IQ.Pilot Release Commit @ bec7652

artifacts/package_sources/tinygrad/examples/audio_helpers.py

@@ -0,0 +1,79 @@
+from typing import Optional
+from tinygrad import Tensor
+from tinygrad.dtype import DTypeLike, dtypes
+import math
+
+# rewritten from numpy
+def rfftfreq(n: int, d: float = 1.0) -> Tensor:
+  val = 1.0 / (n * d)
+  N = n // 2 + 1
+  results = Tensor.arange(N)
+  return results * val
+
+# just like in librosa
+def fft_frequencies(sr: float, n_fft: int) -> Tensor:
+  return rfftfreq(n=n_fft, d=1.0 / sr)
+
+def hz_to_mel(freq: Tensor) -> Tensor:
+  # linear part
+  f_min = 0.0
+  f_sp = 200.0 / 3
+  mels = (freq - f_min) / f_sp
+
+  # log-scale part
+  min_log_hz = 1000.0  # beginning of log region (Hz)
+  mask = freq >= min_log_hz
+  return mask.where(((min_log_hz - f_min) / f_sp) + (freq / min_log_hz).log() / (math.log(6.4) / 27.0), mels)
+
+def mel_to_hz(mels: Tensor) -> Tensor:
+  # linear scale
+  f_min = 0.0
+  f_sp = 200.0 / 3
+  freqs = f_min + f_sp * mels
+
+  # nonlinear scale
+  min_log_hz = 1000.0  # beginning of log region (Hz)
+  min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
+  logstep = math.log(6.4) / 27.0  # step size for log region
+
+  log_t = mels >= min_log_mel
+  freqs = log_t.where(min_log_hz * ((logstep * (mels - min_log_mel)).exp()), freqs)
+  return freqs
+
+def mel_frequencies(n_mels: int = 128, *, fmin: float = 0.0, fmax: float = 11025.0) -> Tensor:
+  # center freqs of mel bands - uniformly spaced between limits
+  min_max_mel = hz_to_mel(Tensor([fmin, fmax]))
+
+  mels = Tensor.linspace(min_max_mel[0], min_max_mel[1], n_mels)
+  hz = mel_to_hz(mels)
+  return hz
+
+def mel(
+  *,
+  sr: float,
+  n_fft: int,
+  n_mels: int = 128,
+  fmin: float = 0.0,
+  fmax: Optional[float] = None,
+  dtype: DTypeLike = dtypes.default_float,
+) -> Tensor:
+  if fmax is None:
+    fmax = float(sr) / 2
+
+  n_mels = int(n_mels)
+
+  fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft)  # center freqs of each FFT bin
+  mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax)  # center freqs of mel bands
+
+  fdiff = mel_f[1:] - mel_f[:-1]
+  ramps = mel_f[None].T.expand(-1, fftfreqs.shape[-1]) - fftfreqs
+
+  lower = -ramps[:n_mels] / fdiff[:n_mels][None].T
+  upper = ramps[2 : n_mels + 2] / fdiff[1 : n_mels + 1][None].T
+  weights = lower.minimum(upper).maximum(0)
+
+  # Slaney-style mel is scaled to be approx constant energy per channel
+  enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
+  weights *= enorm[:, None]
+
+  return weights