speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge branch 'main' of github.com:alibaba-damo-academy/FunASR

Browse Source 
add
This commit is contained in:
游雁 2023-06-29 17:20:46 +08:00
commit 1bdb956318
123 changed files with 235 additions and 689 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
egs/alimeeting/sa_asr/local/format_wav_scp.py
View File

@ -11,7 +11,6 @@ import numpy as np
import resampy
import soundfile
from tqdm import tqdm
from typeguard import check_argument_types
from funasr.utils.cli_utils import get_commandline_args
from funasr.fileio.read_text import read_2column_text
@ -31,7 +30,6 @@ def str2int_tuple(integers: str) -> Optional[Tuple[int, ...]]:
(3, 4, 5)
"""
assert check_argument_types()
if integers.strip() in ("none", "None", "NONE", "null", "Null", "NULL"):
return None
return tuple(map(int, integers.strip().split(",")))

38
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/finetune.py
View File

@ -1,5 +1,4 @@
import os
<<<<<<< HEAD
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
@ -21,50 +20,17 @@ def modelscope_finetune(params):
batch_bins=params.batch_bins,
max_epoch=params.max_epoch,
lr=params.lr)
=======
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
>>>>>>> main
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
<<<<<<< HEAD
params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch", data_path="./data")
params.output_dir = "./checkpoint" # m模型保存路径
params.data_path = "./example_data/" # 数据路径
params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
params.max_epoch = 50 # 最大训练轮数
params.max_epoch = 20 # 最大训练轮数
params.lr = 0.00005 # 设置学习率
=======
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch"
params["model_revision"] = None
>>>>>>> main
modelscope_finetune(params)
modelscope_finetune(params)

33
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py
View File

@ -1,33 +1,3 @@
<<<<<<< HEAD
import os
import shutil
import argparse
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
def modelscope_infer(args):
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=args.model,
output_dir=args.output_dir,
batch_size=args.batch_size,
param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
)
inference_pipeline(audio_in=args.audio_in)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
parser.add_argument('--output_dir', type=str, default="./results/")
parser.add_argument('--decoding_mode', type=str, default="normal")
parser.add_argument('--hotword_txt', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--gpuid', type=str, default="0")
args = parser.parse_args()
modelscope_infer(args)
=======
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
@ -40,5 +10,4 @@ if __name__ == "__main__":
output_dir=output_dir,
)
rec_result = inference_pipeline(audio_in=audio_in, param_dict={"decoding_model":"offline"})
print(rec_result)
>>>>>>> main
print(rec_result)

52
funasr/bin/asr_infer.py
View File

@ -22,9 +22,7 @@ import numpy as np
import requests
import torch
from packaging.version import parse as V
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
@ -78,7 +76,6 @@ class Speech2Text:
frontend_conf: dict = None,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -192,7 +189,6 @@ class Speech2Text:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@ -248,7 +244,6 @@ class Speech2Text:
text = None
results.append((text, token, token_int, hyp))
assert check_return_type(results)
return results
@ -288,7 +283,6 @@ class Speech2TextParaformer:
decoding_ind: int = 0,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -413,7 +407,6 @@ class Speech2TextParaformer:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@ -516,7 +509,6 @@ class Speech2TextParaformer:
vad_offset=begin_time)
results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
def generate_hotwords_list(self, hotword_list_or_file):
@ -656,7 +648,6 @@ class Speech2TextParaformerOnline:
hotword_list_or_file: str = None,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -776,7 +767,6 @@ class Speech2TextParaformerOnline:
text, token, token_int, hyp
"""
assert check_argument_types()
results = []
cache_en = cache["encoder"]
if speech.shape[1] < 16 * 60 and cache_en["is_final"]:
@ -871,7 +861,6 @@ class Speech2TextParaformerOnline:
results.append(postprocessed_result)
# assert check_return_type(results)
return results
@ -912,7 +901,6 @@ class Speech2TextUniASR:
frontend_conf: dict = None,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -1036,7 +1024,6 @@ class Speech2TextUniASR:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@ -1104,7 +1091,6 @@ class Speech2TextUniASR:
text = None
results.append((text, token, token_int, hyp))
assert check_return_type(results)
return results
@ -1143,7 +1129,6 @@ class Speech2TextMFCCA:
streaming: bool = False,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -1248,7 +1233,6 @@ class Speech2TextMFCCA:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@ -1298,7 +1282,6 @@ class Speech2TextMFCCA:
text = None
results.append((text, token, token_int, hyp))
assert check_return_type(results)
return results
@ -1355,7 +1338,6 @@ class Speech2TextTransducer:
"""Construct a Speech2Text object."""
super().__init__()
assert check_argument_types()
asr_model, asr_train_args = build_model_from_file(
asr_train_config, asr_model_file, cmvn_file, device
)
@ -1534,7 +1516,6 @@ class Speech2TextTransducer:
Returns:
nbest_hypothesis: N-best hypothesis.
"""
assert check_argument_types()
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@ -1566,7 +1547,6 @@ class Speech2TextTransducer:
Returns:
nbest_hypothesis: N-best hypothesis.
"""
assert check_argument_types()
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@ -1608,36 +1588,9 @@ class Speech2TextTransducer:
text = None
results.append((text, token, token_int, hyp))
assert check_return_type(results)
return results
@staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
) -> Speech2Text:
"""Build Speech2Text instance from the pretrained model.
Args:
model_tag: Model tag of the pretrained models.
Return:
: Speech2Text instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2TextTransducer(**kwargs)
class Speech2TextSAASR:
"""Speech2Text class
@ -1675,7 +1628,6 @@ class Speech2TextSAASR:
frontend_conf: dict = None,
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
scorers = {}
@ -1793,7 +1745,6 @@ class Speech2TextSAASR:
text, text_id, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@ -1886,5 +1837,4 @@ class Speech2TextSAASR:
results.append((text, text_id, token, token_int, hyp))
assert check_return_type(results)
return results

9
funasr/bin/asr_inference_launch.py
View File

@ -21,7 +21,6 @@ import torch
import torchaudio
import soundfile
import yaml
from typeguard import check_argument_types
from funasr.bin.asr_infer import Speech2Text
from funasr.bin.asr_infer import Speech2TextMFCCA
@ -80,7 +79,6 @@ def inference_asr(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@ -240,7 +238,6 @@ def inference_paraformer(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
@ -481,7 +478,6 @@ def inference_paraformer_vad_punc(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
@ -749,7 +745,6 @@ def inference_paraformer_online(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
@ -957,7 +952,6 @@ def inference_uniasr(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@ -1126,7 +1120,6 @@ def inference_mfcca(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@ -1314,7 +1307,6 @@ def inference_transducer(
right_context: Number of frames in right context AFTER subsampling.
display_partial_hypotheses: Whether to display partial hypotheses.
"""
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
@ -1464,7 +1456,6 @@ def inference_sa_asr(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if word_lm_train_config is not None:

