From 8a100b731efba8c18f7e7b6cb1cb04ded94248b3 Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期二, 21 三月 2023 14:52:15 +0800
Subject: [PATCH] add aishell-1 rnnt egs
---
funasr/tasks/abs_task.py | 19 +++++++++++++++++--
1 files changed, 17 insertions(+), 2 deletions(-)
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index 02311fd..cc5b708 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -71,7 +71,7 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_int
from funasr.utils.types import str_or_none
-from funasr.utils.wav_utils import calc_shape, generate_data_list
+from funasr.utils.wav_utils import calc_shape, generate_data_list, filter_wav_text
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
try:
@@ -1153,6 +1153,14 @@
if args.batch_bins is not None:
args.batch_bins = args.batch_bins * args.ngpu
+ # filter samples if wav.scp and text are mismatch
+ if (args.train_shape_file is None and args.dataset_type == "small") or args.train_data_file is None and args.dataset_type == "large":
+ if not args.simple_ddp or distributed_option.dist_rank == 0:
+ filter_wav_text(args.data_dir, args.train_set)
+ filter_wav_text(args.data_dir, args.dev_set)
+ if args.simple_ddp:
+ dist.barrier()
+
if args.train_shape_file is None and args.dataset_type == "small":
if not args.simple_ddp or distributed_option.dist_rank == 0:
calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)
@@ -1340,12 +1348,16 @@
if args.dataset_type == "large":
from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
+ frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
seg_dict_file=args.seg_dict_file if hasattr(args,
"seg_dict_file") else None,
+ punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
mode="train")
- valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
+ valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
+ frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
seg_dict_file=args.seg_dict_file if hasattr(args,
"seg_dict_file") else None,
+ punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
mode="eval")
elif args.dataset_type == "small":
train_iter_factory = cls.build_iter_factory(
@@ -1564,6 +1576,7 @@
preprocess=iter_options.preprocess_fn,
max_cache_size=iter_options.max_cache_size,
max_cache_fd=iter_options.max_cache_fd,
+ dest_sample_rate=args.frontend_conf["fs"] if args.frontend_conf else 16000,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
@@ -1835,6 +1848,7 @@
key_file: str = None,
batch_size: int = 1,
fs: dict = None,
+ mc: bool = False,
dtype: str = np.float32,
num_workers: int = 1,
allow_variable_data_keys: bool = False,
@@ -1853,6 +1867,7 @@
data_path_and_name_and_type,
float_dtype=dtype,
fs=fs,
+ mc=mc,
preprocess=preprocess_fn,
key_file=key_file,
)
--
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