From ee06cb9c6870d9e1579015aabfe1a84a61a5c087 Mon Sep 17 00:00:00 2001
From: 九耳 <mengzhe.cmz@alibaba-inc.com>
Date: 星期二, 28 二月 2023 18:11:12 +0800
Subject: [PATCH] punctuation:add training code, support largedataset
---
funasr/modules/mask.py | 17
funasr/punctuation/abs_model.py | 4
funasr/datasets/large_datasets/build_dataloader.py | 8
funasr/datasets/large_datasets/utils/padding.py | 5
funasr/tasks/abs_task.py | 2
funasr/punctuation/espnet_model.py | 55 +-
funasr/bin/punc_train.py | 43 ++
funasr/datasets/large_datasets/dataset.py | 16
funasr/tasks/punctuation.py | 25
funasr/modules/attention.py | 12
funasr/datasets/preprocessor.py | 100 +++++
funasr/punctuation/sanm_encoder.py | 590 +++++++++++++++++++++++++++++++
funasr/bin/punc_train_vadrealtime.py | 44 ++
funasr/punctuation/target_delay_transformer.py | 5
funasr/datasets/large_datasets/utils/tokenize.py | 29 +
funasr/punctuation/vad_realtime_transformer.py | 132 ++++++
16 files changed, 1,042 insertions(+), 45 deletions(-)
diff --git a/funasr/bin/punc_train.py b/funasr/bin/punc_train.py
new file mode 100644
index 0000000..61b63ec
--- /dev/null
+++ b/funasr/bin/punc_train.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+import os
+from funasr.tasks.punctuation import PunctuationTask
+
+
+def parse_args():
+ parser = PunctuationTask.get_parser()
+ parser.add_argument(
+ "--gpu_id",
+ type=int,
+ default=0,
+ help="local gpu id.",
+ )
+ parser.add_argument(
+ "--punc_list",
+ type=str,
+ default=None,
+ help="Punctuation list",
+ )
+ args = parser.parse_args()
+ return args
+
+
+def main(args=None, cmd=None):
+ """
+ punc training.
+ """
+ PunctuationTask.main(args=args, cmd=cmd)
+
+
+if __name__ == "__main__":
+ args = parse_args()
+
+ # setup local gpu_id
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
+
+ # DDP settings
+ if args.ngpu > 1:
+ args.distributed = True
+ else:
+ args.distributed = False
+
+ main(args=args)
diff --git a/funasr/bin/punc_train_vadrealtime.py b/funasr/bin/punc_train_vadrealtime.py
new file mode 100644
index 0000000..c5afaad
--- /dev/null
+++ b/funasr/bin/punc_train_vadrealtime.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+import os
+from funasr.tasks.punctuation import PunctuationTask
+
+
+def parse_args():
+ parser = PunctuationTask.get_parser()
+ parser.add_argument(
+ "--gpu_id",
+ type=int,
+ default=0,
+ help="local gpu id.",
+ )
+ parser.add_argument(
+ "--punc_list",
+ type=str,
+ default=None,
+ help="Punctuation list",
+ )
+ args = parser.parse_args()
+ return args
+
+
+def main(args=None, cmd=None):
+ """
+ punc training.
+ """
+ PunctuationTask.main(args=args, cmd=cmd)
+
+
+if __name__ == "__main__":
+ args = parse_args()
+
+ # setup local gpu_id
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
+
+ # DDP settings
+ if args.ngpu > 1:
+ args.distributed = True
+ else:
+ args.distributed = False
+ assert args.num_worker_count == 1
+
+ main(args=args)
diff --git a/funasr/datasets/large_datasets/build_dataloader.py b/funasr/datasets/large_datasets/build_dataloader.py
index 8f7fd0b..093ad60 100644
--- a/funasr/datasets/large_datasets/build_dataloader.py
+++ b/funasr/datasets/large_datasets/build_dataloader.py
@@ -34,16 +34,20 @@
return seg_dict
class ArkDataLoader(AbsIterFactory):
- def __init__(self, data_list, dict_file, dataset_conf, seg_dict_file=None, mode="train"):
+ def __init__(self, data_list, dict_file, dataset_conf, seg_dict_file=None, punc_dict_file=None, mode="train"):
symbol_table = read_symbol_table(dict_file) if dict_file is not None else None
if seg_dict_file is not None:
seg_dict = load_seg_dict(seg_dict_file)
else:
seg_dict = None
+ if punc_dict_file is not None:
+ punc_dict = read_symbol_table(punc_dict_file)
+ else:
+ punc_dict = None
self.dataset_conf = dataset_conf
logging.info("dataloader config: {}".format(self.dataset_conf))
batch_mode = self.dataset_conf.get("batch_mode", "padding")
- self.dataset = Dataset(data_list, symbol_table, seg_dict,
+ self.dataset = Dataset(data_list, symbol_table, seg_dict, punc_dict,
self.dataset_conf, mode=mode, batch_mode=batch_mode)
def build_iter(self, epoch, shuffle=True):
diff --git a/funasr/datasets/large_datasets/dataset.py b/funasr/datasets/large_datasets/dataset.py
index 2123737..61231d2 100644
--- a/funasr/datasets/large_datasets/dataset.py
+++ b/funasr/datasets/large_datasets/dataset.py
@@ -127,14 +127,17 @@
sample_dict["key"] = key
else:
text = item
- sample_dict[data_name] = text.strip().split()[1:]
+ segs = text.strip().split()
