From 856760465b1e04afeba807feb96c658e098b76f5 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 05 一月 2024 16:27:40 +0800
Subject: [PATCH] update
---
funasr/models/transformer/model.py | 18 +++++++++---------
1 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/funasr/models/transformer/model.py b/funasr/models/transformer/model.py
index e4eae10..f09f460 100644
--- a/funasr/models/transformer/model.py
+++ b/funasr/models/transformer/model.py
@@ -12,12 +12,12 @@
from funasr.metrics.compute_acc import th_accuracy
# from funasr.models.e2e_asr_common import ErrorCalculator
from funasr.train_utils.device_funcs import force_gatherable
-from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
+from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fbank
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
-from funasr.utils.register import register_class, registry_tables
+from funasr.register import tables
-@register_class("model_classes", "Transformer")
+@tables.register("model_classes", "Transformer")
class Transformer(nn.Module):
"""CTC-attention hybrid Encoder-Decoder model"""
@@ -60,19 +60,19 @@
super().__init__()
if frontend is not None:
- frontend_class = registry_tables.frontend_classes.get_class(frontend.lower())
+ frontend_class = tables.frontend_classes.get_class(frontend.lower())
frontend = frontend_class(**frontend_conf)
if specaug is not None:
- specaug_class = registry_tables.specaug_classes.get_class(specaug.lower())
+ specaug_class = tables.specaug_classes.get_class(specaug.lower())
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = registry_tables.normalize_classes.get_class(normalize.lower())
+ normalize_class = tables.normalize_classes.get_class(normalize.lower())
normalize = normalize_class(**normalize_conf)
- encoder_class = registry_tables.encoder_classes.get_class(encoder.lower())
+ encoder_class = tables.encoder_classes.get_class(encoder.lower())
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
if decoder is not None:
- decoder_class = registry_tables.decoder_classes.get_class(decoder.lower())
+ decoder_class = tables.decoder_classes.get_class(decoder.lower())
decoder = decoder_class(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -392,7 +392,7 @@
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
- audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ audio_sample_list = load_audio_and_text_image_video(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, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
--
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