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_asr.py | 13 ++++---------
1 files changed, 4 insertions(+), 9 deletions(-)
diff --git a/funasr/models/e2e_asr.py b/funasr/models/e2e_asr.py
index f64ea3d..e6e6a52 100644
--- a/funasr/models/e2e_asr.py
+++ b/funasr/models/e2e_asr.py
@@ -28,7 +28,7 @@
from funasr.modules.e2e_asr_common import ErrorCalculator
from funasr.modules.nets_utils import th_accuracy
from funasr.torch_utils.device_funcs import force_gatherable
-from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.base_model import FunASRModel
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -39,7 +39,7 @@
yield
-class ESPnetASRModel(AbsESPnetModel):
+class ASRModel(FunASRModel):
"""CTC-attention hybrid Encoder-Decoder model"""
def __init__(
@@ -49,9 +49,7 @@
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
- preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
- postencoder: Optional[AbsPostEncoder],
decoder: AbsDecoder,
ctc: CTC,
ctc_weight: float = 0.5,
@@ -64,6 +62,8 @@
sym_space: str = "<space>",
sym_blank: str = "<blank>",
extract_feats_in_collect_stats: bool = True,
+ preencoder: Optional[AbsPreEncoder] = None,
+ postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
@@ -133,7 +133,6 @@
text_lengths: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
-
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -249,7 +248,6 @@
self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
-
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -331,9 +329,7 @@
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,)
@@ -370,7 +366,6 @@
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:
--
Gitblit v1.9.1