def __init__(self, model_path: str, lm_path: str):
super(TraceModel, self).__init__()
self.model, self.train_args = load_trained_model(model_path=model_path)
logging.info(self.model)
assert isinstance(self.model, ASRInterface)
self.model.eval()
self.recog_args = self.__get_recog_args()
self.rnnlm = self.__make_lm_module(lm_path=lm_path)
scorers = self.model.scorers()
scorers["lm"] = self.rnnlm
scorers["length_bonus"] = LengthBonus(len(self.train_args.char_list))
weights = dict(
decoder=1.0 - self.recog_args.ctc_weight,
ctc=self.recog_args.ctc_weight,
lm=self.recog_args.lm_weight,
length_bonus=self.recog_args.penalty,
)
self.beam_search = BeamSearch(
beam_size=self.recog_args.beam_size,
vocab_size=len(self.train_args.char_list),
weights=weights,
scorers=scorers,
sos=self.model.sos,
eos=self.model.eos,
token_list=self.train_args.char_list,
pre_beam_score_key=None
if self.recog_args.ctc_weight == 1.0 else "decoder",
)
def __init__(self, model_path: str, lm_path: str):
super(TraceModel, self).__init__()
self.model, self.train_args = load_trained_model(model_path=model_path)
logging.info(self.model)
assert isinstance(self.model, ASRInterface)
self.model.eval()
self.recog_args = self.__get_recog_args()
def build(self):
self.device = torch.device(self.config["device"])
with open(self.config["model_config"]) as f:
cfg = json.load(f)
encoder, _ = load_trained_model(cfg.pop("pretrained_encoder"))
decoder = DECODERS.get(cfg.pop("decoder_type"))(
output_dim=self.n_classes, **cfg)
return EncoderDecoder(encoder, decoder).to(self.device)
def build_encoder(model_dir, freeze=None):
options = load_args(model_dir)
model, train_args = load_trained_model(options.resume)
model.teacher_model = None
if freeze:
for m in freeze:
logger.info(f"Freeze {m} in encoder")
for p in getattr(model, m).parameters():
p.requires_grad = False
display_model(model, logger.info)
logger.debug(train_args)
return model
def main(args):
"""Run the main function"""
model_name = args[0] + "_" + args[3] + "_" + os.path.splitext(
os.path.basename(args[2]))[0] + "_" + os.path.splitext(
os.path.basename(args[1]))[0]
model_loc = "exp/" + model_name + "/results/" + args[4]
print(model_loc)
model, _ = load_trained_model(model_loc)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Trainable parameters: ", params)
def main(args):
"""Run the main function"""
model_name = args[0] + "_" + args[3] + "_" + os.path.splitext(
os.path.basename(args[2]))[0] + "_" + os.path.splitext(
os.path.basename(args[1]))[0]
model_loc = "exp/" + model_name + "/results/" + args[0]
#model_loc="exp/all_cleaned_pytorch_train_large_transformer_retrained_transformer_model/results/model.last10.avg.best"
print(model_loc)
model, _ = load_trained_model(model_loc)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Trainable parameters: ", params)
def test_auxiliary_task(train_dic):
train_args = make_train_args(**train_dic)
recog_args = make_recog_args()
model, x, ilens, y, data, uttid_list = prepare(train_args)
optim = torch.optim.Adam(model.parameters(), 0.01)
loss = model(x, ilens, y)
optim.zero_grad()
loss.backward()
optim.step()
beam_search = BeamSearchTransducer(
decoder=model.decoder,
joint_network=model.joint_network,
beam_size=recog_args.beam_size,
lm=recog_args.rnnlm,
lm_weight=recog_args.lm_weight,
search_type=recog_args.search_type,
max_sym_exp=recog_args.max_sym_exp,
u_max=recog_args.u_max,
nstep=recog_args.nstep,
prefix_alpha=recog_args.prefix_alpha,
score_norm=recog_args.score_norm_transducer,
)
tmpdir = tempfile.mkdtemp(prefix="tmp_", dir="/tmp")
torch.save(model.state_dict(), tmpdir + "/model.dummy.best")
with open(tmpdir + "/model.json", "wb") as f:
f.write(
json.dumps(
(12, 5, vars(train_args)),
indent=4,
ensure_ascii=False,
sort_keys=True,
).encode("utf_8"))
with torch.no_grad():
model, _ = load_trained_model(tmpdir + "/model.dummy.best",
training=False)
nbest = model.recognize(x[0, :ilens[0]].numpy(), beam_search)
print(y[0])
print(nbest[0]["yseq"][1:-1])
def test_auxiliary_task(train_dic):
idim, odim, ilens, olens = get_default_scope_inputs()
train_args = get_default_train_args(**train_dic)
recog_args = get_default_recog_args()
model = E2E(idim, odim, train_args)
batch = prepare_inputs(idim, odim, ilens, olens)
loss = model(*batch)
loss.backward()
beam_search = BeamSearchTransducer(
decoder=model.dec,
joint_network=model.joint_network,
beam_size=recog_args.beam_size,
lm=recog_args.rnnlm,
lm_weight=recog_args.lm_weight,
search_type=recog_args.search_type,
max_sym_exp=recog_args.max_sym_exp,
u_max=recog_args.u_max,
nstep=recog_args.nstep,
prefix_alpha=recog_args.prefix_alpha,
score_norm=recog_args.score_norm_transducer,
)
tmpdir = tempfile.mkdtemp(prefix="tmp_", dir="/tmp")
torch.save(model.state_dict(), tmpdir + "/model.dummy.best")
with open(tmpdir + "/model.json", "wb") as f:
f.write(
json.dumps(
(idim, odim, vars(train_args)),
indent=4,
ensure_ascii=False,
sort_keys=True,
).encode("utf_8"))
with torch.no_grad():
in_data = np.random.randn(20, idim)
model, _ = load_trained_model(tmpdir + "/model.dummy.best",
training=False)
model.recognize(in_data, beam_search)
def trans(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, STInterface)
# args.ctc_weight = 0.0
model.trans_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(train_args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info("gpu id: " + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.trans_json, "rb") as f:
js = json.load(f)["utts"]
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={"train": False},
)
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)[0][0]
nbest_hyps = model.translate(feat, args, train_args.char_list,
rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = load_inputs_and_targets(batch)[0]
nbest_hyps = model.translate_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, "wb") as f:
f.write(