+ sample_dict[data_name] = segs[1:]
+ if "key" not in sample_dict:
+ sample_dict["key"] = segs[0]
yield sample_dict
self.close_reader(reader_list)
def len_fn_example(data):
- return len(data)
+ return 1
def len_fn_token(data):
@@ -148,6 +151,7 @@
def Dataset(data_list_file,
dict,
seg_dict,
+ punc_dict,
conf,
mode="train",
batch_mode="padding"):
@@ -162,7 +166,7 @@
dataset = FilterIterDataPipe(dataset, fn=filter_fn)
if "text" in data_names:
- vocab = {'vocab': dict, 'seg_dict': seg_dict}
+ vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict}
tokenize_fn = partial(tokenize, **vocab)
dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)
@@ -191,6 +195,10 @@
sort_size=sort_size,
batch_mode=batch_mode)
- dataset = MapperIterDataPipe(dataset, fn=padding if batch_mode == "padding" else clipping)
+ int_pad_value = conf.get("int_pad_value", -1)
+ float_pad_value = conf.get("float_pad_value", 0.0)
+ padding_conf = {"int_pad_value": int_pad_value, "float_pad_value": float_pad_value}
+ padding_fn = partial(padding, **padding_conf)
+ dataset = MapperIterDataPipe(dataset, fn=padding_fn if batch_mode == "padding" else clipping)
return dataset
diff --git a/funasr/datasets/large_datasets/utils/padding.py b/funasr/datasets/large_datasets/utils/padding.py
index e814b1c..e0feac6 100644
--- a/funasr/datasets/large_datasets/utils/padding.py
+++ b/funasr/datasets/large_datasets/utils/padding.py
@@ -6,9 +6,8 @@
def padding(data, float_pad_value=0.0, int_pad_value=-1):
assert isinstance(data, list)
assert "key" in data[0]
- assert "speech" in data[0]
- assert "text" in data[0]
-
+ assert "speech" in data[0] or "text" in data[0]
+
keys = [x["key"] for x in data]
batch = {}
diff --git a/funasr/datasets/large_datasets/utils/tokenize.py b/funasr/datasets/large_datasets/utils/tokenize.py
index 0c01885..caeb426 100644
--- a/funasr/datasets/large_datasets/utils/tokenize.py
+++ b/funasr/datasets/large_datasets/utils/tokenize.py
@@ -31,22 +31,43 @@
def tokenize(data,
vocab=None,
- seg_dict=None):
+ seg_dict=None,
+ punc_dict=None):
assert "text" in data
assert isinstance(vocab, dict)
text = data["text"]
token = []
+ vad = -2
if seg_dict is not None:
assert isinstance(seg_dict, dict)
txt = forward_segment("".join(text).lower(), seg_dict)
text = seg_tokenize(txt, seg_dict)
-
- for x in text:
- if x in vocab:
+
+ length = len(text)
+ for i in range(length):
+ x = text[i]
+ if i == length-1 and "punc" in data and text[i].startswith("vad:"):
+ vad = x[-1][4:]
+ if len(vad) == 0:
+ vad = -1
+ else:
+ vad = int(vad)
+ elif x in vocab:
token.append(vocab[x])
else:
token.append(vocab['<unk>'])
+ if "punc" in data and punc_dict is not None:
+ punc_token = []
+ for punc in data["punc"]:
+ if punc in punc_dict:
+ punc_token.append(punc_dict[punc])
+ else:
+ punc_token.append(punc_dict["_"])
+ data["punc"] = np.array(punc_token)
+
data["text"] = np.array(token)
+ if vad is not -2:
+ data["vad_indexes"]=np.array([vad], dtype=np.int64)
return data
diff --git a/funasr/datasets/preprocessor.py b/funasr/datasets/preprocessor.py
index 8e86794..20a3791 100644
--- a/funasr/datasets/preprocessor.py
+++ b/funasr/datasets/preprocessor.py
@@ -704,3 +704,103 @@
del data[self.split_text_name]
return result
+class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
+ def __init__(
+ self,
+ train: bool,
+ token_type: List[str] = [None],
+ token_list: List[Union[Path, str, Iterable[str]]] = [None],
+ bpemodel: List[Union[Path, str, Iterable[str]]] = [None],
+ text_cleaner: Collection[str] = None,
+ g2p_type: str = None,
+ unk_symbol: str = "<unk>",
+ space_symbol: str = "<space>",
+ non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
+ delimiter: str = None,
+ rir_scp: str = None,
+ rir_apply_prob: float = 1.0,
+ noise_scp: str = None,
+ noise_apply_prob: float = 1.0,
+ noise_db_range: str = "3_10",
+ speech_volume_normalize: float = None,
+ speech_name: str = "speech",
+ text_name: List[str] = ["text"],
+ vad_name: str = "vad_indexes",
+ ):
+ # TODO(jiatong): sync with Kamo and Jing on interface for preprocessor
+ super().__init__(
+ train=train,
+ token_type=token_type[0],
+ token_list=token_list[0],
+ bpemodel=bpemodel[0],
+ text_cleaner=text_cleaner,
+ g2p_type=g2p_type,
+ unk_symbol=unk_symbol,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ delimiter=delimiter,
+ speech_name=speech_name,
+ text_name=text_name[0],
+ rir_scp=rir_scp,
+ rir_apply_prob=rir_apply_prob,
+ noise_scp=noise_scp,
+ noise_apply_prob=noise_apply_prob,
