if args.word_dict is not None:
fin = open(args.word_dict, 'r')
word_dic = {}
for line in fin:
tokens = line.split()
word_dic[''.join(tokens[1:])] = tokens[0]
device = torch.device('cuda')
mdic = torch.load(args.model_dic)
model = Seq2Seq(**mdic['params']).to(device)
model.load_state_dict(mdic['state'])
model.eval()
reader = ScpStreamReader(args.data_scp,
mean_sub=args.mean_sub,
downsample=args.downsample)
reader.initialize()
space, beam_size, max_len = args.space, args.beam_size, args.max_len
start, win, stable_time = args.start_block, args.incl_block, args.stable_time
head, padding = args.attn_head, args.attn_padding
since = time.time()
fctm = open(args.output, 'w')
total_latency = 0
count = 0
with torch.no_grad():
while True:
utt, mat = reader.read_next_utt()
if utt is None or utt == '': break
sort_src=True,
max_len=args.max_len,
max_utt=args.max_utt,
mean_sub=args.mean_sub,
zero_pad=args.zero_pad,
fp16=args.fp16,
shuffle=args.shuffle,
spec_drop=args.spec_drop,
spec_bar=args.spec_bar,
time_stretch=args.time_stretch,
time_win=args.time_win)
cv_reader = ScpStreamReader(args.valid_scp,
args.valid_target,
downsample=args.downsample,
sort_src=True,
max_len=args.max_len,
max_utt=args.max_utt,
mean_sub=args.mean_sub,
zero_pad=args.zero_pad,
fp16=args.fp16)
tensorboard_writer = None
if args.tensorboard:
from torch.utils.tensorboard import SummaryWriter
summary_name = '{}s2s-e{}d{}h{}'.format(args.tensorboard_dir,
args.n_enc, args.n_dec,
args.n_head)
if args.use_cnn:
summary_name += 'cnn'
tensorboard_writer = SummaryWriter(summary_name)
models = []
for dic_path in args.model_dic:
sdic = torch.load(dic_path[0])
m_params = sdic['params']
if sdic['type'] == 'lstm':
model = Seq2Seq(**m_params).to(device)
elif sdic['type'] == 'tf':
model = Transformer(**m_params).to(device)
model.load_state_dict(sdic['state'])
model.eval()
models.append(model)
model = Ensemble(models)
reader = ScpStreamReader(args.data_scp,
mean_sub=args.mean_sub,
downsample=args.downsample)
reader.initialize()
since = time.time()
batch_size = args.batch_size
fout = open(args.output, 'w')
while True:
src_seq, src_mask, utts = reader.read_batch_utt(batch_size)
if len(utts) == 0: break
with torch.no_grad():
src_seq, src_mask = src_seq.to(device), src_mask.to(device)
hypos, scores = Decoder.beam_search(model, src_seq, src_mask,
device, args.beam_size,
args.max_len)
hypos, scores = hypos.tolist(), scores.tolist()
dic = {}
fin = open(args.dict, 'r')
for line in fin:
tokens = line.split()
dic[int(tokens[1])] = tokens[0]
use_gpu = torch.cuda.is_available()
device = torch.device('cuda' if use_gpu else 'cpu')
mdic = torch.load(args.model_dic)
model = Seq2Seq(**mdic['params']).to(device)
model.load_state_dict(mdic['state'])
model.eval()
reader = ScpStreamReader(args.source,
mean_sub=args.mean_sub,
sort_src=True,
downsample=args.downsample)
reader.initialize()
space, beam_size, max_len = args.space, args.beam_size, args.max_len
win = args.win
with torch.no_grad():
while True:
#src_seq, src_mask, tgt_seq = reader.next_batch(1)
src_seq, src_mask, utts = reader.read_batch_utt(1)
if len(utts) == 0: break
utt = utts[0]
fout = open(utt + '.info', 'w')
time_len = src_seq.size(1)
#!/usr/bin/env python3
# encoding: utf-8
# Copyright 2019 Thai-Son Nguyen
# Licensed under the Apache License, Version 2.0 (the "License")
import os
import argparse
from pynn.io.kaldi_seq import ScpStreamReader
parser = argparse.ArgumentParser(description='pynn')
parser.add_argument('--data-scp', help='path to data scp', required=True)
parser.add_argument('--output',
help='output file',
type=str,
default='data.len')
if __name__ == '__main__':
args = parser.parse_args()
loader = ScpStreamReader(args.data_scp)
loader.initialize()
fout = open(args.output, 'w')
while True:
utt_id, utt_mat = loader.read_next_utt()
if utt_id is None or utt_id == '': break
fout.write(utt_id + u' ' + str(len(utt_mat)) + os.linesep)
fout.close()
tr_reader = ScpReader(args.train_scp,
args.train_target,
downsample=args.downsample,
max_len=args.max_len,
max_utt=args.max_utt,
shuffle=args.shuffle,
mean_sub=args.mean_sub,
fp16=args.fp16,
spec_drop=args.spec_drop,
spec_bar=args.spec_bar,
time_stretch=args.time_stretch,
time_win=args.time_win)
cv_reader = ScpStreamReader(args.valid_scp,
args.valid_target,
downsample=args.downsample,
mean_sub=args.mean_sub,
max_len=args.max_len,
max_utt=args.max_utt,
fp16=args.fp16)
cfg = {
'model_path': args.model_path,
'lr': args.lr,
'label_smooth': args.label_smooth,
'weight_decay': args.weight_decay,
'teacher_force': args.teacher_force,
'n_warmup': args.n_warmup,
'n_const': args.n_const,
'b_input': args.b_input,
'b_update': args.b_update,
'n_print': args.n_print