json.dumps({
"utts": new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode("utf_8"))
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.recog_args = args
if args.streaming_mode and "transformer" in train_args.model_module:
raise NotImplementedError("streaming mode for transformer is not implemented")
logging.info(
" Total parameter of the model = "
+ str(sum(p.numel() for p in model.parameters()))
)
# read rnnlm
rnnlm = None
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info("gpu id: " + str(gpu_id))
model.cuda()
# read json data
with open(args.recog_json, "rb") as f:
js = json.load(f)["utts"]
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None
else args.preprocess_conf,
preprocess_args={"train": False},
)
ark_file = open(args.result_ark,'wb')
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)
feat = (
feat[0][0]
if args.num_encs == 1
else [feat[idx][0] for idx in range(model.num_encs)]
)
hyps = model.recognize(
feat, args, train_args.char_list, rnnlm
)
# TODO: is there any way to overwrite decoding results into new js?
hyps = hyps.squeeze(1)
hyps = hyps.data.numpy()
write_mat(ark_file, hyps, key=name)
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
iteration = 1
for names in grouper(args.batchsize, keys, None):
dec_idx = iteration * len(names)
logging.info("(%d/%d) decoding ", dec_idx, len(keys))
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = (
load_inputs_and_targets(batch)[0]
if args.num_encs == 1
else load_inputs_and_targets(batch)
)
hyps = model.recognize_batch(
feats, args, train_args.char_list, rnnlm=rnnlm
)
# TODO: is there any way to overwrite decoding results into new js?
hyps = hyps.data.cpu().numpy()
for idx, hyp in enumerate(hyps):
write_mat(ark_file, hyp, key=names[idx])
iteration+=1
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.recog_args = args
if args.streaming_mode and "transformer" in train_args.model_module:
raise NotImplementedError(
"streaming mode for transformer is not implemented")
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(
len(train_args.char_list),
rnnlm_args.layer,
rnnlm_args.unit,
getattr(rnnlm_args, "embed_unit",
None), # for backward compatibility
))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
word_dict = rnnlm_args.char_list_dict
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(
len(word_dict),
rnnlm_args.layer,
rnnlm_args.unit,
getattr(rnnlm_args, "embed_unit",
None), # for backward compatibility
))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict,
char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor, word_dict,
char_dict))
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info("gpu id: " + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.recog_json, "rb") as f:
js = json.load(f)["utts"]
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={"train": False},
)
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)
feat = (feat[0][0] if args.num_encs == 1 else
[feat[idx][0] for idx in range(model.num_encs)])
if args.streaming_mode == "window" and args.num_encs == 1:
logging.info(
"Using streaming recognizer with window size %d frames",
args.streaming_window,
)
se2e = WindowStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
for i in range(0, feat.shape[0], args.streaming_window):
logging.info("Feeding frames %d - %d", i,
i + args.streaming_window)
se2e.accept_input(feat[i:i + args.streaming_window])
logging.info("Running offline attention decoder")
se2e.decode_with_attention_offline()
logging.info("Offline attention decoder finished")
nbest_hyps = se2e.retrieve_recognition()
elif args.streaming_mode == "segment" and args.num_encs == 1:
logging.info(
"Using streaming recognizer with threshold value %d",
args.streaming_min_blank_dur,
)
nbest_hyps = []
for n in range(args.nbest):
nbest_hyps.append({"yseq": [], "score": 0.0})
se2e = SegmentStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
r = np.prod(model.subsample)
for i in range(0, feat.shape[0], r):
hyps = se2e.accept_input(feat[i:i + r])
if hyps is not None:
text = "".join([
train_args.char_list[int(x)]
for x in hyps[0]["yseq"][1:-1] if int(x) != -1
])
text = text.replace(
"\u2581", " ").strip() # for SentencePiece
text = text.replace(model.space, " ")
text = text.replace(model.blank, "")
logging.info(text)
for n in range(args.nbest):
nbest_hyps[n]["yseq"].extend(hyps[n]["yseq"])
nbest_hyps[n]["score"] += hyps[n]["score"]
else:
nbest_hyps = model.recognize(feat, args,
train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]["input"][0]["shape"][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = (load_inputs_and_targets(batch)[0] if args.num_encs
== 1 else load_inputs_and_targets(batch))
if args.streaming_mode == "window" and args.num_encs == 1:
raise NotImplementedError
elif args.streaming_mode == "segment" and args.num_encs == 1:
if args.batchsize > 1:
raise NotImplementedError
feat = feats[0]
nbest_hyps = []
for n in range(args.nbest):
nbest_hyps.append({"yseq": [], "score": 0.0})
se2e = SegmentStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
r = np.prod(model.subsample)
for i in range(0, feat.shape[0], r):
hyps = se2e.accept_input(feat[i:i + r])
if hyps is not None:
text = "".join([
train_args.char_list[int(x)]
for x in hyps[0]["yseq"][1:-1] if int(x) != -1
])
text = text.replace(
"\u2581", " ").strip() # for SentencePiece
text = text.replace(model.space, " ")
text = text.replace(model.blank, "")
logging.info(text)
for n in range(args.nbest):
nbest_hyps[n]["yseq"].extend(hyps[n]["yseq"])
nbest_hyps[n]["score"] += hyps[n]["score"]
nbest_hyps = [nbest_hyps]
else:
nbest_hyps = model.recognize_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, "wb") as f:
f.write(
json.dumps({
"utts": new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode("utf_8"))
def ctc_align(args):
"""CTC forced alignments with the given args.
