From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/models/e2e_tp.py | 26 +++++++++++++-------------
1 files changed, 13 insertions(+), 13 deletions(-)
diff --git a/funasr/models/e2e_tp.py b/funasr/models/e2e_tp.py
index c5dc63c..567dc70 100644
--- a/funasr/models/e2e_tp.py
+++ b/funasr/models/e2e_tp.py
@@ -2,19 +2,22 @@
from contextlib import contextmanager
from distutils.version import LooseVersion
from typing import Dict
+from typing import List
from typing import Optional
from typing import Tuple
+from typing import Union
import torch
-from typeguard import check_argument_types
+import numpy as np
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.predictor.cif import mae_loss
-from funasr.models.base_model import FunASRModel
from funasr.modules.add_sos_eos import add_sos_eos
from funasr.modules.nets_utils import make_pad_mask, pad_list
from funasr.torch_utils.device_funcs import force_gatherable
+from funasr.models.base_model import FunASRModel
from funasr.models.predictor.cif import CifPredictorV3
-
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -27,18 +30,17 @@
class TimestampPredictor(FunASRModel):
"""
- Author: Speech Lab, Alibaba Group, China
+ Author: Speech Lab of DAMO Academy, Alibaba Group
"""
def __init__(
self,
- frontend: Optional[torch.nn.Module],
- encoder: torch.nn.Module,
+ frontend: Optional[AbsFrontend],
+ encoder: AbsEncoder,
predictor: CifPredictorV3,
predictor_bias: int = 0,
token_list=None,
):
- assert check_argument_types()
super().__init__()
# note that eos is the same as sos (equivalent ID)
@@ -51,7 +53,7 @@
self.predictor_bias = predictor_bias
self.criterion_pre = mae_loss()
self.token_list = token_list
-
+
def forward(
self,
speech: torch.Tensor,
@@ -60,7 +62,6 @@
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, )
@@ -108,7 +109,6 @@
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, )
@@ -123,7 +123,7 @@
encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
return encoder_out, encoder_out_lens
-
+
def _extract_feats(
self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -146,8 +146,8 @@
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
- encoder_out_mask,
- token_num)
+ encoder_out_mask,
+ token_num)
return ds_alphas, ds_cif_peak, us_alphas, us_peaks
def collect_feats(
--
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