+ noise_db_range=noise_db_range,
+ speech_volume_normalize=speech_volume_normalize,
+ )
+
+ assert (
+ len(token_type) == len(token_list) == len(bpemodel) == len(text_name)
+ ), "token_type, token_list, bpemodel, or processing text_name mismatched"
+ self.num_tokenizer = len(token_type)
+ self.tokenizer = []
+ self.token_id_converter = []
+
+ for i in range(self.num_tokenizer):
+ if token_type[i] is not None:
+ if token_list[i] is None:
+ raise ValueError("token_list is required if token_type is not None")
+
+ self.tokenizer.append(
+ build_tokenizer(
+ token_type=token_type[i],
+ bpemodel=bpemodel[i],
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ g2p_type=g2p_type,
+ )
+ )
+ self.token_id_converter.append(
+ TokenIDConverter(
+ token_list=token_list[i],
+ unk_symbol=unk_symbol,
+ )
+ )
+ else:
+ self.tokenizer.append(None)
+ self.token_id_converter.append(None)
+
+ self.text_cleaner = TextCleaner(text_cleaner)
+ self.text_name = text_name # override the text_name from CommonPreprocessor
+ self.vad_name = vad_name
+
+ def _text_process(
+ self, data: Dict[str, Union[str, np.ndarray]]
+ ) -> Dict[str, np.ndarray]:
+ for i in range(self.num_tokenizer):
+ text_name = self.text_name[i]
+ if text_name in data and self.tokenizer[i] is not None:
+ text = data[text_name]
+ text = self.text_cleaner(text)
+ tokens = self.tokenizer[i].text2tokens(text)
+ if "vad:" in tokens[-1]:
+ vad = tokens[-1][4:]
+ tokens = tokens[:-1]
+ if len(vad) == 0:
+ vad = -1
+ else:
+ vad = int(vad)
+ data[self.vad_name] = np.array([vad], dtype=np.int64)
+ text_ints = self.token_id_converter[i].tokens2ids(tokens)
+ data[text_name] = np.array(text_ints, dtype=np.int64)
diff --git a/funasr/modules/attention.py b/funasr/modules/attention.py
index c47d96d..6277005 100644
--- a/funasr/modules/attention.py
+++ b/funasr/modules/attention.py
@@ -439,6 +439,18 @@
att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
return att_outs + fsmn_memory
+class MultiHeadedAttentionSANMwithMask(MultiHeadedAttentionSANM):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
+ q_h, k_h, v_h, v = self.forward_qkv(x)
+ fsmn_memory = self.forward_fsmn(v, mask[0], mask_shfit_chunk)
+ q_h = q_h * self.d_k ** (-0.5)
+ scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+ att_outs = self.forward_attention(v_h, scores, mask[1], mask_att_chunk_encoder)
+ return att_outs + fsmn_memory
+
class MultiHeadedAttentionSANMDecoder(nn.Module):
"""Multi-Head Attention layer.
diff --git a/funasr/modules/mask.py b/funasr/modules/mask.py
index 8f068e1..a8c168b 100644
--- a/funasr/modules/mask.py
+++ b/funasr/modules/mask.py
@@ -33,3 +33,20 @@
ys_mask = ys_in_pad != ignore_id
m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0)
return ys_mask.unsqueeze(-2) & m
+
+def vad_mask(size, vad_pos, device="cpu", dtype=torch.bool):
+ """Create mask for decoder self-attention.
+
+ :param int size: size of mask
+ :param int vad_pos: index of vad index
+ :param str device: "cpu" or "cuda" or torch.Tensor.device
+ :param torch.dtype dtype: result dtype
+ :rtype: torch.Tensor (B, Lmax, Lmax)
+ """
+ ret = torch.ones(size, size, device=device, dtype=dtype)
+ if vad_pos <= 0 or vad_pos >= size:
+ return ret
+ sub_corner = torch.zeros(
+ vad_pos - 1, size - vad_pos, device=device, dtype=dtype)
+ ret[0:vad_pos - 1, vad_pos:] = sub_corner
+ return ret
diff --git a/funasr/punctuation/abs_model.py b/funasr/punctuation/abs_model.py
index 5f6afb7..404d5e8 100644
--- a/funasr/punctuation/abs_model.py
+++ b/funasr/punctuation/abs_model.py
@@ -25,3 +25,7 @@
@abstractmethod
def forward(self, input: torch.Tensor, hidden: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
raise NotImplementedError
+
+ @abstractmethod
+ def with_vad(self) -> bool:
+ raise NotImplementedError
diff --git a/funasr/punctuation/espnet_model.py b/funasr/punctuation/espnet_model.py
index 65edaad..c513779 100644
--- a/funasr/punctuation/espnet_model.py
+++ b/funasr/punctuation/espnet_model.py
@@ -14,15 +14,18 @@
class ESPnetPunctuationModel(AbsESPnetModel):
- def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0):
+ def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0, punc_weight: list = None):
assert check_argument_types()
super().__init__()
self.punc_model = punc_model
+ self.punc_weight = torch.Tensor(punc_weight)
self.sos = 1
self.eos = 2
# ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
self.ignore_id = ignore_id
+ if self.punc_model.with_vad():
+ print("This is a vad puncuation model.")
