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.py | 27 ++++++++++++---------------
1 files changed, 12 insertions(+), 15 deletions(-)
diff --git a/funasr/models/e2e_asr.py b/funasr/models/e2e_asr.py
index 950d699..79c5387 100644
--- a/funasr/models/e2e_asr.py
+++ b/funasr/models/e2e_asr.py
@@ -11,20 +11,23 @@
from typing import Union
import torch
-from typeguard import check_argument_types
+from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
from funasr.models.ctc import CTC
from funasr.models.decoder.abs_decoder import AbsDecoder
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
-from funasr.models.base_model import FunASRModel
+from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.modules.add_sos_eos import add_sos_eos
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.models.base_model import FunASRModel
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -35,19 +38,17 @@
yield
-class ESPnetASRModel(FunASRModel):
+class ASRModel(FunASRModel):
"""CTC-attention hybrid Encoder-Decoder model"""
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
- frontend: Optional[torch.nn.Module],
- specaug: Optional[torch.nn.Module],
- normalize: Optional[torch.nn.Module],
- preencoder: Optional[AbsPreEncoder],
- encoder: torch.nn.Module,
- postencoder: Optional[AbsPostEncoder],
+ frontend: Optional[AbsFrontend],
+ specaug: Optional[AbsSpecAug],
+ normalize: Optional[AbsNormalize],
+ encoder: AbsEncoder,
decoder: AbsDecoder,
ctc: CTC,
ctc_weight: float = 0.5,
@@ -60,8 +61,9 @@
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
assert 0.0 <= interctc_weight < 1.0, interctc_weight
@@ -129,7 +131,6 @@
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, )
@@ -245,7 +246,6 @@
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, )
@@ -327,9 +327,7 @@
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,)
@@ -366,7 +364,6 @@
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:
--
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