From bdc7a17c1f3efccb437517e74c780f64923ea647 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 26 十二月 2023 22:35:27 +0800
Subject: [PATCH] funasr1.0
---
funasr/models/ct_transformer/utils.py | 14 ++++
funasr/download/download_from_hub.py | 5 +
funasr/models/ct_transformer/model.py | 131 ++++++++++++++++++++++++++++++++++++++++---
examples/industrial_data_pretraining/punc/infer.sh | 9 +++
4 files changed, 148 insertions(+), 11 deletions(-)
diff --git a/examples/industrial_data_pretraining/punc/infer.sh b/examples/industrial_data_pretraining/punc/infer.sh
new file mode 100644
index 0000000..9c40547
--- /dev/null
+++ b/examples/industrial_data_pretraining/punc/infer.sh
@@ -0,0 +1,9 @@
+
+cmd="funasr/bin/inference.py"
+
+python $cmd \
++model="/Users/zhifu/Downloads/modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" \
++input="/Users/zhifu/FunASR/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/data/punc_example.txt" \
++output_dir="/Users/zhifu/Downloads/ckpt/funasr2/exp2_punc" \
++device="cpu" \
++debug="true"
diff --git a/funasr/download/download_from_hub.py b/funasr/download/download_from_hub.py
index 2e7578f..4f05b42 100644
--- a/funasr/download/download_from_hub.py
+++ b/funasr/download/download_from_hub.py
@@ -26,12 +26,15 @@
kwargs["init_param"] = init_param
if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.txt")
+ if os.path.exists(os.path.join(model_or_path, "tokens.json")):
+ kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.json")
if os.path.exists(os.path.join(model_or_path, "seg_dict")):
kwargs["tokenizer_conf"]["seg_dict"] = os.path.join(model_or_path, "seg_dict")
if os.path.exists(os.path.join(model_or_path, "bpe.model")):
kwargs["tokenizer_conf"]["bpemodel"] = os.path.join(model_or_path, "bpe.model")
kwargs["model"] = cfg["model"]
- kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
+ if os.path.exists(os.path.join(model_or_path, "am.mvn")):
+ kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
return OmegaConf.to_container(kwargs, resolve=True)
diff --git a/funasr/models/ct_transformer/model.py b/funasr/models/ct_transformer/model.py
index d8c7fc3..a1aff47 100644
--- a/funasr/models/ct_transformer/model.py
+++ b/funasr/models/ct_transformer/model.py
@@ -1,9 +1,16 @@
from typing import Any
from typing import List
from typing import Tuple
+from typing import Optional
+import numpy as np
+import torch.nn.functional as F
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.train_utils.device_funcs import to_device
import torch
import torch.nn as nn
+from funasr.models.ct_transformer.utils import split_to_mini_sentence
from funasr.register import tables
@@ -17,7 +24,7 @@
def __init__(
self,
encoder: str = None,
- encoder_conf: str = None,
+ encoder_conf: dict = None,
vocab_size: int = -1,
punc_list: list = None,
punc_weight: list = None,
@@ -191,7 +198,7 @@
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
@@ -202,11 +209,115 @@
return loss, stats, weight
def generate(self,
- text: torch.Tensor,
- text_lengths: torch.Tensor,
- vad_indexes: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]:
- if self.with_vad():
- assert vad_indexes is not None
- return self.punc_forward(text, text_lengths, vad_indexes)
- else:
- return self.punc_forward(text, text_lengths)
\ No newline at end of file
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+ vad_indexes = kwargs.get("vad_indexes", None)
+ text = data_in
+ text_lengths = data_lengths
+ split_size = kwargs.get("split_size", 20)
+
+ data = {"text": text}
+ result = self.preprocessor(data=data, uid="12938712838719")
+ split_text = self.preprocessor.pop_split_text_data(result)
+ mini_sentences = split_to_mini_sentence(split_text, split_size)
+ mini_sentences_id = split_to_mini_sentence(data["text"], split_size)
+ assert len(mini_sentences) == len(mini_sentences_id)
+ cache_sent = []
+ cache_sent_id = torch.from_numpy(np.array([], dtype='int32'))
+ new_mini_sentence = ""
+ new_mini_sentence_punc = []
+ cache_pop_trigger_limit = 200
+ for mini_sentence_i in range(len(mini_sentences)):
+ mini_sentence = mini_sentences[mini_sentence_i]
+ mini_sentence_id = mini_sentences_id[mini_sentence_i]
+ mini_sentence = cache_sent + mini_sentence
+ mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
+ data = {
+ "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
+ "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
+ }
+ data = to_device(data, self.device)
+ # y, _ = self.wrapped_model(**data)
+ y, _ = self.punc_forward(text, text_lengths)
+ _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
+ punctuations = indices
+ if indices.size()[0] != 1:
+ punctuations = torch.squeeze(indices)
+ assert punctuations.size()[0] == len(mini_sentence)
+
+ # Search for the last Period/QuestionMark as cache
+ if mini_sentence_i < len(mini_sentences) - 1:
+ sentenceEnd = -1
+ last_comma_index = -1
+ for i in range(len(punctuations) - 2, 1, -1):
+ if self.punc_list[punctuations[i]] == "銆�" or self.punc_list[punctuations[i]] == "锛�":
+ sentenceEnd = i
+ break
+ if last_comma_index < 0 and self.punc_list[punctuations[i]] == "锛�":
+ last_comma_index = i
+
+ if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
+ # The sentence it too long, cut off at a comma.