def nll(
self,
@@ -31,6 +34,8 @@
text_lengths: torch.Tensor,
punc_lengths: torch.Tensor,
max_length: Optional[int] = None,
+ vad_indexes: Optional[torch.Tensor] = None,
+ vad_indexes_lengths: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute negative log likelihood(nll)
@@ -49,19 +54,16 @@
else:
text = text[:, :max_length]
punc = punc[:, :max_length]
- # 1. Create a sentence pair like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
- # text: (Batch, Length) -> x, y: (Batch, Length + 1)
- #x = F.pad(text, [1, 0], "constant", self.eos)
- #t = F.pad(text, [0, 1], "constant", self.ignore_id)
- #for i, l in enumerate(text_lengths):
- # t[i, l] = self.sos
- #x_lengths = text_lengths + 1
+
+ if self.punc_model.with_vad():
+ # Should be VadRealtimeTransformer
+ assert vad_indexes is not None
+ y, _ = self.punc_model(text, text_lengths, vad_indexes)
+ else:
+ # Should be TargetDelayTransformer,
+ y, _ = self.punc_model(text, text_lengths)
- # 2. Forward Language model
- # x: (Batch, Length) -> y: (Batch, Length, NVocab)
- y, _ = self.punc_model(text, text_lengths)
-
- # 3. Calc negative log likelihood
+ # Calc negative log likelihood
# nll: (BxL,)
if self.training == False:
_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
@@ -72,7 +74,8 @@
nll = torch.Tensor([f1_score]).repeat(text_lengths.sum())
return nll, text_lengths
else:
- nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), reduction="none", ignore_index=self.ignore_id)
+ self.punc_weight = self.punc_weight.to(punc.device)
+ nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), self.punc_weight, reduction="none", ignore_index=self.ignore_id)
# nll: (BxL,) -> (BxL,)
if max_length is None:
nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0)
@@ -130,9 +133,16 @@
assert x_lengths.size(0) == total_num
return nll, x_lengths
- def forward(self, text: torch.Tensor, punc: torch.Tensor, text_lengths: torch.Tensor,
- punc_lengths: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
- nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths)
+ def forward(
+ self,
+ text: torch.Tensor,
+ punc: torch.Tensor,
+ text_lengths: torch.Tensor,
+ punc_lengths: torch.Tensor,
+ vad_indexes: Optional[torch.Tensor] = None,
+ vad_indexes_lengths: Optional[torch.Tensor] = None,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes)
ntokens = y_lengths.sum()
loss = nll.sum() / ntokens
stats = dict(loss=loss.detach())
@@ -145,5 +155,12 @@
text_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
return {}
- def inference(self, text: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
- return self.punc_model(text, text_lengths)
+ def inference(self,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ vad_indexes: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]:
+ if self.punc_model.with_vad():
+ assert vad_indexes is not None
+ return self.punc_model(text, text_lengths, vad_indexes)
+ else:
+ return self.punc_model(text, text_lengths)
diff --git a/funasr/punctuation/sanm_encoder.py b/funasr/punctuation/sanm_encoder.py
new file mode 100644
index 0000000..8962093
--- /dev/null
+++ b/funasr/punctuation/sanm_encoder.py
@@ -0,0 +1,590 @@
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+import logging
+import torch
+import torch.nn as nn
+from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
+from typeguard import check_argument_types
+import numpy as np
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
+from funasr.modules.embedding import SinusoidalPositionEncoder
+from funasr.modules.layer_norm import LayerNorm
+from funasr.modules.multi_layer_conv import Conv1dLinear
+from funasr.modules.multi_layer_conv import MultiLayeredConv1d
+from funasr.modules.positionwise_feed_forward import (
+ PositionwiseFeedForward, # noqa: H301
+)
+from funasr.modules.repeat import repeat
+from funasr.modules.subsampling import Conv2dSubsampling
+from funasr.modules.subsampling import Conv2dSubsampling2
+from funasr.modules.subsampling import Conv2dSubsampling6
+from funasr.modules.subsampling import Conv2dSubsampling8
+from funasr.modules.subsampling import TooShortUttError
+from funasr.modules.subsampling import check_short_utt
+from funasr.models.ctc import CTC
+from funasr.models.encoder.abs_encoder import AbsEncoder
+
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.mask import subsequent_mask, vad_mask
+
+class EncoderLayerSANM(nn.Module):
+ def __init__(
+ self,
+ in_size,
+ size,
+ self_attn,
+ feed_forward,
+ dropout_rate,
+ normalize_before=True,
+ concat_after=False,
+ stochastic_depth_rate=0.0,
+ ):
+ """Construct an EncoderLayer object."""
+ super(EncoderLayerSANM, self).__init__()
+ self.self_attn = self_attn
+ self.feed_forward = feed_forward
+ self.norm1 = LayerNorm(in_size)
+ self.norm2 = LayerNorm(size)
+ self.dropout = nn.Dropout(dropout_rate)
+ self.in_size = in_size
+ self.size = size
+ self.normalize_before = normalize_before
+ self.concat_after = concat_after
+ if self.concat_after:
+ self.concat_linear = nn.Linear(size + size, size)
+ self.stochastic_depth_rate = stochastic_depth_rate
+ self.dropout_rate = dropout_rate
+
+ def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
+ """Compute encoded features.
+
+ Args:
+ x_input (torch.Tensor): Input tensor (#batch, time, size).
+ mask (torch.Tensor): Mask tensor for the input (#batch, time).
+ cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time).
+
+ """
+ skip_layer = False
+ # with stochastic depth, residual connection `x + f(x)` becomes
+ # `x <- x + 1 / (1 - p) * f(x)` at training time.
