From 6f7e27eb7c2d0a7649ec8f14d167c8da8e29f906 Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 16 五月 2023 15:07:20 +0800
Subject: [PATCH] Merge pull request #518 from alibaba-damo-academy/dev_wjm2
---
funasr/models/e2e_uni_asr.py | 11 ++---------
1 files changed, 2 insertions(+), 9 deletions(-)
diff --git a/funasr/models/e2e_uni_asr.py b/funasr/models/e2e_uni_asr.py
index ca76244..d08ea37 100644
--- a/funasr/models/e2e_uni_asr.py
+++ b/funasr/models/e2e_uni_asr.py
@@ -25,7 +25,7 @@
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.layers.abs_normalize import AbsNormalize
from funasr.torch_utils.device_funcs import force_gatherable
-from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.base_model import FunASRModel
from funasr.modules.streaming_utils.chunk_utilis import sequence_mask
from funasr.models.predictor.cif import mae_loss
@@ -38,7 +38,7 @@
yield
-class UniASR(AbsESPnetModel):
+class UniASR(FunASRModel):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
"""
@@ -179,7 +179,6 @@
decoding_ind: int = None,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
-
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -469,7 +468,6 @@
self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
-
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -533,7 +531,6 @@
ind: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
-
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -627,9 +624,7 @@
ys_pad_lens: torch.Tensor,
) -> torch.Tensor:
"""Compute negative log likelihood(nll) from transformer-decoder
-
Normally, this function is called in batchify_nll.
-
Args:
encoder_out: (Batch, Length, Dim)
encoder_out_lens: (Batch,)
@@ -666,7 +661,6 @@
batch_size: int = 100,
):
"""Compute negative log likelihood(nll) from transformer-decoder
-
To avoid OOM, this fuction seperate the input into batches.
Then call nll for each batch and combine and return results.
Args:
@@ -1072,4 +1066,3 @@
ys_hat = self.ctc2.argmax(encoder_out).data
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
return loss_ctc, cer_ctc
-
--
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