From a1b0cd33d50cee3e4612d1e787399e508b453a4a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 21 十二月 2023 14:20:21 +0800
Subject: [PATCH] rename register tables
---
funasr/bin/train.py | 14 +++++++-------
1 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 1e06c50..b1f0d06 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -21,7 +21,7 @@
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from funasr.download.download_from_hub import download_model
-from funasr.utils.register import registry_tables
+from funasr.register import tables
@hydra.main(config_name=None, version_base=None)
def main_hydra(kwargs: DictConfig):
@@ -39,7 +39,7 @@
# preprocess_config(kwargs)
# import pdb; pdb.set_trace()
# set random seed
- registry_tables.print()
+ tables.print()
set_all_random_seed(kwargs.get("seed", 0))
torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
@@ -62,14 +62,14 @@
tokenizer = kwargs.get("tokenizer", None)
if tokenizer is not None:
- tokenizer_class = registry_tables.tokenizer_classes.get(tokenizer.lower())
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
kwargs["tokenizer"] = tokenizer
# build frontend if frontend is none None
frontend = kwargs.get("frontend", None)
if frontend is not None:
- frontend_class = registry_tables.frontend_classes.get(frontend.lower())
+ frontend_class = tables.frontend_classes.get(frontend.lower())
frontend = frontend_class(**kwargs["frontend_conf"])
kwargs["frontend"] = frontend
kwargs["input_size"] = frontend.output_size()
@@ -77,7 +77,7 @@
# import pdb;
# pdb.set_trace()
# build model
- model_class = registry_tables.model_classes.get(kwargs["model"].lower())
+ model_class = tables.model_classes.get(kwargs["model"].lower())
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
@@ -139,12 +139,12 @@
# import pdb;
# pdb.set_trace()
# dataset
- dataset_class = registry_tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset").lower())
+ dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset").lower())
dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
# dataloader
batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
- batch_sampler_class = registry_tables.batch_sampler_classes.get(batch_sampler.lower())
+ batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler.lower())
if batch_sampler is not None:
batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
--
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