+ stoch_layer_coeff = 1.0
+ if self.training and self.stochastic_depth_rate > 0:
+ skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
+ stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
+
+ if skip_layer:
+ if cache is not None:
+ x = torch.cat([cache, x], dim=1)
+ return x, mask
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm1(x)
+
+ if self.concat_after:
+ x_concat = torch.cat((x, self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)), dim=-1)
+ if self.in_size == self.size:
+ x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
+ else:
+ x = stoch_layer_coeff * self.concat_linear(x_concat)
+ else:
+ if self.in_size == self.size:
+ x = residual + stoch_layer_coeff * self.dropout(
+ self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)
+ )
+ else:
+ x = stoch_layer_coeff * self.dropout(
+ self.self_attn(x, mask, mask_shfit_chunk=mask_shfit_chunk, mask_att_chunk_encoder=mask_att_chunk_encoder)
+ )
+ if not self.normalize_before:
+ x = self.norm1(x)
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm2(x)
+ x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm2(x)
+
+
+ return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
+
+class SANMEncoder(AbsEncoder):
+ """
+ author: Speech Lab, Alibaba Group, China
+
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: Optional[str] = "conv2d",
+ pos_enc_class=SinusoidalPositionEncoder,
+ normalize_before: bool = True,
+ concat_after: bool = False,
+ positionwise_layer_type: str = "linear",
+ positionwise_conv_kernel_size: int = 1,
+ padding_idx: int = -1,
+ interctc_layer_idx: List[int] = [],
+ interctc_use_conditioning: bool = False,
+ kernel_size : int = 11,
+ sanm_shfit : int = 0,
+ selfattention_layer_type: str = "sanm",
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self._output_size = output_size
+
+ if input_layer == "linear":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Linear(input_size, output_size),
+ torch.nn.LayerNorm(output_size),
+ torch.nn.Dropout(dropout_rate),
+ torch.nn.ReLU(),
+ pos_enc_class(output_size, positional_dropout_rate),
+ )
+ elif input_layer == "conv2d":
+ self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d2":
+ self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d6":
+ self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d8":
+ self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
+ elif input_layer == "embed":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+ SinusoidalPositionEncoder(),
+ )
+ elif input_layer is None:
+ if input_size == output_size:
+ self.embed = None
+ else:
+ self.embed = torch.nn.Linear(input_size, output_size)
+ elif input_layer == "pe":
+ self.embed = SinusoidalPositionEncoder()
+ else:
+ raise ValueError("unknown input_layer: " + input_layer)
+ self.normalize_before = normalize_before
+ if positionwise_layer_type == "linear":
+ positionwise_layer = PositionwiseFeedForward
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d":
+ positionwise_layer = MultiLayeredConv1d
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d-linear":
+ positionwise_layer = Conv1dLinear
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ else:
+ raise NotImplementedError("Support only linear or conv1d.")
+
+ if selfattention_layer_type == "selfattn":
+ encoder_selfattn_layer = MultiHeadedAttention
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ )
+
+ elif selfattention_layer_type == "sanm":
+ self.encoder_selfattn_layer = MultiHeadedAttentionSANM
+ encoder_selfattn_layer_args0 = (
+ attention_heads,
+ input_size,
+ output_size,
+ attention_dropout_rate,
+ kernel_size,
+ sanm_shfit,
+ )
+
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ output_size,
+ attention_dropout_rate,
+ kernel_size,
+ sanm_shfit,
+ )
+
+ self.encoders0 = repeat(
+ 1,
+ lambda lnum: EncoderLayerSANM(
+ input_size,
+ output_size,
+ self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ),
+ )
+
+ self.encoders = repeat(
+ num_blocks-1,
+ lambda lnum: EncoderLayerSANM(
+ output_size,
+ output_size,
+ self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ),
+ )
+ if self.normalize_before:
+ self.after_norm = LayerNorm(output_size)
+
+ self.interctc_layer_idx = interctc_layer_idx
+ if len(interctc_layer_idx) > 0:
+ assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
+ self.interctc_use_conditioning = interctc_use_conditioning
+ self.conditioning_layer = None
+ self.dropout = nn.Dropout(dropout_rate)
+
+ def output_size(self) -> int:
+ return self._output_size
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None,
+ ctc: CTC = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+ """Embed positions in tensor.
+
+ Args:
+ xs_pad: input tensor (B, L, D)
+ ilens: input length (B)
+ prev_states: Not to be used now.