Args:
args (namespace): The program arguments.
"""
def add_alignment_to_json(js, alignment, char_list):
"""Add N-best results to json.
Args:
js (dict[str, Any]): Groundtruth utterance dict.
alignment (list[int]): List of alignment.
char_list (list[str]): List of characters.
Returns:
dict[str, Any]: N-best results added utterance dict.
"""
# copy old json info
new_js = dict()
new_js["ctc_alignment"] = []
alignment_tokens = []
for idx, a in enumerate(alignment):
alignment_tokens.append(char_list[a])
alignment_tokens = " ".join(alignment_tokens)
new_js["ctc_alignment"] = alignment_tokens
return new_js
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.eval()
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=True,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={"train": False},
)
if args.ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
if args.ngpu == 1:
device = "cuda"
else:
device = "cpu"
dtype = getattr(torch, args.dtype)
logging.info(f"Decoding device={device}, dtype={dtype}")
model.to(device=device, dtype=dtype).eval()
# read json data
with open(args.align_json, "rb") as f:
js = json.load(f)["utts"]
new_js = {}
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) aligning " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat, label = load_inputs_and_targets(batch)
feat = feat[0]
label = label[0]
enc = model.encode(
torch.as_tensor(feat).to(device)).unsqueeze(0)
alignment = model.ctc.forced_align(enc, label)
new_js[name] = add_alignment_to_json(js[name], alignment,
train_args.char_list)
else:
raise NotImplementedError("Align_batch is not implemented.")
with open(args.result_label, "wb") as f:
f.write(
json.dumps({
"utts": new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode("utf_8"))
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model_path)
assert isinstance(model, ASRInterface)
model.recog_args = args
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
new_js = {}
print(args.preprocess_conf)
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
if args.batchsize == 0:
model.eval()
# import torch.onnx
# batch_size = 5
# xs_pad = torch.randn(batch_size, 1000, 83)
# ilens = torch.randint(100, (batch_size,))
# ys_pad = torch.randint(100, (batch_size, 1000))
# # loss = model(xs_pad, ilens, ys_pad)
# torch.onnx.export(model, (xs_pad, ilens, ys_pad), "sup_mlt.onnx",
# do_constant_folding=True, opset_version=12,
# input_names = ['xs_pad', 'ilens', "ys_pad"], output_names = ['output'],
# dynamic_axes={'xs_pad' : {0 : 'batch_size'},
# 'ilens' : {0 : 'batch_size'},
# 'ys_pad' : {0 : 'batch_size'}})
# scripted_module = torch.jit.script(model)
# seq_len = 257
# x = torch.randn(seq_len, 83, requires_grad=True)
# torch.onnx.export(model, x, "sup_mlt.onnx", opset_version=11,
# do_constant_folding=True,
# input_names = ['input'], output_names = ['output'],
# dynamic_axes={'input' : {0 : 'seq_len'}, 'output' : {0 : 'seq_len'}})
# import onnxruntime
# ort_session = onnxruntime.InferenceSession("sup_mlt.onnx")
# print(ort_session.get_inputs()[0].name)
'''
decoder_fos.onnxde
'''
# from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
# ys = torch.tensor([7442, 2]).unsqueeze(0)
# ys_mask = subsequent_mask(2).unsqueeze(0)
# enc_output = torch.randn(1, 63, 256)
# torch.onnx.export(model, (ys, ys_mask, enc_output), "encoder_fos.onnx", opset_version=11,
# do_constant_folding=True,
# input_names = ['ys', 'ys_mask', 'enc_output'], output_names = ['output'],
# dynamic_axes={'ys' : {1 : 'len1'}, 'ys_mask' : {1 : 'len21', 2 : 'len22'}, 'enc_output' : {1 : 'len3'},
# 'output' : {}})
'''
encoder.onnx
'''
# x = torch.rand(257, 83)
# torch.onnx.export(model, x, "encoder.onnx", opset_version=11,
# do_constant_folding=True,
# input_names = ['x'], output_names = ['enc_output'],
# dynamic_axes={'x' : {0 : 'len1'}, 'enc_output' : {1 : 'len2'}})
'''
ctc_softmax.onnx
'''
# enc_output = torch.rand(1, 63, 256)
# torch.onnx.export(model, enc_output, "ctc_softmax.onnx", opset_version=11,
# do_constant_folding=True,
# input_names = ['enc_output'], output_names = ['lpz'],
# dynamic_axes={'enc_output' : {1 : 'len1'}, 'lpz' : {0 : 'len2'}})
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)
feat = feat[0][0] if args.num_encs == 1 else [
feat[idx][0] for idx in range(model.num_encs)
]
feat = torch.from_numpy(feat)
print(f"input size: {feat.shape}")
from pyonnxrt import infer