65
funasr/bin/diar_infer.py
View File

@ -15,7 +15,6 @@ import numpy as np
import torch
from scipy.ndimage import median_filter
from torch.nn import functional as F
from typeguard import check_argument_types
from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.tasks.diar import DiarTask
@ -45,7 +44,6 @@ class Speech2DiarizationEEND:
device: str = "cpu",
dtype: str = "float32",
):
assert check_argument_types()
# 1. Build Diarization model
diar_model, diar_train_args = build_model_from_file(
@ -88,7 +86,6 @@ class Speech2DiarizationEEND:
diarization results
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@ -107,36 +104,6 @@ class Speech2DiarizationEEND:
return results
@staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build Speech2Diarization instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
Speech2Diarization: Speech2Diarization instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2DiarizationEEND(**kwargs)
class Speech2DiarizationSOND:
"""Speech2Xvector class
@ -163,7 +130,6 @@ class Speech2DiarizationSOND:
smooth_size: int = 83,
dur_threshold: float = 10,
):
assert check_argument_types()
# TODO: 1. Build Diarization model
diar_model, diar_train_args = build_model_from_file(
@ -283,7 +249,6 @@ class Speech2DiarizationSOND:
diarization results for each speaker
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@ -305,33 +270,3 @@ class Speech2DiarizationSOND:
results, pse_labels = self.post_processing(logits, profile.shape[1], output_format)
return results, pse_labels
@staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build Speech2Xvector instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
Speech2Xvector: Speech2Xvector instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2DiarizationSOND(**kwargs)

3
funasr/bin/diar_inference_launch.py
View File

@ -18,7 +18,6 @@ import numpy as np
import soundfile
import torch
from scipy.signal import medfilt
from typeguard import check_argument_types
from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND
from funasr.datasets.iterable_dataset import load_bytes
@ -52,7 +51,6 @@ def inference_sond(
mode: str = "sond",
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@ -233,7 +231,6 @@ def inference_eend(
param_dict: Optional[dict] = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:

2
funasr/bin/lm_inference_launch.py
View File

@ -15,7 +15,6 @@ from typing import Union
import numpy as np
import torch
from torch.nn.parallel import data_parallel
from typeguard import check_argument_types
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
@ -50,7 +49,6 @@ def inference_lm(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)

3
funasr/bin/punc_inference_launch.py
View File

@ -14,7 +14,6 @@ from typing import Optional
from typing import Union
import torch
from typeguard import check_argument_types
from funasr.bin.punc_infer import Text2Punc, Text2PuncVADRealtime
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
@ -38,7 +37,6 @@ def inference_punc(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
@ -118,7 +116,6 @@ def inference_punc_vad_realtime(
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)

35
funasr/bin/sv_infer.py
View File

@ -12,8 +12,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.torch_utils.device_funcs import to_device
@ -42,7 +40,6 @@ class Speech2Xvector:
streaming: bool = False,
embedding_node: str = "resnet1_dense",
):
assert check_argument_types()
# TODO: 1. Build SV model
sv_model, sv_train_args = build_model_from_file(
@ -108,7 +105,6 @@ class Speech2Xvector:
embedding, ref_embedding, similarity_score
"""
assert check_argument_types()
self.sv_model.eval()
embedding = self.calculate_embedding(speech)
ref_emb, score = None, None
@ -117,35 +113,4 @@ class Speech2Xvector:
score = torch.cosine_similarity(embedding, ref_emb)
results = (embedding, ref_emb, score)
assert check_return_type(results)
return results
@staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build Speech2Xvector instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
Speech2Xvector: Speech2Xvector instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2Xvector(**kwargs)

7
funasr/bin/sv_inference_launch.py
View File

@ -15,7 +15,6 @@ from typing import Union
import numpy as np
import torch
from kaldiio import WriteHelper
from typeguard import check_argument_types
from funasr.bin.sv_infer import Speech2Xvector
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
@ -46,7 +45,6 @@ def inference_sv(
param_dict: Optional[dict] = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
@ -79,10 +77,7 @@ def inference_sv(
embedding_node=embedding_node
)
logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
speech2xvector = Speech2Xvector.from_pretrained(
model_tag=model_tag,
**speech2xvector_kwargs,
)
speech2xvector = Speech2Xvector(**speech2xvector_kwargs)
speech2xvector.sv_model.eval()
def _forward(

2
funasr/bin/tokenize_text.py
View File

@ -7,7 +7,6 @@ import sys
from typing import List
from typing import Optional
from typeguard import check_argument_types
from funasr.utils.cli_utils import get_commandline_args
from funasr.text.build_tokenizer import build_tokenizer
@ -81,7 +80,6 @@ def tokenize(
cleaner: Optional[str],
g2p: Optional[str],
):
assert check_argument_types()
logging.basicConfig(
level=log_level,

3
funasr/bin/tp_infer.py
View File

@ -9,7 +9,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.text.token_id_converter import TokenIDConverter
@ -26,7 +25,6 @@ class Speech2Timestamp:
dtype: str = "float32",
**kwargs,
):
assert check_argument_types()
# 1. Build ASR model
tp_model, tp_train_args = build_model_from_file(
timestamp_infer_config, timestamp_model_file, cmvn_file=None, device=device, task_name="asr", mode="tp"
@ -64,7 +62,6 @@ class Speech2Timestamp:
speech_lengths: Union[torch.Tensor, np.ndarray] = None,
text_lengths: Union[torch.Tensor, np.ndarray] = None
):
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):

2
funasr/bin/tp_inference_launch.py
View File

@ -13,7 +13,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.bin.tp_infer import Speech2Timestamp
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
@ -47,7 +46,6 @@ def inference_tp(
seg_dict_file: Optional[str] = None,
**kwargs,
):
assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)

4
funasr/bin/vad_infer.py
View File

@ -13,7 +13,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
@ -42,7 +41,6 @@ class Speech2VadSegment:
dtype: str = "float32",
**kwargs,
):
assert check_argument_types()
# 1. Build vad model
vad_model, vad_infer_args = build_model_from_file(
@ -76,7 +74,6 @@ class Speech2VadSegment:
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@ -149,7 +146,6 @@ class Speech2VadSegmentOnline(Speech2VadSegment):
text, token, token_int, hyp
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):

3
funasr/bin/vad_inference_launch.py
View File

@ -18,7 +18,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
@ -47,7 +46,6 @@ def inference_vad(
num_workers: int = 1,
**kwargs,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
@ -148,7 +146,6 @@ def inference_vad_online(
num_workers: int = 1,
**kwargs,
):
assert check_argument_types()
logging.basicConfig(
level=log_level,

2
funasr/build_utils/build_model_from_file.py
View File

@ -6,7 +6,6 @@ from typing import Union
import torch
import yaml
from typeguard import check_argument_types
from funasr.build_utils.build_model import build_model
from funasr.models.base_model import FunASRModel
@ -30,7 +29,6 @@ def build_model_from_file(
device: Device type, "cpu", "cuda", or "cuda:N".
"""
assert check_argument_types()
if config_file is None:
assert model_file is not None, (
"The argument 'model_file' must be provided "

2
funasr/build_utils/build_streaming_iterator.py
View File

@ -1,6 +1,5 @@
import numpy as np
from torch.utils.data import DataLoader
from typeguard import check_argument_types
from funasr.datasets.iterable_dataset import IterableESPnetDataset
from funasr.datasets.small_datasets.collate_fn import CommonCollateFn
@ -23,7 +22,6 @@ def build_streaming_iterator(
train: bool = False,
) -> DataLoader:
"""Build DataLoader using iterable dataset"""
assert check_argument_types()
# preprocess
if preprocess_fn is not None:

