From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/models/contextual_paraformer/model.py | 349 ++++++++++++++++++++++++++++++++++-----------------------
1 files changed, 209 insertions(+), 140 deletions(-)
diff --git a/funasr/models/contextual_paraformer/model.py b/funasr/models/contextual_paraformer/model.py
index 10bbf9d..fd88220 100644
--- a/funasr/models/contextual_paraformer/model.py
+++ b/funasr/models/contextual_paraformer/model.py
@@ -17,9 +17,6 @@
from distutils.version import LooseVersion
from funasr.register import tables
-from funasr.losses.label_smoothing_loss import (
- LabelSmoothingLoss, # noqa: H301
-)
from funasr.utils import postprocess_utils
from funasr.metrics.compute_acc import th_accuracy
from funasr.models.paraformer.model import Paraformer
@@ -29,7 +26,7 @@
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-import pdb
+
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -47,14 +44,14 @@
FunASR: A Fundamental End-to-End Speech Recognition Toolkit
https://arxiv.org/abs/2305.11013
"""
-
+
def __init__(
self,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
-
+
self.target_buffer_length = kwargs.get("target_buffer_length", -1)
inner_dim = kwargs.get("inner_dim", 256)
bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
@@ -63,14 +60,16 @@
crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
- if bias_encoder_type == 'lstm':
- self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
+ if bias_encoder_type == "lstm":
+ self.bias_encoder = torch.nn.LSTM(
+ inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate
+ )
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
- elif bias_encoder_type == 'mean':
+ elif bias_encoder_type == "mean":
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
else:
logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
-
+
if self.target_buffer_length > 0:
self.hotword_buffer = None
self.length_record = []
@@ -81,7 +80,6 @@
self.attn_loss = torch.nn.L1Loss()
self.crit_attn_smooth = crit_attn_smooth
-
def forward(
self,
speech: torch.Tensor,
@@ -91,74 +89,73 @@
**kwargs,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
-
+
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
"""
- if len(text_lengths.size()) > 1:
- text_lengths = text_lengths[:, 0]
- if len(speech_lengths.size()) > 1:
- speech_lengths = speech_lengths[:, 0]
- pdb.set_trace()
+ text_lengths = text_lengths.squeeze()
+ speech_lengths = speech_lengths.squeeze()
+
batch_size = speech.shape[0]
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
- dha_pad = kwargs.get("dha_pad")
- pdb.set_trace()
+ # dha_pad = kwargs.get("dha_pad")
+
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
- pdb.set_trace()
loss_ctc, cer_ctc = None, None
-
+
stats = dict()
-
+
# 1. CTC branch
if self.ctc_weight != 0.0:
loss_ctc, cer_ctc = self._calc_ctc_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
-
+
# Collect CTC branch stats
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
stats["cer_ctc"] = cer_ctc
-
- pdb.set_trace()
+
# 2b. Attention decoder branch
loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
)
- pdb.set_trace()
+
# 3. CTC-Att loss definition
if self.ctc_weight == 0.0:
loss = loss_att + loss_pre * self.predictor_weight
else:
- loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
-
+ loss = (
+ self.ctc_weight * loss_ctc
+ + (1 - self.ctc_weight) * loss_att
+ + loss_pre * self.predictor_weight
+ )
+
if loss_ideal is not None:
loss = loss + loss_ideal * self.crit_attn_weight
stats["loss_ideal"] = loss_ideal.detach().cpu()
-
+
# Collect Attn branch stats
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
stats["acc"] = acc_att
stats["cer"] = cer_att
stats["wer"] = wer_att
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
-
+
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
+
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
-
-
+
def _calc_att_clas_loss(
self,
encoder_out: torch.Tensor,
@@ -168,46 +165,53 @@
hotword_pad: torch.Tensor,
hotword_lengths: torch.Tensor,
):
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
- encoder_out.device)
- pdb.set_trace()
+ encoder_out_mask = (
+ ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
+ ).to(encoder_out.device)
+
if self.predictor_bias == 1:
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