nbest_hyps = infer(feat)
# nbest_hyps = model(feat)
# get token ids and tokens
tokenid_as_list = list(map(int, nbest_hyps[1:]))
token_as_list = [
train_args.char_list[idx] for idx in tokenid_as_list
]
print(token_as_list)
# new_js[name] = add_results_to_json(js[name], nbest_hyps, train_args.char_list)
# print(new_js)
if idx == 10:
exit()
exit()
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = load_inputs_and_targets(
batch
)[0] if args.num_encs == 1 else load_inputs_and_targets(batch)
if args.streaming_mode == 'window' and args.num_encs == 1:
raise NotImplementedError
elif args.streaming_mode == 'segment' and args.num_encs == 1:
if args.batchsize > 1:
raise NotImplementedError
feat = feats[0]
nbest_hyps = []
for n in range(args.nbest):
nbest_hyps.append({'yseq': [], 'score': 0.0})
se2e = SegmentStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
r = np.prod(model.subsample)
for i in range(0, feat.shape[0], r):
hyps = se2e.accept_input(feat[i:i + r])
if hyps is not None:
text = ''.join([
train_args.char_list[int(x)]
for x in hyps[0]['yseq'][1:-1] if int(x) != -1
])
text = text.replace(
'\u2581', ' ').strip() # for SentencePiece
text = text.replace(model.space, ' ')
text = text.replace(model.blank, '')
logging.info(text)
for n in range(args.nbest):
nbest_hyps[n]['yseq'].extend(hyps[n]['yseq'])
nbest_hyps[n]['score'] += hyps[n]['score']
nbest_hyps = [nbest_hyps]
else:
nbest_hyps = model.recognize_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.recog_args = args
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
model = model.to(device)
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr', load_output=True, sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
import kaldiio
import time
with torch.no_grad(), \
kaldiio.WriteHelper('ark,scp:{o}.ark,{o}.scp'.format(o=args.out)) as f:
ys = []
xs = []
for idx, utt_id in enumerate(js.keys()):
logging.info('(%d/%d) decoding ' + utt_id, idx, len(js.keys()))
batch = [(utt_id, js[utt_id])]
data = load_inputs_and_targets(batch)
feat = data[0][0]
ys.append(data[1][0])
# x = torch.LongTensor(x).to(device)
# decode and write
start_time = time.time()
# include the inference here
# have the layer specification here
# skeleton model.inference(x, args, layer)
scores, outs = model.inference(feat, ys, args, train_args.char_list)
xs.append(scores)
logging.info("inference speed = %s msec / frame." % (
(time.time() - start_time) / (int(outs.size(0)) * 1000)))
logging.warning("output length reaches maximum length (%s)." % utt_id)
logging.info('(%d/%d) %s (size:%d->%d)' % (
idx + 1, len(js.keys()), utt_id, len(feat), outs.size(0)))
f[utt_id] = outs.cpu().numpy()
from espnet.nets.pytorch_backend.nets_utils import th_accuracy
preds = torch.stack(xs).view(len(xs), -1)
labels = torch.LongTensor(ys).view(len(xs), 1)
acc = th_accuracy(preds, labels, -1)
logging.warn("Final acc is (%.2f)" % (acc*100))
#train_json='/mnt/lustre/sjtu/users/yzl23/work_dir/asr/is20_codeswitching/espnet/egs/codeswitching/asr/data/json_data/mix200/data.json'
#valid_json='/mnt/lustre/sjtu/users/yzl23/work_dir/asr/is20_codeswitching/espnet/egs/codeswitching/asr/data/json_data/dev_mix20/data.json'
aux_model_path='/mnt/lustre/sjtu/users/mkh96/wordspace/asr/codeswitch/exp/phone_classifier/transformer_layer12_lsm0.0_ep100/results/snapshot.ep.100'
args, _ = get_parser(required=False).parse_known_args('--config {} --train-json {} --valid-json {} --ngpu 0 '.format(conf, train_json, valid_json))
args2,_ = get_parser(required=False).parse_known_args('--config {} --train-json {} --valid-json {} --ngpu 0 '.format(conf, train_json, eval_json))
# iter
tr_iter, dev_iter = get_iter(args)
_ , eval_iter = get_iter(args2)
tr_iter = tr_iter['main']
dev_iter = dev_iter['main']
eval_iter = eval_iter['main']
aux_model, aux_args = load_trained_model(aux_model_path)
aux_model.eval()
res=OrderedDict()
cnt=1
for batch in itertools.chain(tr_iter, dev_iter, eval_iter):
xs_pad, ilens, ys_pad, uttid_list, train = batch
xs_pad = xs_pad[:, :max(ilens)] # for data parallel
src_mask = (~make_pad_mask(ilens.tolist())).to(xs_pad.device).unsqueeze(-2)
with torch.no_grad():
emb, masks = aux_model.encoder(xs_pad, src_mask)
masks = masks.squeeze(1)
#print(masks, 'masks')
#print(emb.size(), masks.size())
if onehot:
def ctc_align(args, device):
"""ESPnet-specific interface for CTC segmentation.
