From 622d799de8032f957c0d335827fdc637b8d89078 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 05 一月 2024 16:29:40 +0800
Subject: [PATCH] update
---
funasr/models/monotonic_aligner/model.py | 14 ++++++--------
1 files changed, 6 insertions(+), 8 deletions(-)
diff --git a/funasr/models/monotonic_aligner/model.py b/funasr/models/monotonic_aligner/model.py
index ece319d..cc03ee4 100644
--- a/funasr/models/monotonic_aligner/model.py
+++ b/funasr/models/monotonic_aligner/model.py
@@ -4,11 +4,13 @@
from torch.cuda.amp import autocast
from typing import Union, Dict, List, Tuple, Optional
+from funasr.register import tables
+from funasr.utils import postprocess_utils
+from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.cif_predictor import mae_loss
-from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
-from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
-from funasr.metrics.compute_acc import th_accuracy
from funasr.train_utils.device_funcs import force_gatherable
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
@@ -25,7 +27,6 @@
Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
https://arxiv.org/abs/2301.12343
"""
-
def __init__(
self,
input_size: int = 80,
@@ -41,7 +42,6 @@
length_normalized_loss: bool = False,
**kwargs,
):
-
super().__init__()
if specaug is not None:
@@ -155,7 +155,6 @@
frontend=None,
**kwargs,
):
-
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
@@ -190,8 +189,7 @@
timestamp_str, timestamp = ts_prediction_lfr6_standard(us_alpha[:encoder_out_lens[i] * 3],
us_peak[:encoder_out_lens[i] * 3],
copy.copy(token))
- text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
- token, timestamp)
+ text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
"timestamp": time_stamp_postprocessed,
}
--
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