ys_pad_lens = ys_pad_lens + self.predictor_bias
- pdb.set_trace()
- pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
- ignore_id=self.ignore_id)
- pdb.set_trace()
+
+ pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(
+ encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
+ )
# -1. bias encoder
if self.use_decoder_embedding:
hw_embed = self.decoder.embed(hotword_pad)
else:
hw_embed = self.bias_embed(hotword_pad)
- pdb.set_trace()
+
hw_embed, (_, _) = self.bias_encoder(hw_embed)
- pdb.set_trace()
_ind = np.arange(0, hotword_pad.shape[0]).tolist()
selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
- pdb.set_trace()
+
# 0. sampler
decoder_out_1st = None
if self.sampling_ratio > 0.0:
- if self.step_cur < 2:
- logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
- sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
- pre_acoustic_embeds, contextual_info)
+
+ sematic_embeds, decoder_out_1st = self.sampler(
+ encoder_out,
+ encoder_out_lens,
+ ys_pad,
+ ys_pad_lens,
+ pre_acoustic_embeds,
+ contextual_info,
+ )
else:
- if self.step_cur < 2:
- logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
sematic_embeds = pre_acoustic_embeds
- pdb.set_trace()
+
# 1. Forward decoder
decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
+ encoder_out,
+ encoder_out_lens,
+ sematic_embeds,
+ ys_pad_lens,
+ contextual_info=contextual_info,
)
decoder_out, _ = decoder_outs[0], decoder_outs[1]
- '''
+ """
if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
attn_non_blank = attn[:,:,:,:-1]
@@ -215,9 +219,9 @@
loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
else:
loss_ideal = None
- '''
+ """
loss_ideal = None
- pdb.set_trace()
+
if decoder_out_1st is None:
decoder_out_1st = decoder_out
# 2. Compute attention loss
@@ -228,19 +232,28 @@
ignore_label=self.ignore_id,
)
loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
-
+
# Compute cer/wer using attention-decoder
if self.training or self.error_calculator is None:
cer_att, wer_att = None, None
else:
ys_hat = decoder_out_1st.argmax(dim=-1)
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
-
+
return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal
-
-
- def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
- tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+
+ def sampler(
+ self,
+ encoder_out,
+ encoder_out_lens,
+ ys_pad,
+ ys_pad_lens,
+ pre_acoustic_embeds,
+ contextual_info,
+ ):
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(
+ ys_pad.device
+ )
ys_pad = ys_pad * tgt_mask[:, :, 0]
if self.share_embedding:
ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
@@ -248,7 +261,11 @@
ys_pad_embed = self.decoder.embed(ys_pad)
with torch.no_grad():
decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
+ encoder_out,
+ encoder_out_lens,
+ pre_acoustic_embeds,
+ ys_pad_lens,
+ contextual_info=contextual_info,
)
decoder_out, _ = decoder_outs[0], decoder_outs[1]
pred_tokens = decoder_out.argmax(-1)
@@ -258,22 +275,35 @@
input_mask = torch.ones_like(nonpad_positions)
bsz, seq_len = ys_pad.size()
for li in range(bsz):
- target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ target_num = (
+ ((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio
+ ).long()
if target_num > 0:
- input_mask[li].scatter_(dim=0,
- index=torch.randperm(seq_lens[li])[:target_num].to(pre_acoustic_embeds.device),
- value=0)
+ input_mask[li].scatter_(
+ dim=0,
+ index=torch.randperm(seq_lens[li])[:target_num].to(
+ pre_acoustic_embeds.device
+ ),
+ value=0,
+ )
input_mask = input_mask.eq(1)
input_mask = input_mask.masked_fill(~nonpad_positions, False)
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
-
- sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
- input_mask_expand_dim, 0)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(
+ ~input_mask_expand_dim, 0
+ ) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0)
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
-
-
- def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None,
- clas_scale=1.0):
+
+ def cal_decoder_with_predictor(
+ self,
+ encoder_out,
+ encoder_out_lens,
+ sematic_embeds,
+ ys_pad_lens,
+ hw_list=None,
+ clas_scale=1.0,
+ ):
if hw_list is None:
hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
hw_list_pad = pad_list(hw_list, 0)