2
funasr/build_utils/build_sv_model.py
View File

@ -1,7 +1,6 @@
import logging
import torch
from typeguard import check_return_type
from funasr.layers.abs_normalize import AbsNormalize
from funasr.layers.global_mvn import GlobalMVN
@ -254,5 +253,4 @@ def build_sv_model(args):
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model

5
funasr/build_utils/build_trainer.py
View File

@ -25,7 +25,6 @@ import oss2
import torch
import torch.nn
import torch.optim
from typeguard import check_argument_types
from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.main_funcs.average_nbest_models import average_nbest_models
@ -118,7 +117,6 @@ class Trainer:
def build_options(self, args: argparse.Namespace) -> TrainerOptions:
"""Build options consumed by train(), eval()"""
assert check_argument_types()
return build_dataclass(TrainerOptions, args)
@classmethod
@ -156,7 +154,6 @@ class Trainer:
def run(self) -> None:
"""Perform training. This method performs the main process of training."""
assert check_argument_types()
# NOTE(kamo): Don't check the type more strictly as far trainer_options
model = self.model
optimizers = self.optimizers
@ -522,7 +519,6 @@ class Trainer:
options: TrainerOptions,
distributed_option: DistributedOption,
) -> Tuple[bool, bool]:
assert check_argument_types()
grad_noise = options.grad_noise
accum_grad = options.accum_grad
@ -758,7 +754,6 @@ class Trainer:
options: TrainerOptions,
distributed_option: DistributedOption,
) -> None:
assert check_argument_types()
ngpu = options.ngpu
# no_forward_run = options.no_forward_run
distributed = distributed_option.distributed

7
funasr/datasets/collate_fn.py
View File

@ -6,8 +6,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.modules.nets_utils import pad_list
@ -22,7 +20,6 @@ class CommonCollateFn:
not_sequence: Collection[str] = (),
max_sample_size=None
):
assert check_argument_types()
self.float_pad_value = float_pad_value
self.int_pad_value = int_pad_value
self.not_sequence = set(not_sequence)
@ -53,7 +50,6 @@ def common_collate_fn(
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
"""Concatenate ndarray-list to an array and convert to torch.Tensor.
"""
assert check_argument_types()
uttids = [u for u, _ in data]
data = [d for _, d in data]
@ -79,7 +75,6 @@ def common_collate_fn(
output[key + "_lengths"] = lens
output = (uttids, output)
assert check_return_type(output)
return output
def crop_to_max_size(feature, target_size):
@ -99,7 +94,6 @@ def clipping_collate_fn(
not_sequence: Collection[str] = (),
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
# mainly for pre-training
assert check_argument_types()
uttids = [u for u, _ in data]
data = [d for _, d in data]
@ -131,5 +125,4 @@ def clipping_collate_fn(
output[key + "_lengths"] = lens
output = (uttids, output)
assert check_return_type(output)
return output

6
funasr/datasets/dataset.py
View File

@ -23,8 +23,6 @@ import kaldiio
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.fileio.npy_scp import NpyScpReader
from funasr.fileio.rand_gen_dataset import FloatRandomGenerateDataset
@ -37,7 +35,6 @@ from funasr.utils.sized_dict import SizedDict
class AdapterForSoundScpReader(collections.abc.Mapping):
def __init__(self, loader, dtype=None):
assert check_argument_types()
self.loader = loader
self.dtype = dtype
self.rate = None
@ -284,7 +281,6 @@ class ESPnetDataset(AbsDataset):
max_cache_fd: int = 0,
dest_sample_rate: int = 16000,
):
assert check_argument_types()
if len(path_name_type_list) == 0:
raise ValueError(
'1 or more elements are required for "path_name_type_list"'
@ -379,7 +375,6 @@ class ESPnetDataset(AbsDataset):
return _mes
def __getitem__(self, uid: Union[str, int]) -> Tuple[str, Dict[str, np.ndarray]]:
assert check_argument_types()
# Change integer-id to string-id
if isinstance(uid, int):
@ -444,5 +439,4 @@ class ESPnetDataset(AbsDataset):
self.cache[uid] = data
retval = uid, data
assert check_return_type(retval)
return retval

2
funasr/datasets/iterable_dataset.py
View File

@ -16,7 +16,6 @@ import torch
import torchaudio
import soundfile
from torch.utils.data.dataset import IterableDataset
from typeguard import check_argument_types
import os.path
from funasr.datasets.dataset import ESPnetDataset
@ -121,7 +120,6 @@ class IterableESPnetDataset(IterableDataset):
int_dtype: str = "long",
key_file: str = None,
):
assert check_argument_types()
if len(path_name_type_list) == 0:
raise ValueError(
'1 or more elements are required for "path_name_type_list"'

2
funasr/datasets/large_datasets/build_dataloader.py
View File

@ -6,7 +6,6 @@ from typing import Union
import sentencepiece as spm
from torch.utils.data import DataLoader
from typeguard import check_argument_types
from funasr.datasets.large_datasets.dataset import Dataset
from funasr.iterators.abs_iter_factory import AbsIterFactory
@ -43,7 +42,6 @@ def load_seg_dict(seg_dict_file):
class SentencepiecesTokenizer(AbsTokenizer):
def __init__(self, model: Union[Path, str]):
assert check_argument_types()
self.model = str(model)
self.sp = None

11
funasr/datasets/preprocessor.py
View File

@ -11,8 +11,6 @@ from typing import Union
import numpy as np
import scipy.signal
import soundfile
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.cleaner import TextCleaner
@ -268,7 +266,6 @@ class CommonPreprocessor(AbsPreprocessor):
def _speech_process(
self, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, Union[str, np.ndarray]]:
assert check_argument_types()
if self.speech_name in data:
if self.train and (self.rirs is not None or self.noises is not None):
speech = data[self.speech_name]
@ -355,7 +352,6 @@ class CommonPreprocessor(AbsPreprocessor):
speech = data[self.speech_name]
ma = np.max(np.abs(speech))
data[self.speech_name] = speech * self.speech_volume_normalize / ma
assert check_return_type(data)
return data
def _text_process(
@ -372,13 +368,11 @@ class CommonPreprocessor(AbsPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[self.text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
def __call__(
self, uid: str, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, np.ndarray]:
assert check_argument_types()
data = self._speech_process(data)
data = self._text_process(data)
@ -445,7 +439,6 @@ class LMPreprocessor(CommonPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[self.text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
@ -502,13 +495,11 @@ class CommonPreprocessor_multi(AbsPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[text_n] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
def __call__(
self, uid: str, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, np.ndarray]:
assert check_argument_types()
if self.speech_name in data:
# Nothing now: candidates:
@ -612,7 +603,6 @@ class MutliTokenizerCommonPreprocessor(CommonPreprocessor):
tokens = self.tokenizer[i].text2tokens(text)
text_ints = self.token_id_converter[i].tokens2ids(tokens)
data[text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
@ -690,7 +680,6 @@ class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
def __call__(
self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
) -> Dict[str, Union[list, np.ndarray]]:
assert check_argument_types()
# Split words.
if isinstance(data[self.text_name], str):
split_text = self.split_words(data[self.text_name])

