From e8528b8f6208cee52ed9c02ecfa9185f84706502 Mon Sep 17 00:00:00 2001
From: yhliang <68215459+yhliang-aslp@users.noreply.github.com>
Date: 星期五, 16 六月 2023 20:16:47 +0800
Subject: [PATCH] Dev lyh (#645)

---
 funasr/build_utils/build_asr_model.py |   60 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 files changed, 59 insertions(+), 1 deletions(-)

diff --git a/funasr/build_utils/build_asr_model.py b/funasr/build_utils/build_asr_model.py
index 46c11b0..7aa8111 100644
--- a/funasr/build_utils/build_asr_model.py
+++ b/funasr/build_utils/build_asr_model.py
@@ -21,8 +21,10 @@
 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.decoder.transformer_decoder import SAAsrTransformerDecoder
 from funasr.models.e2e_asr import ASRModel
 from funasr.models.e2e_asr_mfcca import MFCCA
+from funasr.models.e2e_sa_asr import SAASRModel
 from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer
 from funasr.models.e2e_tp import TimestampPredictor
 from funasr.models.e2e_uni_asr import UniASR
@@ -30,6 +32,7 @@
 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
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar
 from funasr.models.encoder.rnn_encoder import RNNEncoder
 from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
 from funasr.models.encoder.transformer_encoder import TransformerEncoder
@@ -90,6 +93,8 @@
         timestamp_prediction=TimestampPredictor,
         rnnt=TransducerModel,
         rnnt_unified=UnifiedTransducerModel,
+        sa_asr=SAASRModel,
+
     ),
     default="asr",
 )
@@ -106,6 +111,27 @@
         chunk_conformer=ConformerChunkEncoder,
     ),
     default="rnn",
+)
+asr_encoder_choices = ClassChoices(
+    "asr_encoder",
+    classes=dict(
+        conformer=ConformerEncoder,
+        transformer=TransformerEncoder,
+        rnn=RNNEncoder,
+        sanm=SANMEncoder,
+        sanm_chunk_opt=SANMEncoderChunkOpt,
+        data2vec_encoder=Data2VecEncoder,
+        mfcca_enc=MFCCAEncoder,
+    ),
+    default="rnn",
+)
+
+spk_encoder_choices = ClassChoices(
+    "spk_encoder",
+    classes=dict(
+        resnet34_diar=ResNet34Diar,
+    ),
+    default="resnet34_diar",
 )
 encoder_choices2 = ClassChoices(
     "encoder2",
@@ -131,6 +157,7 @@
         paraformer_decoder_sanm=ParaformerSANMDecoder,
         paraformer_decoder_san=ParaformerDecoderSAN,
         contextual_paraformer_decoder=ContextualParaformerDecoder,
+        sa_decoder=SAAsrTransformerDecoder,
     ),
     default="rnn",
 )
@@ -222,6 +249,10 @@
     rnnt_decoder_choices,
     # --joint_network and --joint_network_conf
     joint_network_choices,
+    # --asr_encoder and --asr_encoder_conf
+    asr_encoder_choices,
+    # --spk_encoder and --spk_encoder_conf
+    spk_encoder_choices,
 ]
 
 
@@ -239,7 +270,7 @@
     # frontend
     if args.input_size is None:
         frontend_class = frontend_choices.get_class(args.frontend)
-        if args.frontend == 'wav_frontend':
+        if args.frontend == 'wav_frontend' or args.frontend == 'multichannelfrontend':
             frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
         else:
             frontend = frontend_class(**args.frontend_conf)
@@ -413,6 +444,33 @@
             joint_network=joint_network,
             **args.model_conf,
         )
+    elif args.model == "sa_asr":
+        asr_encoder_class = asr_encoder_choices.get_class(args.asr_encoder)
+        asr_encoder = asr_encoder_class(input_size=input_size, **args.asr_encoder_conf)
+        spk_encoder_class = spk_encoder_choices.get_class(args.spk_encoder)
+        spk_encoder = spk_encoder_class(input_size=input_size, **args.spk_encoder_conf)
+        decoder = decoder_class(
+            vocab_size=vocab_size,
+            encoder_output_size=asr_encoder.output_size(),
+            **args.decoder_conf,
+        )
+        ctc = CTC(
+            odim=vocab_size, encoder_output_size=asr_encoder.output_size(), **args.ctc_conf
+        )
+
+        model_class = model_choices.get_class(args.model)
+        model = model_class(
+            vocab_size=vocab_size,
+            frontend=frontend,
+            specaug=specaug,
+            normalize=normalize,
+            asr_encoder=asr_encoder,
+            spk_encoder=spk_encoder,
+            decoder=decoder,
+            ctc=ctc,
+            token_list=token_list,
+            **args.model_conf,
+        )
 
     else:
         raise NotImplementedError("Not supported model: {}".format(args.model))

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