class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# ToDo : support tr/eval on different corpus
assert self.config['data']['corpus']['name'] == self.src_config[
'data']['corpus']['name']
self.config['data']['corpus']['path'] = self.src_config['data'][
'corpus']['path']
self.config['data']['corpus']['bucketing'] = False
# The follow attribute should be identical to training config
self.config['data']['audio'] = self.src_config['data']['audio']
self.config['data']['text'] = self.src_config['data']['text']
self.config['model'] = self.src_config['model']
# Output file
self.output_file = str(self.ckpdir) + '_{}_{}.csv'
# Override batch size for beam decoding
self.greedy = self.config['decode']['beam_size'] == 1
if not self.greedy:
self.config['data']['corpus']['batch_size'] = 1
else:
# ToDo : implement greedy
raise NotImplementedError
def load_data(self):
''' Load data for training/validation, store tokenizer and input/output shape'''
self.dv_set, self.tt_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu,
self.paras.pin_memory, False, **self.config['data'])
self.verbose(msg)
def set_model(self):
''' Setup ASR model '''
# Model
init_adadelta = True
self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta,
**self.config['model'])
# Plug-ins
if ('emb' in self.config) and (self.config['emb']['enable']) \
and (self.config['emb']['fuse'] > 0):
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(self.tokenizer,
self.model.dec_dim,
**self.config['emb'])
# Load target model in eval mode
self.load_ckpt()
# Beam decoder
self.decoder = BeamDecoder(self.model.cpu(), self.emb_decoder,
**self.config['decode'])
self.verbose(self.decoder.create_msg())
del self.model
del self.emb_decoder
def exec(self):
''' Testing End-to-end ASR system '''
for s, ds in zip(['dev', 'test'], [self.dv_set, self.tt_set]):
# Setup output
self.cur_output_path = self.output_file.format(s, 'output')
with open(self.cur_output_path, 'w') as f:
f.write('idx\thyp\ttruth\n')
if self.greedy:
# Greedy decode
self.verbose(
'Performing batch-wise greedy decoding on {} set, num of batch = {}.'
.format(s, len(ds)))
self.verbose('Results will be stored at {}'.format(
self.cur_output_path))
else:
# Additional output to store all beams
self.cur_beam_path = self.output_file.format(s, 'beam')
with open(self.cur_beam_path, 'w') as f:
f.write('idx\tbeam\thyp\ttruth\n')
self.verbose(
'Performing instance-wise beam decoding on {} set. (NOTE: use --njobs to speedup)'
.format(s))
# Minimal function to pickle
beam_decode_func = partial(beam_decode,
model=copy.deepcopy(self.decoder),
device=self.device)
# Parallel beam decode
results = Parallel(n_jobs=self.paras.njobs)(
delayed(beam_decode_func)(data) for data in tqdm(ds))
self.verbose('Results/Beams will be stored at {} / {}.'.format(
self.cur_output_path, self.cur_beam_path))
self.write_hyp(results, self.cur_output_path,
self.cur_beam_path)
self.verbose('All done !')
