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7c89374 | 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 | # feature_extraction_mini_whisper.py
import numpy as np
import torch
class MiniWhisperFeatureExtractor:
"""Feature extractor matching Whisper exactly."""
model_input_names = ["input_features"]
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length=30,
n_fft=400,
padding_value=0.0,
dither=0.0,
return_attention_mask=False,
**kwargs,
):
self.feature_size = feature_size
self.sampling_rate = sampling_rate
self.hop_length = hop_length
self.chunk_length = chunk_length
self.n_fft = n_fft
self.n_samples = chunk_length * sampling_rate
self.nb_max_frames = self.n_samples // hop_length
self.padding_value = padding_value
self.dither = dither
self.return_attention_mask = return_attention_mask
self.mel_filters = self._mel_filter_bank()
def _mel_filter_bank(self):
"""Create mel filter bank matching Whisper's parameters."""
try:
from torchaudio.functional import melscale_fbanks
n_freq_bins = 1 + self.n_fft // 2
mel_filters = melscale_fbanks(
n_freqs=n_freq_bins,
f_min=0.0,
f_max=8000.0,
n_mels=self.feature_size,
sample_rate=self.sampling_rate,
norm="slaney",
mel_scale="slaney",
)
return mel_filters.numpy()
except ImportError:
return self._numpy_mel_filter_bank()
def _numpy_mel_filter_bank(self):
"""NumPy fallback for mel filter bank."""
def hz_to_mel(hz):
return 2595 * np.log10(1 + hz / 700)
def mel_to_hz(mel):
return 700 * (10 ** (mel / 2595) - 1)
n_freq_bins = 1 + self.n_fft // 2
fmin, fmax = 0.0, 8000.0
mel_min = hz_to_mel(fmin)
mel_max = hz_to_mel(fmax)
mel_points = np.linspace(mel_min, mel_max, self.feature_size + 2)
hz_points = mel_to_hz(mel_points)
bin_points = np.floor((self.n_fft + 1) * hz_points / self.sampling_rate).astype(int)
filters = np.zeros((self.feature_size, n_freq_bins))
for i in range(self.feature_size):
left, center, right = bin_points[i], bin_points[i + 1], bin_points[i + 2]
for j in range(left, center):
filters[i, j] = (j - left) / (center - left)
for j in range(center, right):
filters[i, j] = (right - j) / (right - center)
filters = filters / (filters.sum(axis=1, keepdims=True) + 1e-10)
return filters
def _np_extract_fbank_features(self, waveform_batch, device="cpu"):
"""NumPy implementation matching Whisper exactly."""
log_spec_batch = []
for waveform in waveform_batch:
# Apply dither
if self.dither != 0.0:
waveform = waveform + self.dither * np.random.randn(*waveform.shape).astype(np.float32)
# Compute STFT
n_frames = 1 + (len(waveform) - self.n_fft) // self.hop_length
spec = np.zeros((self.n_fft // 2 + 1, n_frames), dtype=np.float32)
window = np.hanning(self.n_fft).astype(np.float32)
for i in range(n_frames):
start = i * self.hop_length
frame = waveform[start:start + self.n_fft]
if len(frame) < self.n_fft:
frame = np.pad(frame, (0, self.n_fft - len(frame)))
spec[:, i] = np.abs(np.fft.rfft(frame * window)) ** 2
# Apply mel filters: mel_filters is [n_mels, n_freqs]
mel_spec = self.mel_filters @ spec
# Log10 with floor
log_spec = np.log10(np.maximum(mel_spec, 1e-10))
# Whisper uses: np.maximum(log_spec, log_spec.max() - 8.0)
log_spec = np.maximum(log_spec, log_spec.max() - 8.0)
# Normalize: (log_spec + 4.0) / 4.0
log_spec = (log_spec + 4.0) / 4.0
# Remove last frame to match Whisper
log_spec = log_spec[:, :-1]
log_spec_batch.append(log_spec)
return np.array(log_spec_batch)
def _torch_extract_fbank_features(self, waveform, device="cpu"):
"""PyTorch implementation matching Whisper exactly."""