Parses configuration, infers the CTC posterior probabilities,
and then aligns start and end of utterances using CTC segmentation.
Results are written to the output file given in the args.
:param args: given configuration
:param device: for inference; one of ['cuda', 'cpu']
:return: 0 on success
"""
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=True,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={"train": False},
)
logging.info(f"Decoding device={device}")
model.to(device=device).eval()
# read audio and text json data
with open(args.data_json, "rb") as f:
js = json.load(f)["utts"]
with open(args.utt_text, "r") as f:
lines = f.readlines()
i = 0
text = {}
segment_names = {}
for name in js.keys():
text_per_audio = []
segment_names_per_audio = []
while i < len(lines) and lines[i].startswith(name):
text_per_audio.append(lines[i][lines[i].find(" ") + 1:])
segment_names_per_audio.append(lines[i][:lines[i].find(" ")])
i += 1
text[name] = text_per_audio
segment_names[name] = segment_names_per_audio
# apply configuration
config = CtcSegmentationParameters()
if args.subsampling_factor is not None:
config.subsampling_factor = args.subsampling_factor
if args.frame_duration is not None:
config.frame_duration_ms = args.frame_duration
if args.min_window_size is not None:
config.min_window_size = args.min_window_size
if args.max_window_size is not None:
config.max_window_size = args.max_window_size
char_list = train_args.char_list
if args.use_dict_blank:
config.blank = char_list[0]
logging.debug(
f"Frame timings: {config.frame_duration_ms}ms * {config.subsampling_factor}"
)
# Iterate over audio files to decode and align
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) Aligning " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat, label = load_inputs_and_targets(batch)
feat = feat[0]
with torch.no_grad():
# Encode input frames
enc_output = model.encode(
torch.as_tensor(feat).to(device)).unsqueeze(0)
# Apply ctc layer to obtain log character probabilities
lpz = model.ctc.log_softmax(enc_output)[0].cpu().numpy()
# Prepare the text for aligning
ground_truth_mat, utt_begin_indices = prepare_text(
config, text[name], char_list)
# Align using CTC segmentation
timings, char_probs, state_list = ctc_segmentation(
config, lpz, ground_truth_mat)
# Obtain list of utterances with time intervals and confidence score
segments = determine_utterance_segments(config, utt_begin_indices,
char_probs, timings,
text[name])
# Write to "segments" file
for i, boundary in enumerate(segments):
utt_segment = (f"{segment_names[name][i]} {name} {boundary[0]:.2f}"
f" {boundary[1]:.2f} {boundary[2]:.9f}\n")
args.output.write(utt_segment)
return 0
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.recog_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(train_args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
word_dict = rnnlm_args.char_list_dict
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(word_dict), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict,
char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor, word_dict,
char_dict))
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)[0][0]
if args.streaming_mode == 'window':
logging.info(
'Using streaming recognizer with window size %d frames',
args.streaming_window)
se2e = WindowStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
for i in range(0, feat.shape[0], args.streaming_window):
logging.info('Feeding frames %d - %d', i,
i + args.streaming_window)
se2e.accept_input(feat[i:i + args.streaming_window])
logging.info('Running offline attention decoder')
se2e.decode_with_attention_offline()
logging.info('Offline attention decoder finished')
nbest_hyps = se2e.retrieve_recognition()
elif args.streaming_mode == 'segment':
logging.info(
'Using streaming recognizer with threshold value %d',
args.streaming_min_blank_dur)
nbest_hyps = []
for n in range(args.nbest):
nbest_hyps.append({'yseq': [], 'score': 0.0})
se2e = SegmentStreamingE2E(e2e=model,
recog_args=args,
rnnlm=rnnlm)
r = np.prod(model.subsample)
for i in range(0, feat.shape[0], r):
hyps = se2e.accept_input(feat[i:i + r])
if hyps is not None:
text = ''.join([
train_args.char_list[int(x)]
for x in hyps[0]['yseq'][1:-1] if int(x) != -1
])
text = text.replace(
'\u2581', ' ').strip() # for SentencePiece
text = text.replace(model.space, ' ')
text = text.replace(model.blank, '')
logging.info(text)
for n in range(args.nbest):
nbest_hyps[n]['yseq'].extend(hyps[n]['yseq'])
nbest_hyps[n]['score'] += hyps[n]['score']
else:
nbest_hyps = model.recognize(feat, args,
train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = load_inputs_and_targets(batch)[0]
nbest_hyps = model.recognize_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
def save_alignment(args):
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
model.recog_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError("use '--api v2' option to decode with non-default language model")
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(
len(train_args.char_list), rnnlm_args.layer, rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
word_dict = rnnlm_args.char_list_dict
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(lm_pytorch.RNNLM(
len(word_dict), rnnlm_args.layer, rnnlm_args.unit))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict, char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor,
word_dict, char_dict))
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
dtype = next(model.parameters()).dtype
model = model.to(device=device)
if rnnlm:
rnnlm = rnnlm.to(device=device)
# read json data
with open(args.json, 'rb') as f:
js = json.load(f)['utts']
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr', load_output=True, sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# Setup a converter
if args.num_encs == 1:
converter = CustomConverter(subsampling_factor=model.subsample[0], dtype=dtype)
else:
converter = CustomConverterMulEnc([i[0] for i in model.subsample_list], dtype=dtype)
import matplotlib.pyplot as plt
outdir = args.outdir
if not os.path.exists(outdir):
os.makedirs(outdir)
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
x = converter([load_inputs_and_targets(batch)], device)
alignments = model.calculate_alignments(*x)
for i in range(len(alignments)):
alignment = np.transpose(np.exp(alignments[i].astype(np.float32)))
np_filename = "%s/%s.npy" % (outdir, names[i])
np.save(np_filename, alignment)
plt.imshow(alignment, aspect="auto")
