funasr1.0

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"

funasr/download/download_from_hub.py

@@ -26,11 +26,14 @@ def download_fr_ms(**kwargs):
 	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"]
+	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)

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 @@ class CTTransformer(nn.Module):
     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 @@ class CTTransformer(nn.Module):
         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 @@ class CTTransformer(nn.Module):
         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)
+                 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]] == "。" or self.punc_list[punctuations[i]] == "？":
+                        sentenceEnd = i
+                        break
+                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
+                        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]] == "。" or self.punc_list[punctuations[i-1]] == "？") 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 == "，":
+                            punc_res = ","
+                        elif punc_res == "。":
+                            punc_res = "."
+                        elif punc_res == "？":
+                            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] == "，" or 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] == ",":
+                    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] != "。" and new_mini_sentence[-1] != "？" and len(new_mini_sentence[-1].encode())==0:
+                    new_mini_sentence_out = new_mini_sentence + "。"
+                    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)

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