+ sentenceEnd = last_comma_index
+ punctuations[sentenceEnd] = self.period
+ cache_sent = mini_sentence[sentenceEnd + 1:]
+ cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
+ mini_sentence = mini_sentence[0:sentenceEnd + 1]
+ punctuations = punctuations[0:sentenceEnd + 1]
+
+ # if len(punctuations) == 0:
+ # continue
+
+ punctuations_np = punctuations.cpu().numpy()
+ new_mini_sentence_punc += [int(x) for x in punctuations_np]
+ words_with_punc = []
+ for i in range(len(mini_sentence)):
+ if (i==0 or self.punc_list[punctuations[i-1]] == "銆�" or self.punc_list[punctuations[i-1]] == "锛�") and len(mini_sentence[i][0].encode()) == 1:
+ mini_sentence[i] = mini_sentence[i].capitalize()
+ if i == 0:
+ if len(mini_sentence[i][0].encode()) == 1:
+ mini_sentence[i] = " " + mini_sentence[i]
+ if i > 0:
+ if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
+ mini_sentence[i] = " " + mini_sentence[i]
+ words_with_punc.append(mini_sentence[i])
+ if self.punc_list[punctuations[i]] != "_":
+ punc_res = self.punc_list[punctuations[i]]
+ if len(mini_sentence[i][0].encode()) == 1:
+ if punc_res == "锛�":
+ punc_res = ","
+ elif punc_res == "銆�":
+ punc_res = "."
+ elif punc_res == "锛�":
+ punc_res = "?"
+ words_with_punc.append(punc_res)
+ new_mini_sentence += "".join(words_with_punc)
+ # Add Period for the end of the sentence
+ new_mini_sentence_out = new_mini_sentence
+ new_mini_sentence_punc_out = new_mini_sentence_punc
+ if mini_sentence_i == len(mini_sentences) - 1:
+ if new_mini_sentence[-1] == "锛�" or new_mini_sentence[-1] == "銆�":
+ new_mini_sentence_out = new_mini_sentence[:-1] + "銆�"
+ new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+ elif new_mini_sentence[-1] == ",":
+ new_mini_sentence_out = new_mini_sentence[:-1] + "."
+ new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+ elif new_mini_sentence[-1] != "銆�" and new_mini_sentence[-1] != "锛�" and len(new_mini_sentence[-1].encode())==0:
+ new_mini_sentence_out = new_mini_sentence + "銆�"
+ new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+ elif new_mini_sentence[-1] != "." and new_mini_sentence[-1] != "?" and len(new_mini_sentence[-1].encode())==1:
+ new_mini_sentence_out = new_mini_sentence + "."
+ new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
+
+ return new_mini_sentence_out, new_mini_sentence_punc_out
+
+ # if self.with_vad():
+ # assert vad_indexes is not None
+ # return self.punc_forward(text, text_lengths, vad_indexes)
+ # else:
+ # return self.punc_forward(text, text_lengths)
\ No newline at end of file
diff --git a/funasr/models/ct_transformer/utils.py b/funasr/models/ct_transformer/utils.py
new file mode 100644
index 0000000..0291dbc
--- /dev/null
+++ b/funasr/models/ct_transformer/utils.py
@@ -0,0 +1,14 @@
+
+
+def split_to_mini_sentence(words: list, word_limit: int = 20):
+ assert word_limit > 1
+ if len(words) <= word_limit:
+ return [words]
+ sentences = []
+ length = len(words)
+ sentence_len = length // word_limit
+ for i in range(sentence_len):
+ sentences.append(words[i * word_limit:(i + 1) * word_limit])
+ if length % word_limit > 0:
+ sentences.append(words[sentence_len * word_limit:])
+ return sentences
--
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