5
funasr/datasets/small_datasets/collate_fn.py
View File

@ -6,8 +6,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.modules.nets_utils import pad_list
@ -22,7 +20,6 @@ class CommonCollateFn:
not_sequence: Collection[str] = (),
max_sample_size=None
):
assert check_argument_types()
self.float_pad_value = float_pad_value
self.int_pad_value = int_pad_value
self.not_sequence = set(not_sequence)
@ -53,7 +50,6 @@ def common_collate_fn(
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
"""Concatenate ndarray-list to an array and convert to torch.Tensor.
"""
assert check_argument_types()
uttids = [u for u, _ in data]
data = [d for _, d in data]
@ -79,7 +75,6 @@ def common_collate_fn(
output[key + "_lengths"] = lens
output = (uttids, output)
assert check_return_type(output)
return output
def crop_to_max_size(feature, target_size):

6
funasr/datasets/small_datasets/dataset.py
View File

@ -15,8 +15,6 @@ import kaldiio
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.fileio.npy_scp import NpyScpReader
from funasr.fileio.sound_scp import SoundScpReader
@ -24,7 +22,6 @@ from funasr.fileio.sound_scp import SoundScpReader
class AdapterForSoundScpReader(collections.abc.Mapping):
def __init__(self, loader, dtype=None):
assert check_argument_types()
self.loader = loader
self.dtype = dtype
self.rate = None
@ -112,7 +109,6 @@ class ESPnetDataset(Dataset):
speed_perturb: Union[list, tuple] = None,
mode: str = "train",
):
assert check_argument_types()
if len(path_name_type_list) == 0:
raise ValueError(
'1 or more elements are required for "path_name_type_list"'
@ -207,7 +203,6 @@ class ESPnetDataset(Dataset):
return _mes
def __getitem__(self, uid: Union[str, int]) -> Tuple[str, Dict[str, np.ndarray]]:
assert check_argument_types()
# Change integer-id to string-id
if isinstance(uid, int):
@ -265,5 +260,4 @@ class ESPnetDataset(Dataset):
data[name] = value
retval = uid, data
assert check_return_type(retval)
return retval

2
funasr/datasets/small_datasets/length_batch_sampler.py
View File

@ -4,7 +4,6 @@ from typing import Dict
from typing import Tuple
from typing import Union
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
from funasr.samplers.abs_sampler import AbsSampler
@ -21,7 +20,6 @@ class LengthBatchSampler(AbsSampler):
drop_last: bool = False,
padding: bool = True,
):
assert check_argument_types()
assert batch_bins > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(

11
funasr/datasets/small_datasets/preprocessor.py
View File

@ -10,8 +10,6 @@ from typing import Union
import numpy as np
import scipy.signal
import soundfile
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.cleaner import TextCleaner
@ -260,7 +258,6 @@ class CommonPreprocessor(AbsPreprocessor):
def _speech_process(
self, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, Union[str, np.ndarray]]:
assert check_argument_types()
if self.speech_name in data:
if self.train and (self.rirs is not None or self.noises is not None):
speech = data[self.speech_name]
@ -347,7 +344,6 @@ class CommonPreprocessor(AbsPreprocessor):
speech = data[self.speech_name]
ma = np.max(np.abs(speech))
data[self.speech_name] = speech * self.speech_volume_normalize / ma
assert check_return_type(data)
return data
def _text_process(
@ -365,13 +361,11 @@ class CommonPreprocessor(AbsPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[self.text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
def __call__(
self, uid: str, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, np.ndarray]:
assert check_argument_types()
data = self._speech_process(data)
data = self._text_process(data)
@ -439,7 +433,6 @@ class LMPreprocessor(CommonPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[self.text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
@ -496,13 +489,11 @@ class CommonPreprocessor_multi(AbsPreprocessor):
tokens = self.tokenizer.text2tokens(text)
text_ints = self.token_id_converter.tokens2ids(tokens)
data[text_n] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
def __call__(
self, uid: str, data: Dict[str, Union[str, np.ndarray]]
) -> Dict[str, np.ndarray]:
assert check_argument_types()
if self.speech_name in data:
# Nothing now: candidates:
@ -606,7 +597,6 @@ class MutliTokenizerCommonPreprocessor(CommonPreprocessor):
tokens = self.tokenizer[i].text2tokens(text)
text_ints = self.token_id_converter[i].tokens2ids(tokens)
data[text_name] = np.array(text_ints, dtype=np.int64)
assert check_return_type(data)
return data
@ -685,7 +675,6 @@ class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
def __call__(
self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
) -> Dict[str, Union[list, np.ndarray]]:
assert check_argument_types()
# Split words.
if isinstance(data[self.text_name], str):
split_text = self.split_words(data[self.text_name])

2
funasr/export/export_model.py
View File

@ -1,7 +1,6 @@
import json
from typing import Union, Dict
from pathlib import Path
from typeguard import check_argument_types
import os
import logging
@ -26,7 +25,6 @@ class ModelExport:
calib_num: int = 200,
model_revision: str = None,
):
assert check_argument_types()
self.set_all_random_seed(0)
self.cache_dir = cache_dir

6
funasr/fileio/datadir_writer.py
View File

@ -2,8 +2,6 @@ from pathlib import Path
from typing import Union
import warnings
from typeguard import check_argument_types
from typeguard import check_return_type
class DatadirWriter:
@ -20,7 +18,6 @@ class DatadirWriter:
"""
def __init__(self, p: Union[Path, str]):
assert check_argument_types()
self.path = Path(p)
self.chilidren = {}
self.fd = None
@ -31,7 +28,6 @@ class DatadirWriter:
return self
def __getitem__(self, key: str) -> "DatadirWriter":
assert check_argument_types()
if self.fd is not None:
raise RuntimeError("This writer points out a file")
@ -41,11 +37,9 @@ class DatadirWriter:
self.has_children = True
retval = self.chilidren[key]
assert check_return_type(retval)
return retval
def __setitem__(self, key: str, value: str):
assert check_argument_types()
if self.has_children:
raise RuntimeError("This writer points out a directory")
if key in self.keys:

3
funasr/fileio/npy_scp.py
View File

@ -3,7 +3,6 @@ from pathlib import Path
from typing import Union
import numpy as np
from typeguard import check_argument_types
from funasr.fileio.read_text import read_2column_text
@ -25,7 +24,6 @@ class NpyScpWriter:
"""
def __init__(self, outdir: Union[Path, str], scpfile: Union[Path, str]):
assert check_argument_types()
self.dir = Path(outdir)
self.dir.mkdir(parents=True, exist_ok=True)
scpfile = Path(scpfile)
@ -73,7 +71,6 @@ class NpyScpReader(collections.abc.Mapping):
"""
def __init__(self, fname: Union[Path, str]):
assert check_argument_types()
self.fname = Path(fname)
self.data = read_2column_text(fname)

3
funasr/fileio/rand_gen_dataset.py
View File

@ -3,7 +3,6 @@ from pathlib import Path
from typing import Union
import numpy as np
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
@ -29,7 +28,6 @@ class FloatRandomGenerateDataset(collections.abc.Mapping):
dtype: Union[str, np.dtype] = "float32",
loader_type: str = "csv_int",
):
assert check_argument_types()
shape_file = Path(shape_file)
self.utt2shape = load_num_sequence_text(shape_file, loader_type)
self.dtype = np.dtype(dtype)
@ -68,7 +66,6 @@ class IntRandomGenerateDataset(collections.abc.Mapping):
dtype: Union[str, np.dtype] = "int64",
loader_type: str = "csv_int",
):
assert check_argument_types()
shape_file = Path(shape_file)
self.utt2shape = load_num_sequence_text(shape_file, loader_type)
self.dtype = np.dtype(dtype)

