From 675b4605e8d1d9a406f5e6fc3bc989ddc932b04b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 15 三月 2024 21:14:08 +0800
Subject: [PATCH] Dev gzf llm (#1506)
---
funasr/models/ct_transformer/model.py | 78 ++++++++-------------------------------
1 files changed, 16 insertions(+), 62 deletions(-)
diff --git a/funasr/models/ct_transformer/model.py b/funasr/models/ct_transformer/model.py
index 9f680fd..57a23cc 100644
--- a/funasr/models/ct_transformer/model.py
+++ b/funasr/models/ct_transformer/model.py
@@ -17,7 +17,10 @@
from funasr.utils.load_utils import load_audio_text_image_video
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
-
+try:
+ import jieba
+except:
+ pass
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
else:
@@ -69,6 +72,10 @@
self.sos = sos
self.eos = eos
self.sentence_end_id = sentence_end_id
+ self.jieba_usr_dict = None
+ if kwargs.get("jieba_usr_dict", None) is not None:
+ jieba.load_userdict(kwargs["jieba_usr_dict"])
+ self.jieba_usr_dict = jieba
@@ -237,14 +244,8 @@
# text = data_in[0]
# text_lengths = data_lengths[0] if data_lengths is not None else None
split_size = kwargs.get("split_size", 20)
-
- jieba_usr_dict = kwargs.get("jieba_usr_dict", None)
- if jieba_usr_dict and isinstance(jieba_usr_dict, str):
- import jieba
- jieba.load_userdict(jieba_usr_dict)
- jieba_usr_dict = jieba
- kwargs["jieba_usr_dict"] = "jieba_usr_dict"
- tokens = split_words(text, jieba_usr_dict=jieba_usr_dict)
+
+ tokens = split_words(text, jieba_usr_dict=self.jieba_usr_dict)
tokens_int = tokenizer.encode(tokens)
mini_sentences = split_to_mini_sentence(tokens, split_size)
@@ -347,7 +348,7 @@
else:
punc_array = torch.cat([punc_array, punctuations], dim=0)
# post processing when using word level punc model
- if jieba_usr_dict:
+ if self.jieba_usr_dict is not None:
len_tokens = len(tokens)
new_punc_array = copy.copy(punc_array).tolist()
# for i, (token, punc_id) in enumerate(zip(tokens[::-1], punc_array.tolist()[::-1])):
@@ -364,57 +365,10 @@
results.append(result_i)
return results, meta_data
- def export(
- self,
- **kwargs,
- ):
+ def export(self, **kwargs):
+
+ from .export_meta import export_rebuild_model
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
- is_onnx = kwargs.get("type", "onnx") == "onnx"
- encoder_class = tables.encoder_classes.get(kwargs["encoder"]+"Export")
- self.encoder = encoder_class(self.encoder, onnx=is_onnx)
- self.forward = self.export_forward
-
- return self
-
- def export_forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor):
- """Compute loss value from buffer sequences.
-
- Args:
- input (torch.Tensor): Input ids. (batch, len)
- hidden (torch.Tensor): Target ids. (batch, len)
-
- """
- x = self.embed(inputs)
- h, _ = self.encoder(x, text_lengths)
- y = self.decoder(h)
- return y
-
- def export_dummy_inputs(self):
- length = 120
- text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length)).type(torch.int32)
- text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
- return (text_indexes, text_lengths)
-
- def export_input_names(self):
- return ['inputs', 'text_lengths']
-
- def export_output_names(self):
- return ['logits']
-
- def export_dynamic_axes(self):
- return {
- 'inputs': {
- 0: 'batch_size',
- 1: 'feats_length'
- },
- 'text_lengths': {
- 0: 'batch_size',
- },
- 'logits': {
- 0: 'batch_size',
- 1: 'logits_length'
- },
- }
- def export_name(self):
- return "model.onnx"
\ No newline at end of file
--
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