def write_hyp(self, results, best_path, beam_path):
'''Record decoding results'''
for name, hyp_seqs, truth in tqdm(results):
hyp_seqs = [self.tokenizer.decode(hyp) for hyp in hyp_seqs]
truth = self.tokenizer.decode(truth)
with open(best_path, 'a') as f:
f.write('\t'.join([name, hyp_seqs[0], truth]) + '\n')
if not self.greedy:
with open(beam_path, 'a') as f:
for b, hyp in enumerate(hyp_seqs):
f.write('\t'.join([name, str(b), hyp, truth]) + '\n')
class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# ToDo : support tr/eval on different corpus
assert self.config['data']['corpus']['name'] == self.src_config['data']['corpus']['name']
self.config['data']['corpus']['path'] = self.src_config['data']['corpus']['path']
self.config['data']['corpus']['bucketing'] = False
# The follow attribute should be identical to training config
self.config['data']['audio'] = self.src_config['data']['audio']
self.config['data']['text'] = self.src_config['data']['text']
self.config['model'] = self.src_config['model']
# Output file
self.output_file = str(self.ckpdir)+'_{}_{}.csv'
# Override batch size for beam decoding
self.greedy = self.config['decode']['beam_size'] == 1
if not self.greedy:
self.config['data']['corpus']['batch_size'] = 1
else:
# ToDo : implement greedy
raise NotImplementedError
def load_data(self):
''' Load data for training/validation, store tokenizer and input/output shape'''
if self.paras.upstream is not None:
print(f'[Solver] - using S3PRL {self.paras.upstream}')
self.dv_set, self.tt_set, self.vocab_size, self.tokenizer, msg = \
load_wav_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
False, **self.config['data'])
self.upstream = torch.hub.load(
's3prl/s3prl',
args.upstream,
feature_selection = args.upstream_feature_selection,
refresh = args.upstream_refresh,
ckpt = args.upstream_ckpt,
force_reload = True,
)
self.feat_dim = self.upstream.get_output_dim()
else:
self.dv_set, self.tt_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu,
self.paras.pin_memory, False, **self.config['data'])
self.verbose(msg)
def set_model(self):
''' Setup ASR model '''
# Model
init_adadelta = self.src_config['hparas']['optimizer'] == 'Adadelta'
self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta, **
self.config['model']).to(self.device)
# Plug-ins
if ('emb' in self.config) and (self.config['emb']['enable']) \
and (self.config['emb']['fuse'] > 0):
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(
self.tokenizer, self.model.dec_dim, **self.config['emb'])
# Load target model in eval mode
self.load_ckpt()
# Beam decoder
self.decoder = BeamDecoder(
self.model.cpu(), self.emb_decoder, **self.config['decode'])
self.verbose(self.decoder.create_msg())
del self.model
del self.emb_decoder
def exec(self):
''' Testing End-to-end ASR system '''
for s, ds in zip(['dev', 'test'], [self.dv_set, self.tt_set]):
# Setup output
self.cur_output_path = self.output_file.format(s, 'output')
with open(self.cur_output_path, 'w') as f:
f.write('idx\thyp\ttruth\n')
if self.greedy:
# Greedy decode
self.verbose(
'Performing batch-wise greedy decoding on {} set, num of batch = {}.'.format(s, len(ds)))
self.verbose('Results will be stored at {}'.format(
self.cur_output_path))
else:
# Additional output to store all beams
self.cur_beam_path = self.output_file.format(s, 'beam')
with open(self.cur_beam_path, 'w') as f:
f.write('idx\tbeam\thyp\ttruth\n')
self.verbose(
'Performing instance-wise beam decoding on {} set. (NOTE: use --njobs to speedup)'.format(s))
# Minimal function to pickle
beam_decode_func = partial(beam_decode, model=copy.deepcopy(
self.decoder), device=self.device)
def handler(data):
if self.paras.upstream is not None:
# feat is raw waveform
name, feat, feat_len, txt = data
device = 'cpu' if self.paras.deterministic else self.device
self.upstream.to(device)
def to_device(feat):
return [f.to(device) for f in feat]
def extract_feature(feat):
feat = self.upstream(to_device(feat))
return feat
self.upstream.eval()
with torch.no_grad():
feat = extract_feature(feat)
feat_len = torch.LongTensor([len(f) for f in feat])
feat = pad_sequence(feat, batch_first=True)
txt = pad_sequence(txt, batch_first=True)
data = [name, feat, feat_len, txt]
return data
# Parallel beam decode
results = Parallel(n_jobs=self.paras.njobs)(
delayed(beam_decode_func)(handler(data)) for data in tqdm(ds))
self.verbose(
'Results/Beams will be stored at {} / {}.'.format(self.cur_output_path, self.cur_beam_path))
self.write_hyp(results, self.cur_output_path,
self.cur_beam_path)
self.verbose('All done !')