3
funasr/fileio/read_text.py
View File

@ -4,7 +4,6 @@ from typing import Dict
from typing import List
from typing import Union
from typeguard import check_argument_types
def read_2column_text(path: Union[Path, str]) -> Dict[str, str]:
@ -19,7 +18,6 @@ def read_2column_text(path: Union[Path, str]) -> Dict[str, str]:
{'key1': '/some/path/a.wav', 'key2': '/some/path/b.wav'}
"""
assert check_argument_types()
data = {}
with Path(path).open("r", encoding="utf-8") as f:
@ -47,7 +45,6 @@ def load_num_sequence_text(
>>> d = load_num_sequence_text('text')
>>> np.testing.assert_array_equal(d["key1"], np.array([1, 2, 3]))
"""
assert check_argument_types()
if loader_type == "text_int":
delimiter = " "
dtype = int

3
funasr/fileio/sound_scp.py
View File

@ -6,7 +6,6 @@ import random
import numpy as np
import soundfile
import librosa
from typeguard import check_argument_types
import torch
import torchaudio
@ -106,7 +105,6 @@ class SoundScpReader(collections.abc.Mapping):
dest_sample_rate: int = 16000,
speed_perturb: Union[list, tuple] = None,
):
assert check_argument_types()
self.fname = fname
self.dtype = dtype
self.always_2d = always_2d
@ -179,7 +177,6 @@ class SoundScpWriter:
format="wav",
dtype=None,
):
assert check_argument_types()
self.dir = Path(outdir)
self.dir.mkdir(parents=True, exist_ok=True)
scpfile = Path(scpfile)

2
funasr/iterators/chunk_iter_factory.py
View File

@ -9,7 +9,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.iterators.sequence_iter_factory import SequenceIterFactory
@ -51,7 +50,6 @@ class ChunkIterFactory(AbsIterFactory):
collate_fn=None,
pin_memory: bool = False,
):
assert check_argument_types()
assert all(len(x) == 1 for x in batches), "batch-size must be 1"
self.per_sample_iter_factory = SequenceIterFactory(

2
funasr/iterators/multiple_iter_factory.py
View File

@ -4,7 +4,6 @@ from typing import Collection
from typing import Iterator
import numpy as np
from typeguard import check_argument_types
from funasr.iterators.abs_iter_factory import AbsIterFactory
@ -16,7 +15,6 @@ class MultipleIterFactory(AbsIterFactory):
seed: int = 0,
shuffle: bool = False,
):
assert check_argument_types()
self.build_funcs = list(build_funcs)
self.seed = seed
self.shuffle = shuffle

2
funasr/iterators/sequence_iter_factory.py
View File

@ -4,7 +4,6 @@ from typing import Union
import numpy as np
from torch.utils.data import DataLoader
from typeguard import check_argument_types
from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.samplers.abs_sampler import AbsSampler
@ -46,7 +45,6 @@ class SequenceIterFactory(AbsIterFactory):
collate_fn=None,
pin_memory: bool = False,
):
assert check_argument_types()
if not isinstance(batches, AbsSampler):
self.sampler = RawSampler(batches)

2
funasr/layers/global_mvn.py
View File

@ -4,7 +4,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.modules.nets_utils import make_pad_mask
from funasr.layers.abs_normalize import AbsNormalize
@ -28,7 +27,6 @@ class GlobalMVN(AbsNormalize, InversibleInterface):
norm_vars: bool = True,
eps: float = 1.0e-20,
):
assert check_argument_types()
super().__init__()
self.norm_means = norm_means
self.norm_vars = norm_vars

2
funasr/layers/label_aggregation.py
View File

@ -1,5 +1,4 @@
import torch
from typeguard import check_argument_types
from typing import Optional
from typing import Tuple
@ -13,7 +12,6 @@ class LabelAggregate(torch.nn.Module):
hop_length: int = 128,
center: bool = True,
):
assert check_argument_types()
super().__init__()
self.win_length = win_length

4
funasr/layers/mask_along_axis.py
View File

@ -1,6 +1,5 @@
import math
import torch
from typeguard import check_argument_types
from typing import Sequence
from typing import Union
@ -147,7 +146,6 @@ class MaskAlongAxis(torch.nn.Module):
dim: Union[int, str] = "time",
replace_with_zero: bool = True,
):
assert check_argument_types()
if isinstance(mask_width_range, int):
mask_width_range = (0, mask_width_range)
if len(mask_width_range) != 2:
@ -214,7 +212,6 @@ class MaskAlongAxisVariableMaxWidth(torch.nn.Module):
dim: Union[int, str] = "time",
replace_with_zero: bool = True,
):
assert check_argument_types()
if isinstance(mask_width_ratio_range, float):
mask_width_ratio_range = (0.0, mask_width_ratio_range)
if len(mask_width_ratio_range) != 2:
@ -283,7 +280,6 @@ class MaskAlongAxisLFR(torch.nn.Module):
replace_with_zero: bool = True,
lfr_rate: int = 1,
):
assert check_argument_types()
if isinstance(mask_width_range, int):
mask_width_range = (0, mask_width_range)
if len(mask_width_range) != 2:

4
funasr/layers/sinc_conv.py
View File

@ -5,7 +5,6 @@
"""Sinc convolutions."""
import math
import torch
from typeguard import check_argument_types
from typing import Union
@ -71,7 +70,6 @@ class SincConv(torch.nn.Module):
window_func: Window function on the filter, one of ["hamming", "none"].
fs (str, int, float): Sample rate of the input data
"""
assert check_argument_types()
super().__init__()
window_funcs = {
"none": self.none_window,
@ -208,7 +206,6 @@ class MelScale:
torch.Tensor: Filter start frequencíes.
torch.Tensor: Filter stop frequencies.
"""
assert check_argument_types()
# min and max bandpass edge frequencies
min_frequency = torch.tensor(30.0)
max_frequency = torch.tensor(fs * 0.5)
@ -257,7 +254,6 @@ class BarkScale:
torch.Tensor: Filter start frequencíes.
torch.Tensor: Filter stop frequencíes.
"""
assert check_argument_types()
# min and max BARK center frequencies by approximation
min_center_frequency = torch.tensor(70.0)
max_center_frequency = torch.tensor(fs * 0.45)

2
funasr/layers/stft.py
View File

@ -5,7 +5,6 @@ from typing import Union
import torch
from torch_complex.tensor import ComplexTensor
from typeguard import check_argument_types
from funasr.modules.nets_utils import make_pad_mask
from funasr.layers.complex_utils import is_complex
@ -30,7 +29,6 @@ class Stft(torch.nn.Module, InversibleInterface):
normalized: bool = False,
onesided: bool = True,
):
assert check_argument_types()
super().__init__()
self.n_fft = n_fft
if win_length is None:

2
funasr/layers/utterance_mvn.py
View File

@ -1,7 +1,6 @@
from typing import Tuple
import torch
from typeguard import check_argument_types
from funasr.modules.nets_utils import make_pad_mask
from funasr.layers.abs_normalize import AbsNormalize
@ -14,7 +13,6 @@ class UtteranceMVN(AbsNormalize):
norm_vars: bool = False,
eps: float = 1.0e-20,
):
assert check_argument_types()
super().__init__()
self.norm_means = norm_means
self.norm_vars = norm_vars