@@ -285,63 +315,80 @@
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
else:
hw_lengths = [len(i) for i in hw_list]
- hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
+ hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(
+ encoder_out.device
+ )
if self.use_decoder_embedding:
hw_embed = self.decoder.embed(hw_list_pad)
else:
hw_embed = self.bias_embed(hw_list_pad)
- hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
- enforce_sorted=False)
+ hw_embed = torch.nn.utils.rnn.pack_padded_sequence(
+ hw_embed, hw_lengths, batch_first=True, enforce_sorted=False
+ )
_, (h_n, _) = self.bias_encoder(hw_embed)
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
- pdb.set_trace()
+
decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
+ encoder_out,
+ encoder_out_lens,
+ sematic_embeds,
+ ys_pad_lens,
+ contextual_info=hw_embed,
+ clas_scale=clas_scale,
)
- pdb.set_trace()
+
decoder_out = decoder_outs[0]
decoder_out = torch.log_softmax(decoder_out, dim=-1)
return decoder_out, ys_pad_lens
-
- def inference(self,
- data_in,
- data_lengths=None,
- key: list = None,
- tokenizer=None,
- frontend=None,
- **kwargs,
- ):
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
# init beamsearch
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
- is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
+ is_use_lm = (
+ kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
+ )
if self.beam_search is None and (is_use_lm or is_use_ctc):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
-
+
# extract fbank feats
time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ audio_sample_list = load_audio_text_image_video(
+ data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)
+ )
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=frontend)
+ speech, speech_lengths = extract_fbank(
+ audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
+ )
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data[
- "batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ )
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
# hotword
- self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
+ self.hotword_list = self.generate_hotwords_list(
+ kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend
+ )
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
@@ -350,43 +397,47 @@
# predictor
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
- predictor_outs[2], predictor_outs[3]
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
+ predictor_outs[0],
+ predictor_outs[1],
+ predictor_outs[2],
+ predictor_outs[3],
+ )
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
-
- decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
- pre_acoustic_embeds,
- pre_token_length,
- hw_list=self.hotword_list,
- clas_scale=kwargs.get("clas_scale", 1.0))
+
+ decoder_outs = self.cal_decoder_with_predictor(
+ encoder_out,
+ encoder_out_lens,
+ pre_acoustic_embeds,
+ pre_token_length,
+ hw_list=self.hotword_list,
+ clas_scale=kwargs.get("clas_scale", 1.0),
+ )
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
-
- pdb.set_trace()
+
results = []
b, n, d = decoder_out.size()
- pdb.set_trace()
for i in range(b):
- x = encoder_out[i, :encoder_out_lens[i], :]
- am_scores = decoder_out[i, :pre_token_length[i], :]
- pdb.set_trace()
+ x = encoder_out[i, : encoder_out_lens[i], :]
+ am_scores = decoder_out[i, : pre_token_length[i], :]
if self.beam_search is not None:
nbest_hyps = self.beam_search(
- x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
- minlenratio=kwargs.get("minlenratio", 0.0)
+ x=x,
+ am_scores=am_scores,
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
)
-
+
nbest_hyps = nbest_hyps[: self.nbest]
else:
-
+
yseq = am_scores.argmax(dim=-1)
score = am_scores.max(dim=-1)[0]
score = torch.sum(score, dim=-1)
# pad with mask tokens to ensure compatibility with sos/eos tokens
- yseq = torch.tensor(
- [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
- )
+ yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for nbest_idx, hyp in enumerate(nbest_hyps):
ibest_writer = None
@@ -394,26 +445,29 @@
if not hasattr(self, "writer"):
self.writer = DatadirWriter(kwargs.get("output_dir"))
ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
-
+
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