def write_hyp(self, results, best_path, beam_path):
'''Record decoding results'''
for name, hyp_seqs, truth in tqdm(results):
hyp_seqs = [self.tokenizer.decode(hyp) for hyp in hyp_seqs]
truth = self.tokenizer.decode(truth)
with open(best_path, 'a') as f:
f.write('\t'.join([name, hyp_seqs[0], truth])+'\n')
if not self.greedy:
with open(beam_path, 'a') as f:
for b, hyp in enumerate(hyp_seqs):
f.write('\t'.join([name, str(b), hyp, truth])+'\n')
class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# ToDo : support tr/eval on different corpus
assert self.config['data']['corpus']['name'] == self.src_config[
'data']['corpus']['name']
self.config['data']['corpus']['path'] = self.src_config['data'][
'corpus']['path']
self.config['data']['corpus']['bucketing'] = False
# The follow attribute should be identical to training config
self.config['data']['audio'] = self.src_config['data']['audio']
self.config['data']['text'] = self.src_config['data']['text']
self.config['hparas'] = self.src_config['hparas']
self.config['model'] = self.src_config['model']
# Output file
self.output_file = str(self.ckpdir) + '_{}_{}.csv'
# Override batch size for beam decoding
self.greedy = self.config['decode']['beam_size'] == 1
if not self.greedy:
self.config['data']['corpus']['batch_size'] = 1
self.step = 0
def fetch_data(self, data):
''' Move data to device and compute text seq. length,
For Greedy decoding only ( beam_decode & ctc_beam_decode otherwise)'''
_, feat, feat_len, txt = data
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
txt = txt.to(self.device)
txt_len = torch.sum(txt != 0, dim=-1)
return feat, feat_len, txt, txt_len
def load_data(self):
''' Load data for training/validation, store tokenizer and input/output shape'''
self.dv_set, self.tt_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu,
self.paras.pin_memory, False, **self.config['data'])
self.verbose(msg)
def set_model(self):
''' Setup ASR model '''
# Model
init_adadelta = self.config['hparas']['optimizer'] == 'Adadelta'
self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta,
**self.config['model']).to(self.device)
# Plug-ins
if ('emb' in self.config) and (self.config['emb']['enable']) \
and (self.config['emb']['fuse'] > 0):
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(self.tokenizer,
self.model.dec_dim,
**self.config['emb'])
# Load target model in eval mode
self.load_ckpt()
self.ctc_only = False
if self.greedy:
# Greedy decoding: attention-based if the ASR has a decoder, else use CTC
self.decoder = copy.deepcopy(self.model).to(self.device)
else:
if (not self.model.enable_att) or self.config['decode'].get(
'ctc_weight', 0.0) == 1.0:
# Pure CTC Beam Decoder
assert self.config['decode']['beam_size'] <= self.config[
'decode']['vocab_candidate']
self.decoder = CTCBeamDecoder(
self.model.to(self.device),
[1] + [r for r in range(3, self.vocab_size)],
self.config['decode']['beam_size'],
self.config['decode']['vocab_candidate'],
lm_path=self.config['decode']['lm_path'],
lm_config=self.config['decode']['lm_config'],
lm_weight=self.config['decode']['lm_weight'],
device=self.device)
self.ctc_only = True
else:
# Joint CTC-Attention Beam Decoder
self.decoder = BeamDecoder(self.model.cpu(), self.emb_decoder,
**self.config['decode'])
self.verbose(self.decoder.create_msg())
del self.model
del self.emb_decoder
def greedy_decode(self, dv_set):
''' Greedy Decoding '''
results = []
for i, data in enumerate(dv_set):
self.progress('Valid step - {}/{}'.format(i + 1, len(dv_set)))
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
ctc_output, encode_len, att_output, att_align, dec_state = \
self.decoder( feat, feat_len, int(float(feat_len.max()) * self.config['decode']['max_len_ratio']),
emb_decoder=self.emb_decoder)
for j in range(len(txt)):
idx = j + self.config['data']['corpus']['batch_size'] * i
if att_output is not None:
hyp_seqs = att_output[j].argmax(dim=-1).tolist()
else:
hyp_seqs = ctc_output[j].argmax(dim=-1).tolist()
true_txt = txt[j]
results.append((str(idx), [hyp_seqs], true_txt))
return results
def exec(self):
''' Testing End-to-end ASR system '''
for s, ds in zip(['dev', 'test'], [self.dv_set, self.tt_set]):
# Setup output
self.cur_output_path = self.output_file.format(s, 'output')
with open(self.cur_output_path, 'w', encoding='UTF-8') as f:
f.write('idx\thyp\ttruth\n')
if self.greedy:
# Greedy decode
self.verbose(
'Performing batch-wise greedy decoding on {} set, num of batch = {}.'