2
funasr/main_funcs/average_nbest_models.py
View File

@ -8,7 +8,6 @@ import os
from io import BytesIO
import torch
from typeguard import check_argument_types
from typing import Collection
from funasr.train.reporter import Reporter
@ -34,7 +33,6 @@ def average_nbest_models(
nbest: Number of best model files to be averaged
suffix: A suffix added to the averaged model file name
"""
assert check_argument_types()
if isinstance(nbest, int):
nbests = [nbest]
else:

2
funasr/main_funcs/collect_stats.py
View File

@ -11,7 +11,6 @@ import numpy as np
import torch
from torch.nn.parallel import data_parallel
from torch.utils.data import DataLoader
from typeguard import check_argument_types
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.fileio.npy_scp import NpyScpWriter
@ -37,7 +36,6 @@ def collect_stats(
This method is used before executing train().
"""
assert check_argument_types()
npy_scp_writers = {}
for itr, mode in zip([train_iter, valid_iter], ["train", "valid"]):

7
funasr/models/ctc.py
View File

@ -2,7 +2,6 @@ import logging
import torch
import torch.nn.functional as F
from typeguard import check_argument_types
class CTC(torch.nn.Module):
@ -25,7 +24,6 @@ class CTC(torch.nn.Module):
reduce: bool = True,
ignore_nan_grad: bool = True,
):
assert check_argument_types()
super().__init__()
eprojs = encoder_output_size
self.dropout_rate = dropout_rate
@ -41,11 +39,6 @@ class CTC(torch.nn.Module):
if ignore_nan_grad:
logging.warning("ignore_nan_grad option is not supported for warp_ctc")
self.ctc_loss = warp_ctc.CTCLoss(size_average=True, reduce=reduce)
elif self.ctc_type == "gtnctc":
from espnet.nets.pytorch_backend.gtn_ctc import GTNCTCLossFunction
self.ctc_loss = GTNCTCLossFunction.apply
else:
raise ValueError(
f'ctc_type must be "builtin" or "warpctc": {self.ctc_type}'

2
funasr/models/data2vec.py
View File

@ -10,7 +10,6 @@ from typing import Optional
from typing import Tuple
import torch
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.models.encoder.abs_encoder import AbsEncoder
@ -40,7 +39,6 @@ class Data2VecPretrainModel(FunASRModel):
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
):
assert check_argument_types()
super().__init__()

2
funasr/models/decoder/contextual_decoder.py
View File

@ -7,7 +7,6 @@ import numpy as np
from funasr.modules.streaming_utils import utils as myutils
from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
from typeguard import check_argument_types
from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.modules.embedding import PositionalEncoding
@ -126,7 +125,6 @@ class ContextualParaformerDecoder(ParaformerSANMDecoder):
kernel_size: int = 21,
sanm_shfit: int = 0,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,

2
funasr/models/decoder/rnn_decoder.py
View File

@ -3,7 +3,6 @@ import random
import numpy as np
import torch
import torch.nn.functional as F
from typeguard import check_argument_types
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.nets_utils import to_device
@ -97,7 +96,6 @@ class RNNDecoder(AbsDecoder):
att_conf: dict = get_default_kwargs(build_attention_list),
):
# FIXME(kamo): The parts of num_spk should be refactored more more more
assert check_argument_types()
if rnn_type not in {"lstm", "gru"}:
raise ValueError(f"Not supported: rnn_type={rnn_type}")

2
funasr/models/decoder/rnnt_decoder.py
View File

@ -3,7 +3,6 @@
from typing import List, Optional, Tuple
import torch
from typeguard import check_argument_types
from funasr.modules.beam_search.beam_search_transducer import Hypothesis
from funasr.models.specaug.specaug import SpecAug
@ -38,7 +37,6 @@ class RNNTDecoder(torch.nn.Module):
"""Construct a RNNDecoder object."""
super().__init__()
assert check_argument_types()
if rnn_type not in ("lstm", "gru"):
raise ValueError(f"Not supported: rnn_type={rnn_type}")

3
funasr/models/decoder/sanm_decoder.py
View File

@ -7,7 +7,6 @@ import numpy as np
from funasr.modules.streaming_utils import utils as myutils
from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
from typeguard import check_argument_types
from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.modules.embedding import PositionalEncoding
@ -181,7 +180,6 @@ class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
embed_tensor_name_prefix_tf: str = None,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@ -838,7 +836,6 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
tf2torch_tensor_name_prefix_torch: str = "decoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,

10
funasr/models/decoder/transformer_decoder.py
View File

@ -9,7 +9,6 @@ from typing import Tuple
import torch
from torch import nn
from typeguard import check_argument_types
from funasr.models.decoder.abs_decoder import AbsDecoder
from funasr.modules.attention import MultiHeadedAttention
@ -184,7 +183,6 @@ class BaseTransformerDecoder(AbsDecoder, BatchScorerInterface):
pos_enc_class=PositionalEncoding,
normalize_before: bool = True,
):
assert check_argument_types()
super().__init__()
attention_dim = encoder_output_size
@ -373,7 +371,6 @@ class TransformerDecoder(BaseTransformerDecoder):
normalize_before: bool = True,
concat_after: bool = False,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@ -428,7 +425,6 @@ class ParaformerDecoderSAN(BaseTransformerDecoder):
concat_after: bool = False,
embeds_id: int = -1,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@ -540,7 +536,6 @@ class LightweightConvolutionTransformerDecoder(BaseTransformerDecoder):
conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
conv_usebias: int = False,
):
assert check_argument_types()
if len(conv_kernel_length) != num_blocks:
raise ValueError(
"conv_kernel_length must have equal number of values to num_blocks: "
@ -602,7 +597,6 @@ class LightweightConvolution2DTransformerDecoder(BaseTransformerDecoder):
conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
conv_usebias: int = False,
):
assert check_argument_types()
if len(conv_kernel_length) != num_blocks:
raise ValueError(
"conv_kernel_length must have equal number of values to num_blocks: "
@ -664,7 +658,6 @@ class DynamicConvolutionTransformerDecoder(BaseTransformerDecoder):
conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
conv_usebias: int = False,
):
assert check_argument_types()
if len(conv_kernel_length) != num_blocks:
raise ValueError(
"conv_kernel_length must have equal number of values to num_blocks: "
@ -726,7 +719,6 @@ class DynamicConvolution2DTransformerDecoder(BaseTransformerDecoder):
conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
conv_usebias: int = False,
):
assert check_argument_types()
if len(conv_kernel_length) != num_blocks:
raise ValueError(
"conv_kernel_length must have equal number of values to num_blocks: "
@ -781,7 +773,6 @@ class BaseSAAsrTransformerDecoder(AbsDecoder, BatchScorerInterface):
pos_enc_class=PositionalEncoding,
normalize_before: bool = True,
):
assert check_argument_types()
super().__init__()
attention_dim = encoder_output_size
@ -955,7 +946,6 @@ class SAAsrTransformerDecoder(BaseSAAsrTransformerDecoder):
normalize_before: bool = True,
concat_after: bool = False,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,

