From 0a4e3b7e64e9e095cfdcd4b3c28bde7aa58839e7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 11 二月 2023 17:40:00 +0800
Subject: [PATCH] readme
---
funasr/tasks/abs_task.py | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 53 insertions(+), 0 deletions(-)
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index 5424f13..02311fd 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -43,12 +43,15 @@
from funasr.iterators.chunk_iter_factory import ChunkIterFactory
from funasr.iterators.multiple_iter_factory import MultipleIterFactory
from funasr.iterators.sequence_iter_factory import SequenceIterFactory
+from funasr.main_funcs.collect_stats import collect_stats
from funasr.optimizers.sgd import SGD
+from funasr.optimizers.fairseq_adam import FairseqAdam
from funasr.samplers.build_batch_sampler import BATCH_TYPES
from funasr.samplers.build_batch_sampler import build_batch_sampler
from funasr.samplers.unsorted_batch_sampler import UnsortedBatchSampler
from funasr.schedulers.noam_lr import NoamLR
from funasr.schedulers.warmup_lr import WarmupLR
+from funasr.schedulers.tri_stage_scheduler import TriStageLR
from funasr.torch_utils.load_pretrained_model import load_pretrained_model
from funasr.torch_utils.model_summary import model_summary
from funasr.torch_utils.pytorch_version import pytorch_cudnn_version
@@ -83,6 +86,7 @@
optim_classes = dict(
adam=torch.optim.Adam,
+ fairseq_adam=FairseqAdam,
adamw=torch.optim.AdamW,
sgd=SGD,
adadelta=torch.optim.Adadelta,
@@ -149,6 +153,7 @@
CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR,
noamlr=NoamLR,
warmuplr=WarmupLR,
+ tri_stage=TriStageLR,
cycliclr=torch.optim.lr_scheduler.CyclicLR,
onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
@@ -1268,6 +1273,52 @@
if args.dry_run:
pass
+ elif args.collect_stats:
+ # Perform on collect_stats mode. This mode has two roles
+ # - Derive the length and dimension of all input data
+ # - Accumulate feats, square values, and the length for whitening
+
+ if args.valid_batch_size is None:
+ args.valid_batch_size = args.batch_size
+
+ if len(args.train_shape_file) != 0:
+ train_key_file = args.train_shape_file[0]
+ else:
+ train_key_file = None
+ if len(args.valid_shape_file) != 0:
+ valid_key_file = args.valid_shape_file[0]
+ else:
+ valid_key_file = None
+
+ collect_stats(
+ model=model,
+ train_iter=cls.build_streaming_iterator(
+ data_path_and_name_and_type=args.train_data_path_and_name_and_type,
+ key_file=train_key_file,
+ batch_size=args.batch_size,
+ dtype=args.train_dtype,
+ num_workers=args.num_workers,
+ allow_variable_data_keys=args.allow_variable_data_keys,
+ ngpu=args.ngpu,
+ preprocess_fn=cls.build_preprocess_fn(args, train=False),
+ collate_fn=cls.build_collate_fn(args, train=False),
+ ),
+ valid_iter=cls.build_streaming_iterator(
+ data_path_and_name_and_type=args.valid_data_path_and_name_and_type,
+ key_file=valid_key_file,
+ batch_size=args.valid_batch_size,
+ dtype=args.train_dtype,
+ num_workers=args.num_workers,
+ allow_variable_data_keys=args.allow_variable_data_keys,
+ ngpu=args.ngpu,
+ preprocess_fn=cls.build_preprocess_fn(args, train=False),
+ collate_fn=cls.build_collate_fn(args, train=False),
+ ),
+ output_dir=output_dir,
+ ngpu=args.ngpu,
+ log_interval=args.log_interval,
+ write_collected_feats=args.write_collected_feats,
+ )
else:
logging.info("Training args: {}".format(args))
# 6. Loads pre-trained model
@@ -1783,6 +1834,7 @@
collate_fn,
key_file: str = None,
batch_size: int = 1,
+ fs: dict = None,
dtype: str = np.float32,
num_workers: int = 1,
allow_variable_data_keys: bool = False,
@@ -1800,6 +1852,7 @@
dataset = IterableESPnetDataset(
data_path_and_name_and_type,
float_dtype=dtype,
+ fs=fs,
preprocess=preprocess_fn,
key_file=key_file,
)
--
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