.format(s, len(ds)))
results = self.greedy_decode(ds)
self.verbose('Results will be stored at {}'.format(
self.cur_output_path))
self.write_hyp(results, self.cur_output_path, '-')
elif self.ctc_only:
# CTC beam decode
# Additional output to store all beams
self.cur_beam_path = self.output_file.format(
s, 'beam-{}-{}'.format(self.config['decode']['beam_size'],
self.config['decode']['lm_weight']))
with open(self.cur_beam_path, 'w') as f:
f.write('idx\tbeam\thyp\ttruth\n')
self.verbose(
'Performing instance-wise CTC beam decoding on {} set, num of batch = {}.'
.format(s, len(ds)))
# Minimal function to pickle
ctc_beam_decode_func = partial(ctc_beam_decode,
model=copy.deepcopy(
self.decoder),
device=self.device)
# Parallel beam decode
results = Parallel(n_jobs=self.paras.njobs)(
delayed(ctc_beam_decode_func)(data) for data in tqdm(ds))
self.verbose('Results/Beams will be stored at {} / {}'.format(
self.cur_output_path, self.cur_beam_path))
self.write_hyp(results, self.cur_output_path,
self.cur_beam_path)
else:
# Joint CTC-Attention beam decode
# Additional output to store all beams
self.cur_beam_path = self.output_file.format(
s, 'beam-{}-{}'.format(self.config['decode']['beam_size'],
self.config['decode']['lm_weight']))
with open(self.cur_beam_path, 'w') as f:
f.write('idx\tbeam\thyp\ttruth\n')
self.verbose(
'Performing instance-wise beam decoding on {} set. (NOTE: use --njobs to speedup)'
.format(s))
# Minimal function to pickle
beam_decode_func = partial(beam_decode,
model=copy.deepcopy(self.decoder),
device=self.device)
# Parallel beam decode
results = Parallel(n_jobs=self.paras.njobs)(
delayed(beam_decode_func)(data) for data in tqdm(ds))
self.verbose('Results/Beams will be stored at {} / {}.'.format(
self.cur_output_path, self.cur_beam_path))
self.write_hyp(results, self.cur_output_path,
self.cur_beam_path)
self.verbose('All done !')
def write_hyp(self, results, best_path, beam_path):
'''Record decoding results'''
if self.greedy:
# Ignores repeated symbols if is decoded with CTC
ignore_repeat = not self.decoder.enable_att
else:
ignore_repeat = False
for name, hyp_seqs, truth in tqdm(results):
if self.ctc_only and not self.greedy:
new_hyp_seqs = [
self.tokenizer.decode(hyp, ignore_repeat=False)
for hyp in hyp_seqs[:-1]
]
hyp_seqs = new_hyp_seqs + [
self.tokenizer.decode(hyp_seqs[-1], ignore_repeat=True)
]
else:
hyp_seqs = [self.tokenizer.decode(hyp) for hyp in hyp_seqs]
truth = self.tokenizer.decode(truth)
with open(best_path, 'a') as f:
if len(hyp_seqs[0]) == 0:
# Set the sequence to a whitespace if it was empty
hyp_seqs[0] = ' '
f.write('\t'.join([name, hyp_seqs[0], truth]) + '\n')
if not self.greedy:
with open(beam_path, 'a', encoding='UTF-8') as f:
for b, hyp in enumerate(hyp_seqs):
f.write('\t'.join([name, str(b), hyp, truth]) + '\n')
