diff options
| -rwxr-xr-x | gen-prime.sh | 61 | ||||
| -rw-r--r-- | generate.py | 20 | ||||
| -rwxr-xr-x | get.pl | 29 | ||||
| -rw-r--r-- | model.py | 13 | ||||
| -rw-r--r-- | trainer/plugins.py | 7 |
5 files changed, 109 insertions, 21 deletions
diff --git a/gen-prime.sh b/gen-prime.sh new file mode 100755 index 0000000..bbdee92 --- /dev/null +++ b/gen-prime.sh @@ -0,0 +1,61 @@ +function gen_prime () { + exp_name=${1%.*} + n_samples=$2 + sample_length=$3 + primer=$4 + primer_a=${5:-"0"} + primer_b=${6:-"0"} + recursive=${7:-"False"} + + if [ -z ${var+x} ]; then echo "var is unset" + + sleep 0.1 + echo "" + echo "___________________________________________________" + echo ">> generating $exp_name" + echo "" + python generate.py \ + --exp $exp_name --dataset $exp_name \ + --frame_sizes 8 2 \ + --n_rnn 2 --dim 1024 --q_levels 256 \ + --seq_len 1024 --batch_size 128 \ + --val_frac 0.1 --test_frac 0.1 \ + --sample_rate 44100 \ + --sample_length $sample_length \ + --keep_old_checkpoints False \ + --n_samples $n_samples \ + --epoch_limit 1 \ + --primer $primer \ + --primer_a $primer_a \ + --primer_b $primer_b \ + --recursive $recursive \ + --resume True + + tag="${primer}_${sample_length}" + if [ "$primer_a" -ne "0" ] + then + tag="${tag}_${primer_a}" + fi + if [ "$primer_b" -ne "0" ] + then + tag="${tag}_${primer_b}" + fi + if [ "$recursive" -eq "True" ] + then + tag="${tag}_recursive" + fi + ./latest.pl -l $tag $1 +} + +function gen_prime_set () { + exp_name=$1 + gen_prime $1 6 44100 'zero' + gen_prime $1 6 44100 'noise' + gen_prime $1 6 44100 'sin' 440 + gen_prime $1 6 44100 'noise' 0 0 True + gen_prime $1 6 44100 'sin' 440 0 True +} + +gen_prime_set jwcglassbeat +gen_prime_set neuralacid_solo +gen_prime_set snaps_grain diff --git a/generate.py b/generate.py index c4059ec..f51b586 100644 --- a/generate.py +++ b/generate.py @@ -52,7 +52,10 @@ default_params = { 'loss_smoothing': 0.99, 'cuda': True, 'comet_key': None, - 'primer': '' + 'primer': '', + 'primer_a': '', + 'primer_b': '', + 'recursive': True, } tag_params = [ @@ -224,7 +227,8 @@ def main(exp, frame_sizes, dataset, **params): trainer.register_plugin(GeneratorPlugin( os.path.join(results_path, 'samples'), params['n_samples'], params['sample_length'], params['sample_rate'], - params['primer'] + params['primer'], params['primer_a'], params['primer_b'], + params['recursive'], )) """ trainer.register_plugin( @@ -362,7 +366,17 @@ if __name__ == '__main__': '--comet_key', help='comet.ml API key' ) parser.add_argument( - '--primer', help='prime the generator...' + '--primer', help='prime the generator with a function. noise, sin, some others..' + ) + parser.add_argument( + '--primer_a', help='optional first parameter for primer' + ) + parser.add_argument( + '--primer_b', help='optional second parameter for primer' + ) + parser.add_argument( + '--recursive', type=parse_bool, default=True, + help='generate output recursively' ) parser.set_defaults(**default_params) @@ -2,6 +2,19 @@ $SIG{TERM} = $SIG{INT} = sub { exit 1 }; +sub sanitize ($) { + my $s = lc shift; + $s =~ s/\.[^.]