Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
## -----------------------------------------------------------------------------
# URLs_SPEAKERS10()
# path_dig = 'SPEAKERS10'
## -----------------------------------------------------------------------------
# audio_extensions()[1:6]
# #[1] ".aif" ".aifc" ".aiff" ".au" ".m3u" ".mp2"
## -----------------------------------------------------------------------------
# fnames = get_files(path_dig, extensions = audio_extensions())
# # (#3842) [Path('SPEAKERS10/f0004_us_f0004_00414.wav')...]
## -----------------------------------------------------------------------------
# at = AudioTensor_create(fnames[0])
# at; at$shape
# at %>% show() %>% plot(dpi = 200)
## -----------------------------------------------------------------------------
# cfg = Voice()
#
# cfg$f_max; cfg$sample_rate
# #[1] 8000 # frequency range
# #[1] 16000 # the sampling rate
## -----------------------------------------------------------------------------
# aud2spec = AudioToSpec_from_cfg(cfg)
#
# crop1s = ResizeSignal(1000)
## -----------------------------------------------------------------------------
# pipe = Pipeline(list(AudioTensor_create, crop1s, aud2spec))
# pipe(fnames[0]) %>% show() %>% plot(dpi = 200)
## -----------------------------------------------------------------------------
# item_tfms = list(ResizeSignal(1000), aud2spec)
#
# get_y = function(x) substring(x$name[1],1,1)
#
# aud_digit = DataBlock(blocks = list(AudioBlock(), CategoryBlock()),
# get_items = get_audio_files,
# splitter = RandomSplitter(),
# item_tfms = item_tfms,
# get_y = get_y)
#
# dls = aud_digit %>% dataloaders(source = path_dig, bs = 64)
#
# dls %>% show_batch(figsize = c(15, 8.5), nrows = 3, ncols = 3, max_n = 9, dpi = 180)
## -----------------------------------------------------------------------------
# torch = torch()
# nn = nn()
#
# learn = Learner(dls, xresnet18(pretrained = FALSE), nn$CrossEntropyLoss(), metrics=accuracy)
#
# # channel from 3 to 1
# learn$model[0][0][['in_channels']] %f% 1L
# # reshape
# new_weight_shape <- torch$nn$parameter$Parameter(
# (learn$model[0][0]$weight %>% narrow('[:,1,:,:]'))$unsqueeze(1L))
#
# # assign with %f%
# learn$model[0][0][['weight']] %f% new_weight_shape
## -----------------------------------------------------------------------------
# lrs = learn %>% lr_find()
# #SuggestedLRs(lr_min=0.03019951581954956, lr_steep=0.0030199517495930195)
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(10, 1e-3)
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