+ Returns:
+ position embedded tensor and mask
+ """
+ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+ xs_pad *= self.output_size()**0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ elif (
+ isinstance(self.embed, Conv2dSubsampling)
+ or isinstance(self.embed, Conv2dSubsampling2)
+ or isinstance(self.embed, Conv2dSubsampling6)
+ or isinstance(self.embed, Conv2dSubsampling8)
+ ):
+ short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
+ if short_status:
+ raise TooShortUttError(
+ f"has {xs_pad.size(1)} frames and is too short for subsampling "
+ + f"(it needs more than {limit_size} frames), return empty results",
+ xs_pad.size(1),
+ limit_size,
+ )
+ xs_pad, masks = self.embed(xs_pad, masks)
+ else:
+ xs_pad = self.embed(xs_pad)
+
+ # xs_pad = self.dropout(xs_pad)
+ encoder_outs = self.encoders0(xs_pad, masks)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ intermediate_outs = []
+ if len(self.interctc_layer_idx) == 0:
+ encoder_outs = self.encoders(xs_pad, masks)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ else:
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ encoder_outs = encoder_layer(xs_pad, masks)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+ if layer_idx + 1 in self.interctc_layer_idx:
+ encoder_out = xs_pad
+
+ # intermediate outputs are also normalized
+ if self.normalize_before:
+ encoder_out = self.after_norm(encoder_out)
+
+ intermediate_outs.append((layer_idx + 1, encoder_out))
+
+ if self.interctc_use_conditioning:
+ ctc_out = ctc.softmax(encoder_out)
+ xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+ if self.normalize_before:
+ xs_pad = self.after_norm(xs_pad)
+
+ olens = masks.squeeze(1).sum(1)
+ if len(intermediate_outs) > 0:
+ return (xs_pad, intermediate_outs), olens, None
+ return xs_pad, olens, None
+
+class SANMVadEncoder(AbsEncoder):
+ """
+ author: Speech Lab, Alibaba Group, China
+
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: Optional[str] = "conv2d",
+ pos_enc_class=SinusoidalPositionEncoder,
+ normalize_before: bool = True,
+ concat_after: bool = False,
+ positionwise_layer_type: str = "linear",
+ positionwise_conv_kernel_size: int = 1,
+ padding_idx: int = -1,
+ interctc_layer_idx: List[int] = [],
+ interctc_use_conditioning: bool = False,
+ kernel_size : int = 11,
+ sanm_shfit : int = 0,
+ selfattention_layer_type: str = "sanm",
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self._output_size = output_size
+
+ if input_layer == "linear":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Linear(input_size, output_size),
+ torch.nn.LayerNorm(output_size),
+ torch.nn.Dropout(dropout_rate),
+ torch.nn.ReLU(),
+ pos_enc_class(output_size, positional_dropout_rate),
+ )
+ elif input_layer == "conv2d":
+ self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d2":
+ self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d6":
+ self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d8":
+ self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
+ elif input_layer == "embed":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+ SinusoidalPositionEncoder(),
+ )
+ elif input_layer is None:
+ if input_size == output_size:
+ self.embed = None
+ else:
+ self.embed = torch.nn.Linear(input_size, output_size)
+ elif input_layer == "pe":
+ self.embed = SinusoidalPositionEncoder()
+ else:
+ raise ValueError("unknown input_layer: " + input_layer)
+ self.normalize_before = normalize_before
+ if positionwise_layer_type == "linear":
+ positionwise_layer = PositionwiseFeedForward
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d":
+ positionwise_layer = MultiLayeredConv1d
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d-linear":
+ positionwise_layer = Conv1dLinear
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ else:
+ raise NotImplementedError("Support only linear or conv1d.")
+
+ if selfattention_layer_type == "selfattn":
+ encoder_selfattn_layer = MultiHeadedAttention
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ )
+
+ elif selfattention_layer_type == "sanm":
+ self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask
+ encoder_selfattn_layer_args0 = (
+ attention_heads,
+ input_size,
+ output_size,
+ attention_dropout_rate,
+ kernel_size,
+ sanm_shfit,
+ )
+
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ output_size,
+ attention_dropout_rate,
+ kernel_size,
+ sanm_shfit,
+ )
+
+ self.encoders0 = repeat(
+ 1,
+ lambda lnum: EncoderLayerSANM(
+ input_size,
+ output_size,
+ self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ),
+ )
+
+ self.encoders = repeat(
+ num_blocks-1,
+ lambda lnum: EncoderLayerSANM(
+ output_size,
+ output_size,
+ self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ),
+ )
+ if self.normalize_before:
+ self.after_norm = LayerNorm(output_size)
+
+ self.interctc_layer_idx = interctc_layer_idx
+ if len(interctc_layer_idx) > 0:
+ assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
+ self.interctc_use_conditioning = interctc_use_conditioning
+ self.conditioning_layer = None
+ self.dropout = nn.Dropout(dropout_rate)
+
+ def output_size(self) -> int:
+ return self._output_size
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ vad_indexes: torch.Tensor,
+ prev_states: torch.Tensor = None,
+ ctc: CTC = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+ """Embed positions in tensor.
+
+ Args:
+ xs_pad: input tensor (B, L, D)
+ ilens: input length (B)
+ prev_states: Not to be used now.
+ Returns:
+ position embedded tensor and mask
+ """
+ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+ sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0)
+ no_future_masks = masks & sub_masks
+ xs_pad *= self.output_size()**0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ elif (isinstance(self.embed, Conv2dSubsampling) or isinstance(self.embed, Conv2dSubsampling2)
+ or isinstance(self.embed, Conv2dSubsampling6) or isinstance(self.embed, Conv2dSubsampling8)):
+ short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
+ if short_status:
+ raise TooShortUttError(
+ f"has {xs_pad.size(1)} frames and is too short for subsampling " +
+ f"(it needs more than {limit_size} frames), return empty results",
+ xs_pad.size(1),
+ limit_size,
+ )
+ xs_pad, masks = self.embed(xs_pad, masks)
+ else:
+ xs_pad = self.embed(xs_pad)
+
+ # xs_pad = self.dropout(xs_pad)
+ mask_tup0 = [masks, no_future_masks]
+ encoder_outs = self.encoders0(xs_pad, mask_tup0)
+ xs_pad, _ = encoder_outs[0], encoder_outs[1]
+ intermediate_outs = []
+ #if len(self.interctc_layer_idx) == 0:
+ if False:
+ # Here, we should not use the repeat operation to do it for all layers.
+ encoder_outs = self.encoders(xs_pad, masks)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ else:
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ if layer_idx + 1 == len(self.encoders):
+ # This is last layer.
