[aubio-user] Fwd: aubio help

[Илья]

> Начало переадресованного сообщения:
> 
> Отправитель: Илья <ilya at perevoznik.name <mailto:ilya at perevoznik.name>>
> Тема: aubio help
> Дата: 22 ноября 2016 г., 19:25:41 GMT+3
> Получатель: piem at piem.org <mailto:piem at piem.org>
> 
> Hello, i’m coding app, that use `aubio` in it and i want to make a time-stretch of sound. (i’m using python module)
> 
> i found a demo (in you’re python/demos folder) that actually what I’m looking for, but i need to rewrite it to compatibility with my already existing functions etc.
> I have a trouble with sink_out, i tried to look in clang code of it, but anyway i can’t understand, but i can’t use it cause i have my own file saving system. i tried to emulate sink_out with a list (that would convert to ndarray after stretching) and it doesn’t works, cuz i have just 1279 samples at the out, but at in it was 352800 :D
> 
> please, help me to understand how to rewrite it without sink_out. i need to write all to ndarray. my code below:
> 
> def time_stretch(self, rate):
>     win_s = self._win_size
>     hop_s = self._hop_size
> 
>     warmup = win_s // hop_s - 1
> 
>     p = pvoc(win_s, hop_s)
> 
>     # allocate memory to store norms and phases
>     n_blocks = len(self._samples) // hop_s + 1
>     # adding an empty frame at end of spectrogram
>     norms = np.zeros((n_blocks + 1, win_s // 2 + 1), dtype=float_type)
>     phases = np.zeros((n_blocks + 1, win_s // 2 + 1), dtype=float_type)
> 
>     block_read = 0
>     samples = self._samples
>     steps_max = len(samples) - (len(samples) % hop_s)
> 
>     for i in range(0, steps_max, hop_s):
>         # pitchin = pitch_o(samples[i:i + self._hop_size])
>         # read from source
>         samples_proc = np.asarray(samples[i:i + hop_s]).astype(np.float32)
>         # compute fftgrain
>         spec = p(samples_proc)
>         # store current grain
>         norms[block_read] = spec.norm
>         phases[block_read] = spec.phas
>         # increment block counter
>         block_read += 1
> 
>     # just to make sure
>     # source_in.close()
> 
>     sink_out = []
> 
>     # interpolated time steps (j = alpha * i)
>     steps = np.arange(0, n_blocks, rate, dtype=float_type)
>     # initial phase
>     phas_acc = phases[0]
>     # excepted phase advance in each bin
>     phi_advance = np.linspace(0, np.pi * hop_s, win_s / 2 + 1).astype(float_type)
> 
>     new_grain = cvec(win_s)
> 
>     for (t, step) in enumerate(steps):
> 
>         frac = 1. - np.mod(step, 1.0)
>         # get pair of frames
>         t_norms = norms[int(step):int(step + 2)]
>         t_phases = phases[int(step):int(step + 2)]
> 
>         # compute interpolated frame
>         new_grain.norm = frac * t_norms[0] + (1. - frac) * t_norms[1]
>         new_grain.phas = phas_acc
>         # print t, step, new_grain.norm
>         # print t, step, phas_acc
> 
>         # psola
>         samples_proc = p.rdo(new_grain)
>         if t > warmup:  # skip the first few frames to warm up phase vocoder
>             # write to sink
>             sink_out.append(samples_proc[:hop_s])
> 
>         # calculate phase advance
>         dphas = t_phases[1] - t_phases[0] - phi_advance
>         # unwrap angle to [-pi; pi]
>         dphas = unwrap2pi(dphas)
>         # cumulate phase, to be used for next frame
>         phas_acc += phi_advance + dphas
> 
>     for t in range(warmup + 1):  # purge the last frames from the phase vocoder
>         new_grain.norm[:] = 0
>         new_grain.phas[:] = 0
>         samples_proc = p.rdo(new_grain)
>         sink_out.append(samples_proc[:hop_s])
> 
>     self._samples = np.asarray(sink_out).astype(np.int16)
> P.S. Sorry for my bad English.
> 
> Thank You!

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