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Running on Zero
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import os
import random
import logging
import yaml
import numpy as np
from contextlib import contextmanager
import torch
from speakerlab.utils.fileio import load_yaml
def parse_config(config_file):
if config_file.endwith('.yaml'):
config = load_yaml(config_file)
else:
raise Exception("Other formats not currently supported.")
return config
def set_seed(seed=0):
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = True
def get_logger(fpath=None, fmt=None):
if fmt is None:
fmt = "%(asctime)s - %(levelname)s: %(message)s"
logging.basicConfig(level=logging.INFO, format=fmt)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
if fpath is not None:
handler = logging.FileHandler(fpath)
handler.setFormatter(logging.Formatter(fmt))
logger.addHandler(handler)
return logger
def get_utt2spk_dict(utt2spk, suffix=''):
temp_dict={}
with open(utt2spk,'r') as utt2spk_f:
lines = utt2spk_f.readlines()
for i in lines:
i=i.strip().split()
if suffix == '' or suffix is None:
key_i = i[0]
value_spk = i[1]
else:
key_i = i[0]+'_'+suffix
value_spk = i[1]+'_'+suffix
if key_i in temp_dict:
raise ValueError('The key must be unique.')
temp_dict[key_i]=value_spk
return temp_dict
def get_wavscp_dict(wavscp, suffix=''):
temp_dict={}
with open(wavscp, 'r') as wavscp_f:
lines = wavscp_f.readlines()
for i in lines:
i=i.strip().split()
if suffix == '' or suffix is None:
key_i = i[0]
else:
key_i = i[0]+'_'+suffix
value_path = i[1]
if key_i in temp_dict:
raise ValueError('The key must be unique.')
temp_dict[key_i]=value_path
return temp_dict
def accuracy(x, target):
# x: [*, C], target: [*,]
_, pred = x.topk(1)
pred = pred.squeeze(-1)
acc = pred.eq(target).float().mean()
return acc*100
def average_precision(scores, labels):
# scores: [N, ], labels: [N, ]
if torch.is_tensor(scores):
scores = scores.cpu().numpy()
if torch.is_tensor(labels):
labels = labels.cpu().numpy()
if isinstance(scores, list):
scores = np.array(scores)
if isinstance(labels, list):
labels = np.array(labels)
assert isinstance(scores, np.ndarray) and isinstance(
labels, np.ndarray), 'Input should be numpy.array.'
assert len(scores.shape)==1 and len(labels.shape)==1 and \
scores.shape[0]==labels.shape[0]
sort_idx = np.argsort(scores)[::-1]
scores = scores[sort_idx]
labels = labels[sort_idx]
tp_count = (labels==1).sum()
tp = labels.cumsum()
recall = tp / tp_count
precision = tp / (np.arange(len(labels)) + 1)
recall = np.concatenate([[0], recall, [1]])
precision = np.concatenate([[0], precision, [0]])
# Smooth precision to be monotonically decreasing.
for i in range(len(precision) - 2, -1, -1):
precision[i] = np.maximum(precision[i], precision[i + 1])
indices = np.where(recall[1:] != recall[:-1])[0] + 1
average_precision = np.sum(
(recall[indices] - recall[indices - 1]) * precision[indices])
return average_precision
def load_params(dst_model, src_state, strict=True):
dst_state = {}
for k in src_state:
if k.startswith('module'):
dst_state[k[7:]] = src_state[k]
else:
dst_state[k] = src_state[k]
dst_model.load_state_dict(dst_state, strict=strict)
return dst_model
def merge_vad(vad1: list, vad2: list):
intervals = vad1 + vad2
intervals.sort(key=lambda x: x[0])
merged = []
for interval in intervals:
if not merged or merged[-1][1] < interval[0]:
merged.append(interval)
else:
merged[-1][1] = max(merged[-1][1], interval[1])
return merged
class AverageMeter(object):
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class AverageMeters(object):
def __init__(self, names: list = None, fmts: list = None):
self.cont = dict()
if names is None or fmts is None:
return
for name, fmt in zip(names, fmts):
self.cont[name] = AverageMeter(name, fmt)
def add(self, name, fmt=':f'):
self.cont[name] = AverageMeter(name, fmt)
def update(self, name, val, n=1):
self.cont[name].update(val, n)
def avg(self, name):
return self.cont[name].avg
def val(self, name):
return self.cont[name].val
def __str__(self):
return '\t'.join([str(s) for s in self.cont.values()])
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(self.meters)]
return '\t'.join(entries)
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
@contextmanager
def silent_print():
original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
try:
yield
finally:
sys.stdout.close()
sys.stdout = original_stdout
def download_model_from_modelscope(model_id, model_revision=None, cache_dir=None):
from modelscope.hub.snapshot_download import snapshot_download
if cache_dir is None:
cache_dir = snapshot_download(
model_id,
revision=model_revision,
)
else:
cfg_file = os.path.join(cache_dir, model_id, 'configuration.json')
if not os.path.exists(cfg_file):
cache_dir = snapshot_download(
model_id,
revision=model_revision,
cache_dir=cache_dir,
)
else:
cache_dir = os.path.join(cache_dir, model_id)
return cache_dir
def circle_pad(x: torch.Tensor, target_len, dim=0):
xlen = x.shape[dim]
if xlen >= target_len:
return x
n = int(np.ceil(target_len/xlen))
xcat = torch.cat([x for _ in range(n)], dim=dim)
return torch.narrow(xcat, dim, 0, target_len)
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