From f22cb3c12f6016c644ccdb1c5d5a348df2e8a6b9 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 14 六月 2023 20:52:59 +0800
Subject: [PATCH] update repo

---
 funasr/build_utils/build_asr_model.py |   24 +++++++++++++++---------
 1 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/funasr/build_utils/build_asr_model.py b/funasr/build_utils/build_asr_model.py
index 46c11b0..621c4d9 100644
--- a/funasr/build_utils/build_asr_model.py
+++ b/funasr/build_utils/build_asr_model.py
@@ -6,6 +6,7 @@
 from funasr.models.decoder.abs_decoder import AbsDecoder
 from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
 from funasr.models.decoder.rnn_decoder import RNNDecoder
+from funasr.models.decoder.rnnt_decoder import RNNTDecoder
 from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder, FsmnDecoderSCAMAOpt
 from funasr.models.decoder.transformer_decoder import (
     DynamicConvolution2DTransformerDecoder,  # noqa: H301
@@ -19,14 +20,13 @@
 )
 from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
 from funasr.models.decoder.transformer_decoder import TransformerDecoder
-from funasr.models.decoder.rnnt_decoder import RNNTDecoder
-from funasr.models.joint_net.joint_network import JointNetwork
 from funasr.models.e2e_asr import ASRModel
 from funasr.models.e2e_asr_mfcca import MFCCA
-from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer
+from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, \
+    ContextualParaformer
+from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
 from funasr.models.e2e_tp import TimestampPredictor
 from funasr.models.e2e_uni_asr import UniASR
-from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
 from funasr.models.encoder.conformer_encoder import ConformerEncoder, ConformerChunkEncoder
 from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
 from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
@@ -39,6 +39,7 @@
 from funasr.models.frontend.s3prl import S3prlFrontend
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.models.frontend.windowing import SlidingWindow
+from funasr.models.joint_net.joint_network import JointNetwork
 from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
 from funasr.models.specaug.specaug import SpecAug
 from funasr.models.specaug.specaug import SpecAugLFR
@@ -227,17 +228,21 @@
 
 def build_asr_model(args):
     # token_list
-    if args.token_list is not None:
-        with open(args.token_list) as f:
+    if isinstance(args.token_list, str):
+        with open(args.token_list, encoding="utf-8") as f:
             token_list = [line.rstrip() for line in f]
         args.token_list = list(token_list)
+        vocab_size = len(token_list)
+        logging.info(f"Vocabulary size: {vocab_size}")
+    elif isinstance(args.token_list, (tuple, list)):
+        token_list = list(args.token_list)
         vocab_size = len(token_list)
         logging.info(f"Vocabulary size: {vocab_size}")
     else:
         vocab_size = None
 
     # frontend
-    if args.input_size is None:
+    if hasattr(args, "input_size") and args.input_size is None:
         frontend_class = frontend_choices.get_class(args.frontend)
         if args.frontend == 'wav_frontend':
             frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
@@ -248,7 +253,7 @@
         args.frontend = None
         args.frontend_conf = {}
         frontend = None
-        input_size = args.input_size
+        input_size = args.input_size if hasattr(args, "input_size") else None
 
     # data augmentation for spectrogram
     if args.specaug is not None:
@@ -294,7 +299,8 @@
             token_list=token_list,
             **args.model_conf,
         )
-    elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]:
+    elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer",
+                        "contextual_paraformer"]:
         # predictor
         predictor_class = predictor_choices.get_class(args.predictor)
         predictor = predictor_class(**args.predictor_conf)

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