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Running on Zero
Running on Zero
File size: 2,053 Bytes
21e9df7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import sys
import os
import torch
import numpy as np
sys.path.insert(0, os.path.join(sys.path[0], "../../../.."))
def pad(x, max_len, dim, n_dim):
if x.shape[dim] < max_len:
if n_dim == 1:
x = np.pad(x, (0, max_len - x.shape[dim]),
"constant", constant_values=(0, 0))
elif n_dim == 2:
if dim == 0:
x = np.pad(x, ((0, max_len - x.shape[dim]), (0, 0)),
"constant", constant_values=(0, 0))
elif dim == 1:
x = np.pad(x, ((0, 0), (0, max_len - x.shape[dim])),
"constant", constant_values=(0, 0))
return x
def detect_silence(path, mode="wav"):
x = AudioSegment.from_file(path, mode)
dBFS = x.dBFS
sil = silence.detect_silence(x, min_silence_len=1000, silence_thresh=dBFS - 16)
if len(sil) == 0:
return 0, -1
x_len = x.duration_seconds
st = 0 if sil[0][0] > 0 else sil[0][1] / 1000.
ed = sil[-1][0] / 1000. if sil[-1][1] / 1000. >= x_len - 1 else -1
return st, ed
def mkdir(folder):
if not os.path.exists(folder):
os.mkdir(folder)
def listdir(folder, suffix=None):
outs = []
for f in os.listdir(folder):
if suffix is None or str.endswith(f, suffix):
outs.append(f)
return outs
def print_trainable_parameters(model):
trainable_params = 0
all_param = 0
for k, param in model.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
print(k)
trainable_params += num_params
print(
f"trainable params: {trainable_params:,d} || all params: {all_param:,d} || trainable%: {100 * trainable_params / all_param}"
)
def freeze(model):
for n, p in model.named_parameters():
p.requires_grad = False
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