+ coner_mask = torch.ones(masks.size(0),
+ masks.size(-1),
+ masks.size(-1),
+ device=xs_pad.device,
+ dtype=torch.bool)
+ for word_index, length in enumerate(ilens):
+ coner_mask[word_index, :, :] = vad_mask(masks.size(-1),
+ vad_indexes[word_index],
+ device=xs_pad.device)
+ layer_mask = masks & coner_mask
+ else:
+ layer_mask = no_future_masks
+ mask_tup1 = [masks, layer_mask]
+ encoder_outs = encoder_layer(xs_pad, mask_tup1)
+ xs_pad, layer_mask = encoder_outs[0], encoder_outs[1]
+
+ if layer_idx + 1 in self.interctc_layer_idx:
+ encoder_out = xs_pad
+
+ # intermediate outputs are also normalized
+ if self.normalize_before:
+ encoder_out = self.after_norm(encoder_out)
+
+ intermediate_outs.append((layer_idx + 1, encoder_out))
+
+ if self.interctc_use_conditioning:
+ ctc_out = ctc.softmax(encoder_out)
+ xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+ if self.normalize_before:
+ xs_pad = self.after_norm(xs_pad)
+
+ olens = masks.squeeze(1).sum(1)
+ if len(intermediate_outs) > 0:
+ return (xs_pad, intermediate_outs), olens, None
+ return xs_pad, olens, None
+
diff --git a/funasr/punctuation/target_delay_transformer.py b/funasr/punctuation/target_delay_transformer.py
index 10cc5a8..219af26 100644
--- a/funasr/punctuation/target_delay_transformer.py
+++ b/funasr/punctuation/target_delay_transformer.py
@@ -8,7 +8,7 @@
from funasr.modules.embedding import PositionalEncoding
from funasr.modules.embedding import SinusoidalPositionEncoder
#from funasr.models.encoder.transformer_encoder import TransformerEncoder as Encoder
-from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
+from funasr.punctuation.sanm_encoder import SANMEncoder as Encoder
#from funasr.modules.mask import subsequent_n_mask
from funasr.punctuation.abs_model import AbsPunctuation
@@ -73,6 +73,9 @@
y = self.decoder(h)
return y, None
+ def with_vad(self):
+ return False
+
def score(self, y: torch.Tensor, state: Any, x: torch.Tensor) -> Tuple[torch.Tensor, Any]:
"""Score new token.
diff --git a/funasr/punctuation/vad_realtime_transformer.py b/funasr/punctuation/vad_realtime_transformer.py
new file mode 100644
index 0000000..35224f9
--- /dev/null
+++ b/funasr/punctuation/vad_realtime_transformer.py
@@ -0,0 +1,132 @@
+from typing import Any
+from typing import List
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+from funasr.modules.embedding import SinusoidalPositionEncoder
+from funasr.punctuation.sanm_encoder import SANMVadEncoder as Encoder
+from funasr.punctuation.abs_model import AbsPunctuation
+
+
+class VadRealtimeTransformer(AbsPunctuation):
+
+ def __init__(
+ self,
+ vocab_size: int,
+ punc_size: int,
+ pos_enc: str = None,
+ embed_unit: int = 128,
+ att_unit: int = 256,
+ head: int = 2,
+ unit: int = 1024,
+ layer: int = 4,
+ dropout_rate: float = 0.5,
+ kernel_size: int = 11,
+ sanm_shfit: int = 0,
+ ):
+ super().__init__()
+ if pos_enc == "sinusoidal":
+ # pos_enc_class = PositionalEncoding
+ pos_enc_class = SinusoidalPositionEncoder
+ elif pos_enc is None:
+
+ def pos_enc_class(*args, **kwargs):
+ return nn.Sequential() # indentity
+
+ else:
+ raise ValueError(f"unknown pos-enc option: {pos_enc}")
+
+ self.embed = nn.Embedding(vocab_size, embed_unit)
+ self.encoder = Encoder(
+ input_size=embed_unit,
+ output_size=att_unit,
+ attention_heads=head,
+ linear_units=unit,
+ num_blocks=layer,
+ dropout_rate=dropout_rate,
+ input_layer="pe",
+ # pos_enc_class=pos_enc_class,
+ padding_idx=0,
+ kernel_size=kernel_size,
+ sanm_shfit=sanm_shfit,
+ )
+ self.decoder = nn.Linear(att_unit, punc_size)
+
+
+# def _target_mask(self, ys_in_pad):
+# ys_mask = ys_in_pad != 0
+# m = subsequent_n_mask(ys_mask.size(-1), 5, device=ys_mask.device).unsqueeze(0)
+# return ys_mask.unsqueeze(-2) & m
+
+ def forward(self, input: torch.Tensor, text_lengths: torch.Tensor,
+ vad_indexes: torch.Tensor) -> Tuple[torch.Tensor, None]:
+ """Compute loss value from buffer sequences.
+
+ Args:
+ input (torch.Tensor): Input ids. (batch, len)
+ hidden (torch.Tensor): Target ids. (batch, len)
+
+ """
+ x = self.embed(input)
+ # mask = self._target_mask(input)
+ h, _, _ = self.encoder(x, text_lengths, vad_indexes)
+ y = self.decoder(h)
+ return y, None
+
+ def with_vad(self):
+ return True
+
+ def score(self, y: torch.Tensor, state: Any, x: torch.Tensor) -> Tuple[torch.Tensor, Any]:
+ """Score new token.
+
+ Args:
+ y (torch.Tensor): 1D torch.int64 prefix tokens.
+ state: Scorer state for prefix tokens
+ x (torch.Tensor): encoder feature that generates ys.