plt.xlabel("Input Index")
plt.ylabel("Label Index")
plt.tight_layout()
fig_filename = "%s/%s.png" % (outdir, names[i])
plt.savefig(fig_filename)
plt.close()
# with open("/root/kws_data/dump/dev/deltafalse/data.json", "r") as f:
# dev_json = json.load(f)["utts"]
train_data_batches = make_batchset(train_json, 1)
test_data_batches = make_batchset(test_json, 1)
# dev_data_batches = make_batchset(dev_json, 1)
load_tr = LoadInputsAndTargets(
mode='asr', load_output=True, preprocess_conf=None,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
converter = CustomConverter(subsampling_factor=1, dtype=torch.float32)
model, train_args = load_trained_model("/home/ram/chaitanay/model/model.acc.best")
model = model.to(device=device)
model.dec.sampling_probability = 1.0
phone_to_int = dict(zip(train_args.char_list, np.arange(len(train_args.char_list))))
keyword = "G R EY T"
keyword_tokens = torch.tensor([[phone_to_int[phn] for phn in keyword.split(" ")]]).to(device)
encoder_output = 0
# def get_att_score2(att_w_list):
# atts = [ele.detach().cpu().numpy().flatten() for ele in att_w_list]
# atts = np.array(atts)
# sum_att = np.prod(atts, axis=0)
# att_score = np.trapz(sum_att)
# return att_score
def get_n_params(model):
pp = 0
for p in list(model[0].parameters()):
nn = 1
for s in list(p.size()):
nn = nn * s
pp += nn
return pp
from espnet.asr.pytorch_backend.asr_init import load_trained_model
model = load_trained_model(
r"/teamscratch/tts_intern_experiment/yuwu1/ASR/librispeech_0.4.0/transformer_ourarch_mask_speed_finetune/results/snapshot.ep.60"
)
get_n_params(model)
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
model.recog_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(
len(train_args.char_list),
rnnlm_args.layer,
rnnlm_args.unit,
getattr(rnnlm_args, "embed_unit",
None), # for backward compatibility
))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info("gpu id: " + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.recog_json, "rb") as f:
js = json.load(f)["utts"]
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={"train": False},
)
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) decoding " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)
feat = feat[0][0]
if args.prefix_decode:
best, ids, score = model.prefix_recognize(
feat, args, train_args, train_args.char_list, rnnlm)
new_js[name] = add_single_results(js[name], best, ids, score)
else:
nbest_hyps = model.recognize(feat, args, train_args.char_list,
rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
with open(args.result_label, "wb") as f:
f.write(
json.dumps({
"utts": new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode("utf_8"))
def train(args):
"""Train with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][-1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][-1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
if args.enc_init is not None or args.dec_init is not None:
model = load_trained_modules(idim, odim, args)
elif args.asr_init is not None:
model, _ = load_trained_model(args.asr_init)
else:
model_class = dynamic_import(args.model_module)
model = model_class(idim, odim, args)
assert isinstance(model, ASRInterface)
subsampling_factor = model.subsample[0]
if args.rnnlm is not None:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch.load(args.rnnlm, rnnlm)
model.rnnlm = rnnlm
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)),
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
if args.batch_size != 0:
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
if args.train_dtype in ("float16", "float32", "float64"):
dtype = getattr(torch, args.train_dtype)
else:
dtype = torch.float32
logging.info(device)
logging.info(dtype)
model = model.to(device=device, dtype=dtype)
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = torch.optim.Adadelta(model.parameters(),
rho=0.95,
eps=args.eps,
weight_decay=args.weight_decay)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=args.weight_decay)
elif args.opt == 'noam':
from espnet.nets.pytorch_backend.rnn.optimizer import get_std_opt
optimizer = get_std_opt(model, args.adim,
args.transformer_warmup_steps,
args.transformer_lr)
else:
raise NotImplementedError("unknown optimizer: " + args.opt)
# setup apex.amp
if args.train_dtype in ("O0", "O1", "O2", "O3"):
try:
from apex import amp
except ImportError as e:
logging.error(
f"You need to install apex for --train-dtype {args.train_dtype}. "
"See https://github.com/NVIDIA/apex#linux")
raise e
if args.opt == 'noam':
model, optimizer.optimizer = amp.initialize(
model, optimizer.optimizer, opt_level=args.train_dtype)
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.train_dtype)
use_apex = True
else:
use_apex = False
# FIXME: TOO DIRTY HACK
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(subsampling_factor=subsampling_factor,
dtype=dtype)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
load_tr = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
load_cv = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.n_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train, load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train, load_tr),
batch_size=1,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(TransformDataset(
valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model,
args.grad_clip,
train_iter,
optimizer,
converter,
device,
args.ngpu,
args.grad_noise,
args.accum_grad,
use_apex=use_apex)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device,
args.ngpu))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
att_reporter = plot_class(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
transform=load_cv,
device=device)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
trainer.extend(
extensions.PlotReport(['main/cer_ctc', 'validation/main/cer_ctc'],
'epoch',
file_name='cer.png'))
# Save best models
trainer.extend(
snapshot_object(model, 'model.loss.best'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode != 'ctc':
trainer.extend(
snapshot_object(model, 'model.acc.best'),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# save snapshot which contains model and optimizer states
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode != 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(
extensions.LogReport(trigger=(args.report_interval_iters,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'main/cer_ctc', 'validation/main/cer_ctc', 'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').
