From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/models/e2e_asr_transducer.py |   38 +++++++-------------------------------
 1 files changed, 7 insertions(+), 31 deletions(-)

diff --git a/funasr/models/e2e_asr_transducer.py b/funasr/models/e2e_asr_transducer.py
index 3f9f31c..bffd54d 100644
--- a/funasr/models/e2e_asr_transducer.py
+++ b/funasr/models/e2e_asr_transducer.py
@@ -6,8 +6,9 @@
 
 import torch
 from packaging.version import parse as V
-from typeguard import check_argument_types
-
+from funasr.losses.label_smoothing_loss import (
+    LabelSmoothingLoss,  # noqa: H301
+)
 from funasr.models.frontend.abs_frontend import AbsFrontend
 from funasr.models.specaug.abs_specaug import AbsSpecAug
 from funasr.models.decoder.rnnt_decoder import RNNTDecoder
@@ -15,6 +16,8 @@
 from funasr.models.encoder.abs_encoder import AbsEncoder
 from funasr.models.joint_net.joint_network import JointNetwork
 from funasr.modules.nets_utils import get_transducer_task_io
+from funasr.modules.nets_utils import th_accuracy
+from funasr.modules.add_sos_eos import add_sos_eos
 from funasr.layers.abs_normalize import AbsNormalize
 from funasr.torch_utils.device_funcs import force_gatherable
 from funasr.models.base_model import FunASRModel
@@ -81,8 +84,6 @@
     ) -> None:
         """Construct an ESPnetASRTransducerModel object."""
         super().__init__()
-
-        assert check_argument_types()
 
         # The following labels ID are reserved: 0 (blank) and vocab_size - 1 (sos/eos)
         self.blank_id = 0
@@ -352,11 +353,6 @@
         """
         if self.criterion_transducer is None:
             try:
-                # from warprnnt_pytorch import RNNTLoss
-	        # self.criterion_transducer = RNNTLoss(
-                    # reduction="mean",
-                    # fastemit_lambda=self.fastemit_lambda,
-                # )
                 from warp_rnnt import rnnt_loss as RNNTLoss
                 self.criterion_transducer = RNNTLoss
 
@@ -367,12 +363,6 @@
                 )
                 exit(1)
 
-        # loss_transducer = self.criterion_transducer(
-        #     joint_out,
-        #     target,
-        #     t_len,
-        #     u_len,
-        # )
         log_probs = torch.log_softmax(joint_out, dim=-1)
 
         loss_transducer = self.criterion_transducer(
@@ -542,8 +532,6 @@
         """Construct an ESPnetASRTransducerModel object."""
         super().__init__()
 
-        assert check_argument_types()
-
         # The following labels ID are reserved: 0 (blank) and vocab_size - 1 (sos/eos)
         self.blank_id = 0
 
@@ -638,7 +626,6 @@
 
         batch_size = speech.shape[0]
         text = text[:, : text_lengths.max()]
-        #print(speech.shape)
         # 1. Encoder
         encoder_out, encoder_out_chunk, encoder_out_lens = self.encode(speech, speech_lengths)
 
@@ -709,8 +696,8 @@
             loss_lm = self._calc_lm_loss(decoder_out, target)
 
         loss_trans = loss_trans_utt + loss_trans_chunk
-        loss_ctc = loss_ctc + loss_ctc_chunk 
-        loss_ctc = loss_att + loss_att_chunk
+        loss_ctc = loss_ctc + loss_ctc_chunk
+        loss_att = loss_att + loss_att_chunk
 
         loss = (
             self.transducer_weight * loss_trans
@@ -855,11 +842,6 @@
         """
         if self.criterion_transducer is None:
             try:
-                # from warprnnt_pytorch import RNNTLoss
-            # self.criterion_transducer = RNNTLoss(
-                    # reduction="mean",
-                    # fastemit_lambda=self.fastemit_lambda,
-                # )
                 from warp_rnnt import rnnt_loss as RNNTLoss
                 self.criterion_transducer = RNNTLoss
 
@@ -870,12 +852,6 @@
                 )
                 exit(1)
 
-        # loss_transducer = self.criterion_transducer(
-        #     joint_out,
-        #     target,
-        #     t_len,
-        #     u_len,
-        # )
         log_probs = torch.log_softmax(joint_out, dim=-1)
 
         loss_transducer = self.criterion_transducer(

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