+
+ Returns:
+ tuple[torch.Tensor, Any]: Tuple of
+ torch.float32 scores for next token (vocab_size)
+ and next state for ys
+
+ """
+ y = y.unsqueeze(0)
+ h, _, cache = self.encoder.forward_one_step(self.embed(y), self._target_mask(y), cache=state)
+ h = self.decoder(h[:, -1])
+ logp = h.log_softmax(dim=-1).squeeze(0)
+ return logp, cache
+
+ def batch_score(self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor) -> Tuple[torch.Tensor, List[Any]]:
+ """Score new token batch.
+
+ Args:
+ ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+ states (List[Any]): Scorer states for prefix tokens.
+ xs (torch.Tensor):
+ The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+ Returns:
+ tuple[torch.Tensor, List[Any]]: Tuple of
+ batchfied scores for next token with shape of `(n_batch, vocab_size)`
+ and next state list for ys.
+
+ """
+ # merge states
+ n_batch = len(ys)
+ n_layers = len(self.encoder.encoders)
+ if states[0] is None:
+ batch_state = None
+ else:
+ # transpose state of [batch, layer] into [layer, batch]
+ batch_state = [torch.stack([states[b][i] for b in range(n_batch)]) for i in range(n_layers)]
+
+ # batch decoding
+ h, _, states = self.encoder.forward_one_step(self.embed(ys), self._target_mask(ys), cache=batch_state)
+ h = self.decoder(h[:, -1])
+ logp = h.log_softmax(dim=-1)
+
+ # transpose state of [layer, batch] into [batch, layer]
+ state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
+ return logp, state_list
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index 5be9089..d2a00b2 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -1350,10 +1350,12 @@
train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
seg_dict_file=args.seg_dict_file if hasattr(args,
"seg_dict_file") else None,
+ punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
mode="train")
valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
seg_dict_file=args.seg_dict_file if hasattr(args,
"seg_dict_file") else None,
+ punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
mode="eval")
elif args.dataset_type == "small":
train_iter_factory = cls.build_iter_factory(
diff --git a/funasr/tasks/punctuation.py b/funasr/tasks/punctuation.py
index 1837b2a..ea1e102 100644
--- a/funasr/tasks/punctuation.py
+++ b/funasr/tasks/punctuation.py
@@ -13,10 +13,11 @@
from typeguard import check_return_type
from funasr.datasets.collate_fn import CommonCollateFn
-from funasr.datasets.preprocessor import MutliTokenizerCommonPreprocessor
+from funasr.datasets.preprocessor import PuncTrainTokenizerCommonPreprocessor
from funasr.punctuation.abs_model import AbsPunctuation
from funasr.punctuation.espnet_model import ESPnetPunctuationModel
from funasr.punctuation.target_delay_transformer import TargetDelayTransformer
+from funasr.punctuation.vad_realtime_transformer import VadRealtimeTransformer
from funasr.tasks.abs_task import AbsTask
from funasr.text.phoneme_tokenizer import g2p_choices
from funasr.torch_utils.initialize import initialize
@@ -29,11 +30,9 @@
punc_choices = ClassChoices(
"punctuation",
- classes=dict(
- target_delay=TargetDelayTransformer,
- ),
+ classes=dict(target_delay=TargetDelayTransformer, vad_realtime=VadRealtimeTransformer),
type_check=AbsPunctuation,
- default="TargetDelayTransformer",
+ default="target_delay",
)
@@ -56,8 +55,6 @@
# NOTE(kamo): add_arguments(..., required=True) can't be used
# to provide --print_config mode. Instead of it, do as
required = parser.get_default("required")
- #import pdb;pdb.set_trace()
- #required += ["token_list"]
group.add_argument(
"--token_list",
@@ -154,7 +151,7 @@
bpemodels = [args.bpemodel, args.bpemodel]
text_names = ["text", "punc"]
if args.use_preprocessor:
- retval = MutliTokenizerCommonPreprocessor(
+ retval = PuncTrainTokenizerCommonPreprocessor(
train=train,
token_type=token_types,
token_list=token_lists,
@@ -182,7 +179,7 @@
def optional_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
- retval = ()
+ retval = ("vad",)
return retval
@classmethod
@@ -197,11 +194,13 @@
args.token_list = token_list.copy()
if isinstance(args.punc_list, str):
with open(args.punc_list, encoding="utf-8") as f2:
- punc_list = [line.rstrip() for line in f2]
+ pairs = [line.rstrip().split(":") for line in f2]
+ punc_list = [pair[0] for pair in pairs]
+ punc_weight_list = [float(pair[1]) for pair in pairs]
args.punc_list = punc_list.copy()
elif isinstance(args.punc_list, list):
- # This is in the inference code path.
punc_list = args.punc_list.copy()
+ punc_weight_list = [1] * len(punc_list)
if isinstance(args.token_list, (tuple, list)):
token_list = args.token_list.copy()
else:
@@ -217,7 +216,9 @@
# 2. Build ESPnetModel
# Assume the last-id is sos_and_eos
- model = ESPnetPunctuationModel(punc_model=punc, vocab_size=vocab_size, **args.model_conf)
+ if "punc_weight" in args.model_conf:
+ args.model_conf.pop("punc_weight")
+ model = ESPnetPunctuationModel(punc_model=punc, vocab_size=vocab_size, punc_weight=punc_weight_list, **args.model_conf)
# FIXME(kamo): Should be done in model?
# 3. Initialize
--
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