From 0e622e694e6cb4459955f1e5942a7c53349ce640 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 19 十二月 2023 21:58:14 +0800
Subject: [PATCH] funasr2
---
funasr/models/transducer/model.py | 27 +++++++++++++--------------
1 files changed, 13 insertions(+), 14 deletions(-)
diff --git a/funasr/models/transducer/model.py b/funasr/models/transducer/model.py
index ec340f6..21de4ba 100644
--- a/funasr/models/transducer/model.py
+++ b/funasr/models/transducer/model.py
@@ -16,7 +16,6 @@
import random
import numpy as np
import time
-# from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
@@ -24,17 +23,17 @@
# from funasr.models.decoder.abs_decoder import AbsDecoder
# from funasr.models.e2e_asr_common import ErrorCalculator
# from funasr.models.encoder.abs_encoder import AbsEncoder
-# from funasr.models.frontend.abs_frontend import AbsFrontend
+# from funasr.frontends.abs_frontend import AbsFrontend
# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
-from funasr.models.predictor.cif import mae_loss
+from funasr.models.paraformer.cif_predictor import mae_loss
# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
# from funasr.models.specaug.abs_specaug import AbsSpecAug
-from funasr.models.transformer.add_sos_eos import add_sos_eos
+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.models.transformer.utils.nets_utils import th_accuracy
+from funasr.metrics.compute_acc import th_accuracy
from funasr.train_utils.device_funcs import force_gatherable
# from funasr.models.base_model import FunASRModel
-# from funasr.models.predictor.cif import CifPredictorV3
+# from funasr.models.paraformer.cif_predictor import CifPredictorV3
from funasr.models.paraformer.search import Hypothesis
from funasr.models.model_class_factory import *
@@ -46,7 +45,7 @@
@contextmanager
def autocast(enabled=True):
yield
-from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
+from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.transformer.utils.nets_utils import get_transducer_task_io
@@ -98,19 +97,19 @@
super().__init__()
if frontend is not None:
- frontend_class = frontend_choices.get_class(frontend)
+ frontend_class = frontend_classes.get_class(frontend)
frontend = frontend_class(**frontend_conf)
if specaug is not None:
- specaug_class = specaug_choices.get_class(specaug)
+ specaug_class = specaug_classes.get_class(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = normalize_choices.get_class(normalize)
+ normalize_class = normalize_classes.get_class(normalize)
normalize = normalize_class(**normalize_conf)
- encoder_class = encoder_choices.get_class(encoder)
+ encoder_class = encoder_classes.get_class(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
- decoder_class = decoder_choices.get_class(decoder)
+ decoder_class = decoder_classes.get_class(decoder)
decoder = decoder_class(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -118,7 +117,7 @@
)
decoder_output_size = decoder.output_size
- joint_network_class = joint_network_choices.get_class(decoder)
+ joint_network_class = joint_network_classes.get_class(decoder)
joint_network = joint_network_class(
vocab_size,
encoder_output_size,
@@ -521,7 +520,7 @@
audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, date_type=kwargs.get("date_type", "sound"), frontend=self.frontend)
+ speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
--
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