param_groups[0]["eps"]),
trigger=(args.report_interval_iters, 'iteration'))
report_keys.append('eps')
if args.report_cer:
report_keys.append('validation/main/cer')
if args.report_wer:
report_keys.append('validation/main/wer')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.report_interval_iters, 'iteration'))
trainer.extend(
extensions.ProgressBar(update_interval=args.report_interval_iters))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
trainer.extend(TensorboardLogger(SummaryWriter(args.tensorboard_dir),
att_reporter),
trigger=(args.report_interval_iters, "iteration"))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def trans(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
assert isinstance(model, STInterface)
# args.ctc_weight = 0.0
model.trans_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(train_args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.trans_json, 'rb') as f:
js = json.load(f)['utts']
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
# Change to evaluation mode
model.eval()
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('| (%d/%d) decoding ' + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)[0][0]
if args.recog_and_trans: # for cross
logging.info(
'***** Recognize and Translate simultaneously for cross decoders ******'
)
if args.beam_search_type == 'sum':
logging.info('=== Beam search by sum of scores ===')
nbest_hyps = model.recognize_and_translate_sum(
feat,
args,
train_args.char_list,
rnnlm,
decode_asr_weight=args.decode_asr_weight,
score_is_prob=args.score_is_prob,
ratio_diverse_st=args.ratio_diverse_st,
ratio_diverse_asr=args.ratio_diverse_asr,
debug=args.debug)
new_js[name] = add_results_to_json_st_asr(
js[name], nbest_hyps, train_args.char_list)
elif args.beam_search_type == 'sum-mono':
logging.info('=== Beam search by sum of scores ===')
nbest_hyps = model.recognize_and_translate_sum(
feat,
args,
train_args.char_list,
rnnlm,
decode_asr_weight=args.decode_asr_weight,
score_is_prob=args.score_is_prob,
ratio_diverse_st=args.ratio_diverse_st,
ratio_diverse_asr=args.ratio_diverse_asr,
debug=args.debug)
new_js[name] = add_results_to_json(
js[name], nbest_hyps, train_args.char_list)
elif args.beam_search_type == 'separate':
logging.info(
'=== Beam search using beam_cross hypothesis ===')
nbest_hyps, nbest_hyps_asr = model.recognize_and_translate_separate(
feat, args, train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(
js[name], nbest_hyps, train_args.char_list)
new_js[name]['output'].append(
add_results_to_json(js[name],
nbest_hyps_asr,
train_args.char_list,
output_idx=1)['output'][0])
else:
raise NotImplementedError
elif args.recog and args.trans:
logging.info(
'***** Recognize and Translate separately ******')
nbest_hyps_asr = model.recognize(feat, args,
train_args.char_list,
rnnlm)
nbest_hyps = model.translate(feat, args,
train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
new_js[name]['output'].append(
add_results_to_json(js[name],
nbest_hyps_asr,
train_args.char_list,
output_idx=1)['output'][0])
elif args.recog:
logging.info('***** Recognize ONLY ******')
nbest_hyps_asr = model.recognize(feat, args,
train_args.char_list,
rnnlm)
new_js[name] = add_results_to_json(js[name],
nbest_hyps_asr,
train_args.char_list)
elif args.trans:
logging.info('***** Translate ONLY ******')
nbest_hyps = model.translate(feat, args,
train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
else:
raise NotImplementedError
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data if batchsize > 1
keys = list(js.keys())
if args.batchsize > 1:
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = load_inputs_and_targets(batch)[0]
nbest_hyps = model.translate_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
def ctc_align(args, device):
"""ESPnet-specific interface for CTC segmentation.
Parses configuration, infers the CTC posterior probabilities,
and then aligns start and end of utterances using CTC segmentation.
Results are written to the output file given in the args.
