[paraformer] separate cif and paraformer

examples/aishell/cif/README.md → examples/aishell/paraformer/README.md

@@ -1,9 +1,7 @@
 # Performance Record
 
-## Standard CIF Conformer Result(TODO)
 
-
-## Paraformer like CIF Conformer Result
+## Paraformer, Conformer Result
 
 * Feature info: using fbank feature, dither, cmvn, online speed perturb
 * Training info: lr 0.002, batch size 16, 4 gpu, acc_grad 4, 240 epochs, dither 0.1
@@ -16,7 +14,7 @@
 | cif greedy search      | 4.41 | 4.92 |
 | cif beam search        | 4.35 | 4.86 |
 
-## Conformer CIF DecoderSANM Result(Deprecated)
+## Paraformer, Conformer DecoderSANM Result(Deprecated)
 
 * Feature info: using fbank feature, dither, cmvn, online speed perturb
 * Training info: lr 0.002, batch size 16, 4 gpu, acc_grad 4, 240 epochs, dither 0.1

examples/aishell/cif/conf/train_paraformer.yaml → examples/aishell/paraformer/conf/train_paraformer.yaml

@@ -28,9 +28,8 @@ decoder_conf:
     self_attention_dropout_rate: 0.0
     src_attention_dropout_rate: 0.0
     input_layer: 'none'
-    src_attention: true
 
-cif_predictor: predictor_v1
+paraformer: true
 cif_predictor_conf:
     idim: 256
     threshold: 1.0

examples/aishell/cif/run.sh → examples/aishell/paraformer/run.sh

@@ -38,11 +38,10 @@ num_utts_per_shard=1000
 
 train_set=train
 # Optional train_config
-# conf/train_cif_conformer.yaml: standard non streaming CIF,
 # conf/train_paraformer.yaml: paraformer like CIF, apply encoder-decoder attention
-train_config=conf/train_cif_conformer.yaml
+train_config=conf/train_paraformer.yaml
 cmvn=true
-dir=exp/cif_conformer
+dir=exp/paraformer
 checkpoint=
 
 # use average_checkpoint will get better result
@@ -52,8 +51,8 @@ average_num=20
 #decode_modes="ctc_greedy_search ctc_prefix_beam_search cif_greedy_search cif_beam_search"
 # Since the Predictor Loss also plays an important role in the training of the CIF models,
 # the performance of the predictor cannot be used by using the CTC-related decoding methods,
-# so we strongly recommend that you use the 'cif_greedy_search' and 'cif_beam_search'.
-decode_modes="cif_greedy_search cif_beam_search"
+# so we strongly recommend that you use the 'paraformer_greedy_search' and 'paraformer_beam_search'.
+decode_modes="paraformer_greedy_search paraformer_beam_search"
 
 . tools/parse_options.sh || exit 1;
 

wenet/bin/recognize.py

@@ -25,13 +25,12 @@
 from torch.utils.data import DataLoader
 
 from wenet.dataset.dataset import Dataset
+from wenet.paraformer.search.beam_search import build_beam_search
 from wenet.utils.checkpoint import load_checkpoint
 from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols
 from wenet.utils.config import override_config
 from wenet.utils.init_model import init_model
 
-from wenet.cif.search.beam_search import build_beam_search
-
 
 def get_args():
     parser = argparse.ArgumentParser(description='recognize with your model')
@@ -69,8 +68,8 @@ def get_args():
                             'rnnt_greedy_search', 'rnnt_beam_search',
                             'rnnt_beam_attn_rescoring',
                             'ctc_beam_td_attn_rescoring', 'hlg_onebest',
-                            'hlg_rescore', 'cif_greedy_search',
-                            'cif_beam_search',
+                            'hlg_rescore', 'paraformer_greedy_search',
+                            'paraformer_beam_search',
                         ],
                         default='attention',
                         help='decoding mode')
@@ -166,7 +165,7 @@ def main():
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
 
     if args.mode in ['ctc_prefix_beam_search', 'attention_rescoring',
-                     'cif_beam_search', ] and args.batch_size > 1:
+                     'paraformer_beam_search', ] and args.batch_size > 1:
         logging.fatal(
             'decoding mode {} must be running with batch_size == 1'.format(
                 args.mode))
@@ -225,10 +224,10 @@ def main():
     model.eval()
 
