diff options
| author | Jules Laplace <julescarbon@gmail.com> | 2018-05-15 00:15:41 +0200 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2018-05-15 00:15:41 +0200 |
| commit | f76afe8416d84c50742836049eaf6598b3c2b964 (patch) | |
| tree | 4cc4b5c2a364561f58c5bb0e4d0d5b967a6d2c78 /model.py | |
| parent | 14d69ee61cab3549a71b24a419c4e044ec789e3f (diff) | |
lol typo
Diffstat (limited to 'model.py')
| -rw-r--r-- | model.py | 11 |
1 files changed, 9 insertions, 2 deletions
@@ -325,8 +325,9 @@ class PrimedGenerator(Runner): tmp_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 + q_width = q_levels >> 4 + q_min = q_width + q_max = q_levels - q_width print("_______-___-_---_-____") print("_____________--_-_-_______") @@ -343,11 +344,17 @@ class PrimedGenerator(Runner): for i in range(n_samples): x[:, i] = int((math.sin(i/44100 * primer_freq) + 1) / 2 * (q_max - q_min) + q_min) return x + def _saw(x): + primer_freq = float(prime_param_a) + for i in range(n_samples): + x[:, i] = int((math.sin(i/44100 * primer_freq) + 1) / 2 * (q_max - q_min) + q_min) + return x sequence_lookup = { 'zero': lambda x: x.fill_(utils.q_zero(self.model.q_levels)), 'noise': _noise, 'sin': _sin, + 'saw': _saw, } sequences = sequence_lookup.get(primer, 'zero')(sequences) |
