults to processor's rate)
normalize: Whether to normalize before saving
batch_prefix: Prefix for batch files
Returns:
List of saved file paths
"""
import soundfile as sf
if sampling_rate is None:
sampling_rate = self.sampling_rate
# Convert to numpy
if isinstance(audio, torch.Tensor):
audio_np = audio.float().detach().cpu().numpy()
elif isinstance(audio, list):
if all(isinstance(a, torch.Tensor) for a in audio):
audio_np = [a.float().detach().cpu().numpy() for a in audio]
else:
audio_np = audio
else:
audio_np = audio
saved_paths = []
if isinstance(audio_np, list):
os.makedirs(output_path, exist_ok=True)
for i, item in enumerate(audio_np):
item = self._prepare_for_save(item, normalize)
path = os.path.join(output_path, f"{batch_prefix}{i}.wav")
sf.write(path, item, sampling_rate)
saved_paths.append(path)
elif len(audio_np.shape) >= 3 and audio_np.shape[0] > 1:
os.makedirs(output_path, exist_ok=True)
for i in range(audio_np.shape[0]):
item = audio_np[i].squeeze()
item = self._prepare_for_save(item, normalize)
path = os.path.join(output_path, f"{batch_prefix}{i}.wav")
sf.write(path, item, sampling_rate)
saved_paths.append(path)
else:
item = self._prepare_for_save(audio_np.squeeze(), normalize)
sf.write(output_path, item, sampling_rate)
saved_paths.append(output_path)
return saved_paths
def _prepare_for_save(self, audio: np.ndarray, normalize: bool) -> np.ndarray:
"""Prepare audio for saving."""
if len(audio.shape) > 1 and audio.shape[0] == 1:
audio = audio.squeeze(0)
if normalize:
max_val = np.abs(audio).max()
if max_val > 0:
audio = audio / max_val
return audio
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for serialization."""
return self.feature_extractor_dict
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