waveform = torch.from_numpy(waveform).to(device, torch.float32)
window = torch.hann_window(self.n_fft, device=device)
if self.dither != 0.0:
waveform += self.dither * torch.randn(waveform.shape, dtype=waveform.dtype, device=waveform.device)
stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2 # Remove last frame
# mel_filters shape: [n_mels, n_freqs] from mel_filter_bank
mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32)
# Transpose and matmul: mel_filters.T @ magnitudes
mel_spec = mel_filters.T @ magnitudes
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
if waveform.dim() == 2:
max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0]
log_spec = torch.maximum(log_spec, max_val - 8.0)
else:
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
if device != "cpu":
log_spec = log_spec.detach().cpu()
return log_spec.numpy()
@staticmethod
def zero_mean_unit_var_norm(input_values, attention_mask, padding_value=0.0):
if attention_mask is not None:
attention_mask = np.array(attention_mask, np.int32)
normed = []
for vector, length in zip(input_values, attention_mask.sum(-1)):
normed_slice = (vector - vector[:length].mean()) / (np.sqrt(vector[:length].var() + 1e-7))
if length < normed_slice.shape[0]:
normed_slice[length:] = padding_value
normed.append(normed_slice)
else:
normed = [(x - x.mean()) / (np.sqrt(x.var() + 1e-7)) for x in input_values]
return normed
def __call__(
self,
raw_speech,
sampling_rate=None,
truncation=True,
return_tensors="pt",
return_attention_mask=None,
do_normalize=False,
**kwargs,
):
if sampling_rate is not None and sampling_rate != self.sampling_rate:
raise ValueError(f"Expected {self.sampling_rate}, got {sampling_rate}")
is_batched = isinstance(raw_speech, list) or (isinstance(raw_speech, np.ndarray) and raw_speech.ndim > 1)
if not is_batched:
raw_speech = [raw_speech]
# Convert to numpy float32
processed = []
for audio in raw_speech:
if isinstance(audio, np.ndarray) and audio.dtype == np.float64:
audio = audio.astype(np.float32)
elif not isinstance(audio, np.ndarray):
audio = np.array(audio, dtype=np.float32)
# Pad or truncate
if len(audio) > self.n_samples:
if truncation:
audio = audio[:self.n_samples]
elif len(audio) < self.n_samples:
audio = np.pad(audio, (0, self.n_samples - len(audio)), constant_values=self.padding_value)
processed.append(audio)
waveform_batch = np.stack(processed, axis=0)
# Use torch if available
if torch.cuda.is_available():
input_features = self._torch_extract_fbank_features(waveform_batch, device="cuda")
# Result is [batch, n_mels, n_frames]
else:
input_features = self._np_extract_fbank_features(waveform_batch)
result = {"input_features": input_features}
if return_attention_mask is None:
return_attention_mask = self.return_attention_mask
if return_attention_mask:
result["attention_mask"] = np.ones(input_features.shape[0], dtype=np.int64)
if return_tensors == "pt":
result["input_features"] = torch.from_numpy(input_features).float()
if "attention_mask" in result:
result["attention_mask"] = torch.from_numpy(result["attention_mask"]).long()
return result
def to_dict(self):
return {
"feature_size": self.feature_size,
"sampling_rate": self.sampling_rate,
"hop_length": self.hop_length,
"n_fft": self.n_fft,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"dither": self.dither,
}
def save_pretrained(self, save_directory):
import json, os
os.makedirs(save_directory, exist_ok=True)
with open(os.path.join(save_directory, "preprocessor_config.json"), "w") as f:
json.dump(self.to_dict(), f, indent=2)
@classmethod
def from_pretrained(cls, save_directory):
import json, os
path = os.path.join(save_directory, "preprocessor_config.json")
if os.path.exists(path):
with open(path) as f:
return cls(**json.load(f))
return cls()
__all__ = ["MiniWhisperFeatureExtractor"] |