:param args: given configuration
:param device: for inference; one of ['cuda', 'cpu']
:return: 0 on success
"""
model, train_args = load_trained_model(args.model)
assert isinstance(model, ASRInterface)
load_inputs_and_targets = LoadInputsAndTargets(
mode="asr",
load_output=True,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None
else args.preprocess_conf,
preprocess_args={"train": False},
)
logging.info(f"Decoding device={device}")
# Warn for nets with high memory consumption on long audio files
if hasattr(model, "enc"):
encoder_module = model.enc.__class__.__module__
elif hasattr(model, "encoder"):
encoder_module = model.encoder.__class__.__module__
else:
encoder_module = "Unknown"
logging.info(f"Encoder module: {encoder_module}")
logging.info(f"CTC module: {model.ctc.__class__.__module__}")
if "rnn" not in encoder_module:
logging.warning("No BLSTM model detected; memory consumption may be high.")
model.to(device=device).eval()
# read audio and text json data
with open(args.data_json, "rb") as f:
js = json.load(f)["utts"]
with open(args.utt_text, "r", encoding="utf-8") as f:
lines = f.readlines()
i = 0
text = {}
segment_names = {}
for name in js.keys():
text_per_audio = []
segment_names_per_audio = []
while i < len(lines) and lines[i].startswith(name):
text_per_audio.append(lines[i][lines[i].find(" ") + 1 :])
segment_names_per_audio.append(lines[i][: lines[i].find(" ")])
i += 1
text[name] = text_per_audio
segment_names[name] = segment_names_per_audio
# apply configuration
config = CtcSegmentationParameters()
subsampling_factor = 1
frame_duration_ms = 10
if args.subsampling_factor is not None:
subsampling_factor = args.subsampling_factor
if args.frame_duration is not None:
frame_duration_ms = args.frame_duration
# Backwards compatibility to ctc_segmentation <= 1.5.3
if hasattr(config, "index_duration"):
config.index_duration = frame_duration_ms * subsampling_factor / 1000
else:
config.subsampling_factor = subsampling_factor
config.frame_duration_ms = frame_duration_ms
if args.min_window_size is not None:
config.min_window_size = args.min_window_size
if args.max_window_size is not None:
config.max_window_size = args.max_window_size
config.char_list = train_args.char_list
if args.use_dict_blank is not None:
logging.warning(
"The option --use-dict-blank is deprecated. If needed,"
" use --set-blank instead."
)
if args.set_blank is not None:
config.blank = args.set_blank
if args.replace_spaces_with_blanks is not None:
if args.replace_spaces_with_blanks:
config.replace_spaces_with_blanks = True
else:
config.replace_spaces_with_blanks = False
if args.gratis_blank:
config.blank_transition_cost_zero = True
if config.blank_transition_cost_zero and args.replace_spaces_with_blanks:
logging.error(
"Blanks are inserted between words, and also the transition cost of blank"
" is zero. This configuration may lead to misalignments!"
)
if args.scoring_length is not None:
config.score_min_mean_over_L = args.scoring_length
logging.info(f"Frame timings: {frame_duration_ms}ms * {subsampling_factor}")
# Iterate over audio files to decode and align
for idx, name in enumerate(js.keys(), 1):
logging.info("(%d/%d) Aligning " + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat, label = load_inputs_and_targets(batch)
feat = feat[0]
with torch.no_grad():
# Encode input frames
enc_output = model.encode(torch.as_tensor(feat).to(device)).unsqueeze(0)
# Apply ctc layer to obtain log character probabilities
lpz = model.ctc.log_softmax(enc_output)[0].cpu().numpy()
# Prepare the text for aligning
ground_truth_mat, utt_begin_indices = prepare_text(config, text[name])
# Align using CTC segmentation
timings, char_probs, state_list = ctc_segmentation(
config, lpz, ground_truth_mat
)
logging.debug(f"state_list = {state_list}")
# Obtain list of utterances with time intervals and confidence score
segments = determine_utterance_segments(
config, utt_begin_indices, char_probs, timings, text[name]
)
# Write to "segments" file
for i, boundary in enumerate(segments):
utt_segment = (
f"{segment_names[name][i]} {name} {boundary[0]:.2f}"
f" {boundary[1]:.2f} {boundary[2]:.9f}\n"
)
args.output.write(utt_segment)
return 0
def trans(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
model, train_args = load_trained_model(args.model)
# assert isinstance(model, STInterface)
# TODO(hirofumi0810) fix this for after supporting Transformer
args.ctc_weight = 0.0
model.trans_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
if getattr(rnnlm_args, "model_module", "default") != "default":
raise ValueError(
"use '--api v2' option to decode with non-default language model"
)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(train_args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
word_dict = rnnlm_args.char_list_dict
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(word_dict), rnnlm_args.layer,
rnnlm_args.unit))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict,
char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor, word_dict,
char_dict))
# gpu
if args.ngpu == 1:
gpu_id = list(range(args.ngpu))
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.trans_json, 'rb') as f:
js = json.load(f)['utts']
new_js = {}
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False})
if args.batchsize == 0:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)[0][0]
nbest_hyps = model.translate(feat, args, train_args.char_list,
rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
else:
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data
keys = list(js.keys())
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
batch = [(name, js[name]) for name in names]
feats = load_inputs_and_targets(batch)[0]
nbest_hyps = model.translate_batch(feats,
args,
train_args.char_list,
rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp,
train_args.char_list)
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))