2
funasr/models/e2e_asr.py
View File

@ -11,7 +11,6 @@ from typing import Tuple
from typing import Union
import torch
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
@ -65,7 +64,6 @@ class ASRModel(FunASRModel):
preencoder: Optional[AbsPreEncoder] = None,
postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight

2
funasr/models/e2e_asr_contextual_paraformer.py
View File

@ -9,7 +9,6 @@ from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.models.ctc import CTC
@ -73,7 +72,6 @@ class NeatContextualParaformer(Paraformer):
preencoder: Optional[AbsPreEncoder] = None,
postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight

2
funasr/models/e2e_asr_mfcca.py
View File

@ -7,7 +7,6 @@ from typing import Tuple
from typing import Union
import logging
import torch
from typeguard import check_argument_types
from funasr.modules.e2e_asr_common import ErrorCalculator
from funasr.modules.nets_utils import th_accuracy
@ -65,7 +64,6 @@ class MFCCA(FunASRModel):
sym_blank: str = "<blank>",
preencoder: Optional[AbsPreEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"

6
funasr/models/e2e_asr_paraformer.py
View File

@ -10,7 +10,6 @@ from typing import Union
import torch
import random
import numpy as np
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
@ -80,7 +79,6 @@ class Paraformer(FunASRModel):
postencoder: Optional[AbsPostEncoder] = None,
use_1st_decoder_loss: bool = False,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight
@ -645,7 +643,6 @@ class ParaformerOnline(Paraformer):
postencoder: Optional[AbsPostEncoder] = None,
use_1st_decoder_loss: bool = False,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight
@ -1255,7 +1252,6 @@ class ParaformerBert(Paraformer):
preencoder: Optional[AbsPreEncoder] = None,
postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight
@ -1528,7 +1524,6 @@ class BiCifParaformer(Paraformer):
preencoder: Optional[AbsPreEncoder] = None,
postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight
@ -1806,7 +1801,6 @@ class ContextualParaformer(Paraformer):
preencoder: Optional[AbsPreEncoder] = None,
postencoder: Optional[AbsPostEncoder] = None,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight

9
funasr/models/e2e_asr_transducer.py
View File

@ -6,7 +6,6 @@ from typing import Dict, List, Optional, Tuple, Union
import torch
from packaging.version import parse as V
from typeguard import check_argument_types
from funasr.losses.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
@ -86,8 +85,6 @@ class TransducerModel(FunASRModel):
"""Construct an ESPnetASRTransducerModel object."""
super().__init__()
assert check_argument_types()
# The following labels ID are reserved: 0 (blank) and vocab_size - 1 (sos/eos)
self.blank_id = 0
self.vocab_size = vocab_size
@ -546,8 +543,6 @@ class UnifiedTransducerModel(FunASRModel):
"""Construct an ESPnetASRTransducerModel object."""
super().__init__()
assert check_argument_types()
# The following labels ID are reserved: 0 (blank) and vocab_size - 1 (sos/eos)
self.blank_id = 0
@ -713,7 +708,7 @@ class UnifiedTransducerModel(FunASRModel):
loss_lm = self._calc_lm_loss(decoder_out, target)
loss_trans = loss_trans_utt + loss_trans_chunk
loss_ctc = loss_ctc + loss_ctc_chunk
loss_ctc = loss_ctc + loss_ctc_chunk
loss_ctc = loss_att + loss_att_chunk
loss = (
@ -1018,4 +1013,4 @@ class UnifiedTransducerModel(FunASRModel):
ignore_label=self.ignore_id,
)
return loss_att, acc_att
return loss_att, acc_att

2
funasr/models/e2e_diar_eend_ola.py
View File

@ -9,7 +9,6 @@ from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
from typeguard import check_argument_types
from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
@ -48,7 +47,6 @@ class DiarEENDOLAModel(FunASRModel):
mapping_dict=None,
**kwargs,
):
assert check_argument_types()
super().__init__()
self.frontend = frontend

2
funasr/models/e2e_diar_sond.py
View File

@ -12,7 +12,6 @@ from typing import Tuple, List
import numpy as np
import torch
from torch.nn import functional as F
from typeguard import check_argument_types
from funasr.modules.nets_utils import to_device
from funasr.modules.nets_utils import make_pad_mask
@ -66,7 +65,6 @@ class DiarSondModel(FunASRModel):
inter_score_loss_weight: float = 0.0,
inputs_type: str = "raw",
):
assert check_argument_types()
super().__init__()

2
funasr/models/e2e_sa_asr.py
View File

@ -12,7 +12,6 @@ from typing import Union
import torch
import torch.nn.functional as F
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
@ -67,7 +66,6 @@ class SAASRModel(FunASRModel):
sym_blank: str = "<blank>",
extract_feats_in_collect_stats: bool = True,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight

2
funasr/models/e2e_sv.py
View File

@ -12,7 +12,6 @@ from typing import Tuple
from typing import Union
import torch
from typeguard import check_argument_types
from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
@ -56,7 +55,6 @@ class ESPnetSVModel(FunASRModel):
pooling_layer: torch.nn.Module,
decoder: AbsDecoder,
):
assert check_argument_types()
super().__init__()
# note that eos is the same as sos (equivalent ID)

2
funasr/models/e2e_tp.py
View File

@ -9,7 +9,6 @@ from typing import Union
import torch
import numpy as np
from typeguard import check_argument_types
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.frontend.abs_frontend import AbsFrontend
@ -42,7 +41,6 @@ class TimestampPredictor(FunASRModel):
predictor_bias: int = 0,
token_list=None,
):
assert check_argument_types()
super().__init__()
# note that eos is the same as sos (equivalent ID)

2
funasr/models/e2e_uni_asr.py
View File

@ -8,7 +8,6 @@ from typing import Tuple
from typing import Union
import torch
from typeguard import check_argument_types
from funasr.models.e2e_asr_common import ErrorCalculator
from funasr.modules.nets_utils import th_accuracy
@ -82,7 +81,6 @@ class UniASR(FunASRModel):
postencoder: Optional[AbsPostEncoder] = None,
encoder1_encoder2_joint_training: bool = True,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight

3
funasr/models/encoder/conformer_encoder.py
View File

@ -12,7 +12,6 @@ from typing import Dict
import torch
from torch import nn
from typeguard import check_argument_types
from funasr.models.ctc import CTC
from funasr.modules.attention import (
@ -533,7 +532,6 @@ class ConformerEncoder(AbsEncoder):
interctc_use_conditioning: bool = False,
stochastic_depth_rate: Union[float, List[float]] = 0.0,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
@ -943,7 +941,6 @@ class ConformerChunkEncoder(AbsEncoder):
"""Construct an Encoder object."""
super().__init__()
assert check_argument_types()
self.embed = StreamingConvInput(
input_size,

2
funasr/models/encoder/data2vec_encoder.py
View File

@ -10,7 +10,6 @@ import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from typeguard import check_argument_types
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.data2vec.data_utils import compute_mask_indices
@ -97,7 +96,6 @@ class Data2VecEncoder(AbsEncoder):
# FP16 optimization
required_seq_len_multiple: int = 2,
):
assert check_argument_types()
super().__init__()
# ConvFeatureExtractionModel