     # Build BeamSearchCIF object
-    if args.mode == 'cif_beam_search':
-        cif_beam_search = build_beam_search(model, args, device)
+    if args.mode == 'paraformer_beam_search':
+        paraformer_beam_search = build_beam_search(model, args, device)
     else:
-        cif_beam_search = None
+        paraformer_beam_search = None
 
     with torch.no_grad(), open(args.result_file, 'w') as fout:
         for batch_idx, batch in enumerate(test_data_loader):
@@ -355,16 +354,16 @@ def main():
                     hlg=args.hlg,
                     word=args.word,
                     symbol_table=symbol_table)
-            elif args.mode == 'cif_beam_search':
-                hyps = model.cif_beam_search(
+            elif args.mode == 'paraformer_beam_search':
+                hyps = model.paraformer_beam_search(
                     feats,
                     feats_lengths,
-                    beam_search=cif_beam_search,
+                    beam_search=paraformer_beam_search,
                     decoding_chunk_size=args.decoding_chunk_size,
                     num_decoding_left_chunks=args.num_decoding_left_chunks,
                     simulate_streaming=args.simulate_streaming)
-            elif args.mode == 'cif_greedy_search':
-                hyps = model.cif_greedy_search(
+            elif args.mode == 'paraformer_greedy_search':
+                hyps = model.paraformer_greedy_search(
                     feats,
                     feats_lengths,
                     decoding_chunk_size=args.decoding_chunk_size,

wenet/cif/cif_model.py → wenet/paraformer/paraformer.py

@@ -20,7 +20,7 @@
 import torch
 
 from wenet.cif.predictor import MAELoss
-from wenet.cif.search.beam_search import Hypothesis
+from wenet.paraformer.search.beam_search import Hypothesis
 from wenet.transformer.asr_model import ASRModel
 from wenet.transformer.ctc import CTC
 from wenet.transformer.decoder import TransformerDecoder
@@ -29,9 +29,10 @@
 from wenet.utils.mask import make_pad_mask
 
 
-class CIFModel(ASRModel):
-    """ Continuous Integrate-and-Fire model
-        see https://arxiv.org/pdf/1905.11235.pdf
+class Paraformer(ASRModel):
+    """ Paraformer: Fast and Accurate Parallel Transformer for
+        Non-autoregressive End-to-End Speech Recognition
+        see https://arxiv.org/pdf/2206.08317.pdf
     """
 
     def __init__(
@@ -169,7 +170,7 @@ def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens,
     def recognize(self):
         raise NotImplementedError
 
-    def cif_greedy_search(
+    def paraformer_greedy_search(
         self,
         speech: torch.Tensor,
         speech_lengths: torch.Tensor,
@@ -249,7 +250,7 @@ def cif_greedy_search(
                 hyps.append(token_int)
         return hyps
 
-    def cif_beam_search(
+    def paraformer_beam_search(
         self,
         speech: torch.Tensor,
         speech_lengths: torch.Tensor,

wenet/cif/search/beam_search.py → wenet/paraformer/search/beam_search.py

@@ -22,9 +22,9 @@
 
 import torch
 
-from wenet.cif.utils import end_detect
-from wenet.cif.search.ctc import CTCPrefixScorer
-from wenet.cif.search.scorer_interface import ScorerInterface, \
+from wenet.paraformer.utils import end_detect
+from wenet.paraformer.search.ctc import CTCPrefixScorer
+from wenet.paraformer.search.scorer_interface import ScorerInterface, \
     PartialScorerInterface
 
 

wenet/cif/search/ctc.py → wenet/paraformer/search/ctc.py

@@ -17,8 +17,9 @@
 import numpy as np
 import torch
 
-from wenet.cif.search.ctc_prefix_score import CTCPrefixScore, CTCPrefixScoreTH
-from wenet.cif.search.scorer_interface import BatchPartialScorerInterface
+from wenet.paraformer.search.ctc_prefix_score import CTCPrefixScore
+from wenet.paraformer.search.ctc_prefix_score import CTCPrefixScoreTH
+from wenet.paraformer.search.scorer_interface import BatchPartialScorerInterface
 
 
 class CTCPrefixScorer(BatchPartialScorerInterface):

wenet/utils/init_model.py

@@ -24,7 +24,7 @@
 from wenet.transformer.encoder import ConformerEncoder, TransformerEncoder
 from wenet.squeezeformer.encoder import SqueezeformerEncoder
 from wenet.efficient_conformer.encoder import EfficientConformerEncoder
-from wenet.cif.cif_model import CIFModel
+from wenet.paraformer.paraformer import Paraformer
 from wenet.cif.predictor import Predictor
 from wenet.utils.cmvn import load_cmvn
 
@@ -104,14 +104,14 @@ def init_model(configs):
                            joint=joint,
                            ctc=ctc,
                            **configs['model_conf'])
-    elif 'cif_predictor' in configs:
+    elif 'paraformer' in configs:
         predictor = Predictor(**configs['cif_predictor_conf'])
-        model = CIFModel(vocab_size=vocab_size,
-                         encoder=encoder,
-                         decoder=decoder,
-                         ctc=ctc,
-                         predictor=predictor,
-                         **configs['model_conf'])
+        model = Paraformer(vocab_size=vocab_size,
+                           encoder=encoder,
+                           decoder=decoder,
+                           ctc=ctc,
+                           predictor=predictor,
+                           **configs['model_conf'])
     else:
         model = ASRModel(vocab_size=vocab_size,
                          encoder=encoder,