+$//g; + $s =~ s/\W//g; + $s =~ s/\s+/_/g; + if (length($s) == 0) { + print "fn is empty"; + exit(1); + } + $s =~ s/_+/_/g; + return $s; +} + chdir('datasets'); my $fn, $new_fn; @@ -22,19 +35,10 @@ foreach my $s (@ARGV){ } } if ($fn) { - $new_fn = lc $fn; - $new_fn =~ s/\.flac$//g; - $new_fn =~ s/\s+/_/g; - $new_fn =~ s/\W//g; - if (length($new_fn) == 0) { - $new_fn = lc $s; - $new_fn =~ s/\s+/_/g; - $new_fn =~ s/\W//g; - } - $new_fn =~ s/_+/_/g; + $new_fn = sanitize($fn) $new_fn .= '.flac'; system('mv', $fn, $new_fn); - print"got fn, $fn => $new_fn\n"; + print "got fn, $fn => $new_fn\n"; system('/usr/bin/perl', 'dataset.pl', $new_fn); } } @@ -44,8 +48,9 @@ foreach my $s (@ARGV){ system('/usr/bin/wget', 'https://neural:spawn5@asdf.us/neural/' . $s); system('/usr/bin/perl', 'dataset.pl', $s); } + my $tag = sanitize($fn); open(my $fd, ">>../run_slap.sh"); - print $fd "standard $fn"; + print $fd "standard $tag\n"; close $fn; $fn = undef; @@ -310,7 +310,7 @@ class PrimedGenerator(Runner): super().__init__(model) self.cuda = cuda - def __call__(self, n_seqs, seq_len, primer): + def __call__(self, n_seqs, seq_len, primer, prime_param_a, prime_param_b, recursive): # generation doesn't work with CUDNN for some reason torch.backends.cudnn.enabled = False @@ -321,10 +321,14 @@ class PrimedGenerator(Runner): sequences = torch.LongTensor(n_seqs, n_samples) # 64-bit int frame_level_outputs = [None for _ in self.model.frame_level_rnns] + if recursive: + out_sequences = sequences + else: + out_sequences = torch.LongTensor(n_seqs, n_samples).fill_(utils.q_zero(self.model.q_levels)) + q_levels = self.model.q_levels q_min = 0 q_max = q_levels - primer_freq = 440 print("_______-___-_---_-____") print("_____________--_-_-_______") @@ -337,6 +341,7 @@ class PrimedGenerator(Runner): for i in xrange(n_samples): x[:, i] = random.triangular(q_min, q_max) def sin(x): + primer_freq = prime_param_a for i in xrange(n_samples): x[:, i] = (math.sin(i/44100 * primer_freq) + 1) / 2 * (q_max - q_min) + q_min @@ -387,8 +392,8 @@ class PrimedGenerator(Runner): sample_dist = self.model.sample_level_mlp( prev_samples, upper_tier_conditioning ).squeeze(1).exp_().data - sequences[:, i] = sample_dist.multinomial(1).squeeze(1) + out_sequences[:, i] = sample_dist.multinomial(1).squeeze(1) torch.backends.cudnn.enabled = True - return sequences[:, self.model.lookback :] + return out_sequences[:, self.model.lookback :] diff --git a/trainer/plugins.py b/trainer/plugins.py index 562355e..132c33d 100644 --- a/trainer/plugins.py +++ b/trainer/plugins.py @@ -143,13 +143,16 @@ class GeneratorPlugin(Plugin): pattern = 'd-{}-ep{}-s{}.wav' - def __init__(self, samples_path, n_samples, sample_length, sample_rate, primer): + def __init__(self, samples_path, n_samples, sample_length, sample_rate, primer, primer_a, primer_b, recursive): super().__init__([(1, 'epoch')]) self.samples_path = samples_path self.n_samples = n_samples self.sample_length = sample_length self.sample_rate = sample_rate self.primer = primer + self.primer_a = primer_a + self.primer_b = primer_b + self.recursive = recursive def register(self, trainer): if self.primer == "": @@ -158,7 +161,7 @@ class GeneratorPlugin(Plugin): self.generate = PrimedGenerator(trainer.model.model, trainer.cuda) def epoch(self, epoch_index): - samples = self.generate(self.n_samples, self.sample_length, self.primer) \ + samples = self.generate(self.n_samples, self.sample_length, self.primer, self.primer_a, self.primer_b, self.recursive) \ .cpu().float().numpy() for i in range(self.n_samples): write_wav( |