Funasr1.0

.gitignore

@@ -23,3 +23,4 @@ samples
 .ipynb_checkpoints
 outputs*
 emotion2vec*
+GPT-SoVITS*

funasr/auto/auto_model.py

@@ -6,6 +6,7 @@
 import string
 import logging
 import os.path
+import numpy as np
 from tqdm import tqdm
 from omegaconf import DictConfig, OmegaConf, ListConfig
 
@@ -96,25 +97,25 @@ def __init__(self, **kwargs):
         vad_kwargs = kwargs.get("vad_model_revision", None)
         if vad_model is not None:
             logging.info("Building VAD model.")
-            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs, "device": kwargs["device"]}
             vad_model, vad_kwargs = self.build_model(**vad_kwargs)
 
         # if punc_model is not None, build punc model else None
         punc_model = kwargs.get("punc_model", None)
         punc_kwargs = kwargs.get("punc_model_revision", None)
         if punc_model is not None:
             logging.info("Building punc model.")
-            punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+            punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs, "device": kwargs["device"]}
             punc_model, punc_kwargs = self.build_model(**punc_kwargs)
 
         # if spk_model is not None, build spk model else None
         spk_model = kwargs.get("spk_model", None)
         spk_kwargs = kwargs.get("spk_model_revision", None)
         if spk_model is not None:
             logging.info("Building SPK model.")
-            spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
+            spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs, "device": kwargs["device"]}
             spk_model, spk_kwargs = self.build_model(**spk_kwargs)
-            self.cb_model = ClusterBackend()
+            self.cb_model = ClusterBackend().to(kwargs["device"])
             spk_mode = kwargs.get("spk_mode", 'punc_segment')
             if spk_mode not in ["default", "vad_segment", "punc_segment"]:
                 logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
@@ -334,7 +335,7 @@ def inference_with_vad(self, input, input_len=None, **cfg):
                     for _b in range(len(speech_j)):
                         vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
                                         sorted_data[beg_idx:end_idx][_b][0][1]/1000.0,
-                                        speech_j[_b]]]
+                                        np.array(speech_j[_b])]]
                         segments = sv_chunk(vad_segments)
                         all_segments.extend(segments)
                         speech_b = [i[2] for i in segments]
@@ -376,7 +377,7 @@ def inference_with_vad(self, input, input_len=None, **cfg):
                             result[k] = restored_data[j][k]
                         else:
                             result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
-                    elif k == 'text':
+                    elif k == 'raw_text':
                         if k not in result:
                             result[k] = restored_data[j][k]
                         else:
@@ -397,20 +398,20 @@ def inference_with_vad(self, input, input_len=None, **cfg):
             if self.spk_model is not None:
                 all_segments = sorted(all_segments, key=lambda x: x[0])
                 spk_embedding = result['spk_embedding']
-                labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
+                labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
                 del result['spk_embedding']
                 sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                 if self.spk_mode == 'vad_segment':
                     sentence_list = []
                     for res, vadsegment in zip(restored_data, vadsegments):
                         sentence_list.append({"start": vadsegment[0],\
                                                 "end": vadsegment[1],
-                                                "sentence": res['text'],
+                                                "sentence": res['raw_text'],
                                                 "timestamp": res['timestamp']})
                 else: # punc_segment
                     sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                         result['timestamp'], \
-                                                        result['text'])
+                                                        result['raw_text'])
                 distribute_spk(sentence_list, sv_output)
                 result['sentence_info'] = sentence_list
 

funasr/models/paraformer/model.py

@@ -528,9 +528,9 @@ def inference(self,
                 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)
+                    text_postprocessed = tokenizer.tokens2text(token)
+                    if not hasattr(tokenizer, "bpemodel"):
+                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
 
                     result_i = {"key": key[i], "text": text_postprocessed}
 

funasr/models/seaco_paraformer/model.py

@@ -415,12 +415,12 @@ def inference(self,
                         token, timestamp)
 
                     result_i = {"key": key[i], "text": text_postprocessed,
-                                "timestamp": time_stamp_postprocessed,
+                                "timestamp": time_stamp_postprocessed, "raw_text": copy.copy(text_postprocessed)
                                 }
 
                     if ibest_writer is not None:
                         ibest_writer["token"][key[i]] = " ".join(token)
-                        # ibest_writer["text"][key[i]] = text
+                        # ibest_writer["raw_text"][key[i]] = text
                         ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
                         ibest_writer["text"][key[i]] = text_postprocessed
                 else:

funasr/tokenizer/sentencepiece_tokenizer.py

@@ -5,12 +5,16 @@
 
 import sentencepiece as spm
 
-from funasr.tokenizer.abs_tokenizer import AbsTokenizer
-
-
-class SentencepiecesTokenizer(AbsTokenizer):
-    def __init__(self, model: Union[Path, str]):
-        self.model = str(model)
+from funasr.tokenizer.abs_tokenizer import BaseTokenizer
+from funasr.register import tables
+
+@tables.register("tokenizer_classes", "SentencepiecesTokenizer")
+class SentencepiecesTokenizer(BaseTokenizer):
+    def __init__(self, bpemodel: Union[Path, str],
+                 **kwargs
+                 ):
+        super().__init__(**kwargs)
+        self.bpemodel = str(bpemodel)
         # NOTE(kamo):
         # Don't build SentencePieceProcessor in __init__()
         # because it's not picklable and it may cause following error,
@@ -19,13 +23,13 @@ def __init__(self, model: Union[Path, str]):
         self.sp = None
 
     def __repr__(self):
-        return f'{self.__class__.__name__}(model="{self.model}")'
+        return f'{self.__class__.__name__}(model="{self.bpemodel}")'
 
     def _build_sentence_piece_processor(self):
         # Build SentencePieceProcessor lazily.
         if self.sp is None:
             self.sp = spm.SentencePieceProcessor()
-            self.sp.load(self.model)
+            self.sp.load(self.bpemodel)
 
     def text2tokens(self, line: str) -> List[str]:
         self._build_sentence_piece_processor()
@@ -34,3 +38,11 @@ def text2tokens(self, line: str) -> List[str]:
     def tokens2text(self, tokens: Iterable[str]) -> str:
         self._build_sentence_piece_processor()
         return self.sp.DecodePieces(list(tokens))
+
+    def encode(self, line: str) -> List[int]:
+        self._build_sentence_piece_processor()
+        return self.sp.EncodeAsIds(line)
+
+    def decode(self, line: List[int]):
+        self._build_sentence_piece_processor()
+        return self.sp.DecodeIds(line)