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/bin/train.py | 89 +++++++++++++++++++++++---------------------
1 files changed, 46 insertions(+), 43 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 6a88233..72fa9fa 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -8,34 +8,30 @@
import hydra
from omegaconf import DictConfig, OmegaConf
from funasr.train_utils.set_all_random_seed import set_all_random_seed
-# from funasr.model_class_factory1 import model_choices
from funasr.models.lora.utils import mark_only_lora_as_trainable
-from funasr.optimizers import optim_choices
-from funasr.schedulers import scheduler_choices
+from funasr.optimizers import optim_classes
+from funasr.schedulers import scheduler_classes
from funasr.train_utils.load_pretrained_model import load_pretrained_model
from funasr.train_utils.initialize import initialize
-from funasr.datasets.fun_datasets.data_sampler import BatchSampler
# from funasr.tokenizer.build_tokenizer import build_tokenizer
# from funasr.tokenizer.token_id_converter import TokenIDConverter
-from funasr.tokenizer.funtoken import build_tokenizer
-from funasr.datasets.fun_datasets.dataset_jsonl import AudioDataset
+# from funasr.tokenizer.funtoken import build_tokenizer
from funasr.train_utils.trainer import Trainer
-# from funasr.utils.load_fr_py import load_class_from_path
-from funasr.utils.dynamic_import import dynamic_import
import torch.distributed as dist
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
@hydra.main(config_name=None, version_base=None)
def main_hydra(kwargs: DictConfig):
import pdb; pdb.set_trace()
- if ":" in kwargs["model"]:
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
- import pdb;
- pdb.set_trace()
+
main(**kwargs)
@@ -43,6 +39,7 @@
# preprocess_config(kwargs)
# import pdb; pdb.set_trace()
# set random seed
+ registry_tables.print_register_tables()
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)
@@ -56,31 +53,38 @@
dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method='env://')
torch.cuda.set_device(local_rank)
-
- # build_tokenizer
- tokenizer = build_tokenizer(
- token_type=kwargs.get("token_type", "char"),
- bpemodel=kwargs.get("bpemodel", None),
- delimiter=kwargs.get("delimiter", None),
- space_symbol=kwargs.get("space_symbol", "<space>"),
- non_linguistic_symbols=kwargs.get("non_linguistic_symbols", None),
- g2p_type=kwargs.get("g2p_type", None),
- token_list=kwargs.get("token_list", None),
- unk_symbol=kwargs.get("unk_symbol", "<unk>"),
- )
+ # save config.yaml
+ if (use_ddp or use_fsdp) and dist.get_rank() == 0 or not (use_ddp or use_fsdp) and local_rank == 0:
+ os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True)
+ yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml")
+ OmegaConf.save(config=kwargs, f=yaml_file)
+ logging.info("config.yaml is saved to: %s", yaml_file)
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = registry_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 = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+
# import pdb;
# pdb.set_trace()
# build model
- # model_class = model_choices.get_class(kwargs.get("model", "asr"))
- # model_class = load_class_from_path(kwargs.get("model").split(":"))
- model_class = dynamic_import(kwargs.get("model"))
+ model_class = registry_tables.model_classes.get(kwargs["model"].lower())
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
- frontend = model.frontend
+
+
+
# init_param
init_param = kwargs.get("init_param", None)
if init_param is not None:
- if not isinstance(init_param, Sequence):
+ if not isinstance(init_param, (list, tuple)):
init_param = (init_param,)
logging.info("init_param is not None: %s", init_param)
for p in init_param:
@@ -93,9 +97,8 @@
)
else:
initialize(model, kwargs.get("init", "kaiming_normal"))
-
- # import pdb;
- # pdb.set_trace()
+
+
# freeze_param
freeze_param = kwargs.get("freeze_param", None)
if freeze_param is not None:
@@ -122,33 +125,33 @@
# optim
optim = kwargs.get("optim", "adam")
- assert optim in optim_choices
- optim_class = optim_choices.get(optim)
+ assert optim in optim_classes
+ optim_class = optim_classes.get(optim)
optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
# scheduler
scheduler = kwargs.get("scheduler", "warmuplr")
- assert scheduler in scheduler_choices
- scheduler_class = scheduler_choices.get(scheduler)
+ assert scheduler in scheduler_classes
+ scheduler_class = scheduler_classes.get(scheduler)
scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
-
+ # import pdb;
+ # pdb.set_trace()
# dataset
- dataset_tr = AudioDataset(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
+ dataset_class = registry_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 = BatchSampler(dataset_tr, **kwargs.get("dataset_conf"), **kwargs.get("dataset_conf").get("batch_conf"))
+ batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
+ batch_sampler_class = registry_tables.batch_sampler_classes.get(batch_sampler.lower())
+ batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
collate_fn=dataset_tr.collator,
batch_sampler=batch_sampler,
num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
pin_memory=True)
- if (use_ddp or use_fsdp) and dist.get_rank() == 0 or not (use_ddp or use_fsdp) and local_rank == 0:
- os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True)
- yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml")
- OmegaConf.save(config=kwargs, f=yaml_file)
- logging.info("config.yaml is saved to: %s", yaml_file)
+
trainer = Trainer(
model=model,
--
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