token_int = hyp.yseq[1:last_pos]
else:
token_int = hyp.yseq[1:last_pos].tolist()
-
+
# remove blank symbol id, which is assumed to be 0
token_int = list(
- filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
-
+ filter(
+ lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
+ )
+ )
+
if tokenizer is not None:
# Change integer-ids to tokens
token = tokenizer.ids2tokens(token_int)
text = tokenizer.tokens2text(token)
-
+
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
result_i = {"key": key[i], "text": text_postprocessed}
-
+
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["text"][key[i]] = text
@@ -421,9 +475,8 @@
else:
result_i = {"key": key[i], "token_int": token_int}
results.append(result_i)
-
- return results, meta_data
+ return results, meta_data
def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
def load_seg_dict(seg_dict_file):
@@ -437,9 +490,9 @@
value = s[1:]
seg_dict[key] = " ".join(value)
return seg_dict
-
+
def seg_tokenize(txt, seg_dict):
- pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
+ pattern = re.compile(r"^[\u4E00-\u9FA50-9]+$")
out_txt = ""
for word in txt:
word = word.lower()
@@ -455,11 +508,11 @@
else:
out_txt += "<unk>" + " "
return out_txt.strip().split()
-
+
seg_dict = None
if frontend.cmvn_file is not None:
model_dir = os.path.dirname(frontend.cmvn_file)
- seg_dict_file = os.path.join(model_dir, 'seg_dict')
+ seg_dict_file = os.path.join(model_dir, "seg_dict")
if os.path.exists(seg_dict_file):
seg_dict = load_seg_dict(seg_dict_file)
else:
@@ -468,11 +521,11 @@
if hotword_list_or_file is None:
hotword_list = None
# for local txt inputs
- elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
+ elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith(".txt"):
logging.info("Attempting to parse hotwords from local txt...")
hotword_list = []
hotword_str_list = []
- with codecs.open(hotword_list_or_file, 'r') as fin:
+ with codecs.open(hotword_list_or_file, "r") as fin:
for line in fin.readlines():
hw = line.strip()
hw_list = hw.split()
@@ -481,11 +534,14 @@
hotword_str_list.append(hw)
hotword_list.append(tokenizer.tokens2ids(hw_list))
hotword_list.append([self.sos])
- hotword_str_list.append('<s>')
- logging.info("Initialized hotword list from file: {}, hotword list: {}."
- .format(hotword_list_or_file, hotword_str_list))
+ hotword_str_list.append("<s>")
+ logging.info(
+ "Initialized hotword list from file: {}, hotword list: {}.".format(
+ hotword_list_or_file, hotword_str_list
+ )
+ )
# for url, download and generate txt
- elif hotword_list_or_file.startswith('http'):
+ elif hotword_list_or_file.startswith("http"):
logging.info("Attempting to parse hotwords from url...")
work_dir = tempfile.TemporaryDirectory().name
if not os.path.exists(work_dir):
@@ -496,7 +552,7 @@
hotword_list_or_file = text_file_path
hotword_list = []
hotword_str_list = []
- with codecs.open(hotword_list_or_file, 'r') as fin:
+ with codecs.open(hotword_list_or_file, "r") as fin:
for line in fin.readlines():
hw = line.strip()
hw_list = hw.split()
@@ -505,11 +561,14 @@
hotword_str_list.append(hw)
hotword_list.append(tokenizer.tokens2ids(hw_list))
hotword_list.append([self.sos])
- hotword_str_list.append('<s>')
- logging.info("Initialized hotword list from file: {}, hotword list: {}."
- .format(hotword_list_or_file, hotword_str_list))
+ hotword_str_list.append("<s>")
+ logging.info(
+ "Initialized hotword list from file: {}, hotword list: {}.".format(
+ hotword_list_or_file, hotword_str_list
+ )
+ )
# for text str input
- elif not hotword_list_or_file.endswith('.txt'):
+ elif not hotword_list_or_file.endswith(".txt"):
logging.info("Attempting to parse hotwords as str...")
hotword_list = []
hotword_str_list = []
@@ -520,9 +579,19 @@
hw_list = seg_tokenize(hw_list, seg_dict)
hotword_list.append(tokenizer.tokens2ids(hw_list))
hotword_list.append([self.sos])
- hotword_str_list.append('<s>')
+ hotword_str_list.append("<s>")
logging.info("Hotword list: {}.".format(hotword_str_list))
else:
hotword_list = None
return hotword_list
+ def export(
+ self,
+ **kwargs,
+ ):
+ if "max_seq_len" not in kwargs:
+ kwargs["max_seq_len"] = 512
+ from .export_meta import export_rebuild_model
+
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
--
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