2
funasr/models/encoder/mfcca_encoder.py
View File

@ -5,7 +5,6 @@ import logging
import torch
from torch import nn
from typeguard import check_argument_types
from funasr.models.encoder.encoder_layer_mfcca import EncoderLayer
from funasr.modules.nets_utils import get_activation
@ -161,7 +160,6 @@ class MFCCAEncoder(AbsEncoder):
cnn_module_kernel: int = 31,
padding_idx: int = -1,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size

2
funasr/models/encoder/opennmt_encoders/conv_encoder.py
View File

@ -7,7 +7,6 @@ import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
from typeguard import check_argument_types
import numpy as np
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.layer_norm import LayerNorm
@ -90,7 +89,6 @@ class ConvEncoder(AbsEncoder):
tf2torch_tensor_name_prefix_torch: str = "speaker_encoder",
tf2torch_tensor_name_prefix_tf: str = "EAND/speaker_encoder",
):
assert check_argument_types()
super().__init__()
self._output_size = num_units

1
funasr/models/encoder/opennmt_encoders/fsmn_encoder.py
View File

@ -7,7 +7,6 @@ import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
from typeguard import check_argument_types
import numpy as np
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.layer_norm import LayerNorm

2
funasr/models/encoder/opennmt_encoders/self_attention_encoder.py
View File

@ -7,7 +7,6 @@ import logging
import torch
import torch.nn as nn
from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
from typeguard import check_argument_types
import numpy as np
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.attention import MultiHeadSelfAttention, MultiHeadedAttentionSANM
@ -144,7 +143,6 @@ class SelfAttentionEncoder(AbsEncoder):
tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
out_units=None,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size

2
funasr/models/encoder/rnn_encoder.py
View File

@ -5,7 +5,6 @@ from typing import Tuple
import numpy as np
import torch
from typeguard import check_argument_types
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.rnn.encoders import RNN
@ -37,7 +36,6 @@ class RNNEncoder(AbsEncoder):
dropout: float = 0.0,
subsample: Optional[Sequence[int]] = (2, 2, 1, 1),
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
self.rnn_type = rnn_type

4
funasr/models/encoder/sanm_encoder.py
View File

@ -8,7 +8,6 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
from typeguard import check_argument_types
import numpy as np
from funasr.torch_utils.device_funcs import to_device
from funasr.modules.nets_utils import make_pad_mask
@ -151,7 +150,6 @@ class SANMEncoder(AbsEncoder):
tf2torch_tensor_name_prefix_torch: str = "encoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
@ -601,7 +599,6 @@ class SANMEncoderChunkOpt(AbsEncoder):
tf2torch_tensor_name_prefix_torch: str = "encoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
@ -1060,7 +1057,6 @@ class SANMVadEncoder(AbsEncoder):
sanm_shfit : int = 0,
selfattention_layer_type: str = "sanm",
):
assert check_argument_types()
super().__init__()
self._output_size = output_size

2
funasr/models/encoder/transformer_encoder.py
View File

@ -9,7 +9,6 @@ from typing import Tuple
import torch
from torch import nn
from typeguard import check_argument_types
import logging
from funasr.models.ctc import CTC
@ -189,7 +188,6 @@ class TransformerEncoder(AbsEncoder):
interctc_layer_idx: List[int] = [],
interctc_use_conditioning: bool = False,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size

3
funasr/models/frontend/default.py
View File

@ -7,7 +7,6 @@ import humanfriendly
import numpy as np
import torch
from torch_complex.tensor import ComplexTensor
from typeguard import check_argument_types
from funasr.layers.log_mel import LogMel
from funasr.layers.stft import Stft
@ -40,7 +39,6 @@ class DefaultFrontend(AbsFrontend):
apply_stft: bool = True,
use_channel: int = None,
):
assert check_argument_types()
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
@ -167,7 +165,6 @@ class MultiChannelFrontend(AbsFrontend):
cmvn_file: str = None,
mc: bool = True
):
assert check_argument_types()
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)

2
funasr/models/frontend/fused.py
View File

@ -3,7 +3,6 @@ from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.s3prl import S3prlFrontend
import numpy as np
import torch
from typeguard import check_argument_types
from typing import Tuple
@ -12,7 +11,6 @@ class FusedFrontends(AbsFrontend):
self, frontends=None, align_method="linear_projection", proj_dim=100, fs=16000
):
assert check_argument_types()
super().__init__()
self.align_method = (
align_method # fusing method : linear_projection only for now

2
funasr/models/frontend/s3prl.py
View File

@ -8,7 +8,6 @@ from typing import Union
import humanfriendly
import torch
from typeguard import check_argument_types
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.modules.frontends.frontend import Frontend
@ -37,7 +36,6 @@ class S3prlFrontend(AbsFrontend):
download_dir: str = None,
multilayer_feature: bool = False,
):
assert check_argument_types()
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)

4
funasr/models/frontend/wav_frontend.py
View File

@ -6,7 +6,6 @@ import numpy as np
import torch
import torchaudio.compliance.kaldi as kaldi
from torch.nn.utils.rnn import pad_sequence
from typeguard import check_argument_types
import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from funasr.models.frontend.abs_frontend import AbsFrontend
@ -95,7 +94,6 @@ class WavFrontend(AbsFrontend):
snip_edges: bool = True,
upsacle_samples: bool = True,
):
assert check_argument_types()
super().__init__()
self.fs = fs
self.window = window
@ -227,7 +225,6 @@ class WavFrontendOnline(AbsFrontend):
snip_edges: bool = True,
upsacle_samples: bool = True,
):
assert check_argument_types()
super().__init__()
self.fs = fs
self.window = window
@ -466,7 +463,6 @@ class WavFrontendMel23(AbsFrontend):
lfr_m: int = 1,
lfr_n: int = 1,
):
assert check_argument_types()
super().__init__()
self.fs = fs
self.frame_length = frame_length

2
funasr/models/frontend/windowing.py
View File

@ -6,7 +6,6 @@
from funasr.models.frontend.abs_frontend import AbsFrontend
import torch
from typeguard import check_argument_types
from typing import Tuple
@ -38,7 +37,6 @@ class SlidingWindow(AbsFrontend):
padding: Padding (placeholder, currently not implemented).
fs: Sampling rate (placeholder for compatibility, not used).
"""
assert check_argument_types()
super().__init__()
self.fs = fs
self.win_length = win_length

2
funasr/models/postencoder/hugging_face_transformers_postencoder.py
View File

@ -6,7 +6,6 @@
from funasr.modules.nets_utils import make_pad_mask
from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from typeguard import check_argument_types
from typing import Tuple
import copy
@ -30,7 +29,6 @@ class HuggingFaceTransformersPostEncoder(AbsPostEncoder):
model_name_or_path: str,
):
"""Initialize the module."""
assert check_argument_types()
super().__init__()
if not is_transformers_available:

2
funasr/models/preencoder/linear.py
View File

@ -5,7 +5,6 @@
"""Linear Projection."""
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from typeguard import check_argument_types
from typing import Tuple
import torch
@ -20,7 +19,6 @@ class LinearProjection(AbsPreEncoder):
output_size: int,
):
"""Initialize the module."""
assert check_argument_types()
super().__init__()
self.output_dim = output_size

3
funasr/models/preencoder/sinc.py
View File

@ -10,7 +10,6 @@ from funasr.layers.sinc_conv import LogCompression
from funasr.layers.sinc_conv import SincConv
import humanfriendly
import torch
from typeguard import check_argument_types
from typing import Optional
from typing import Tuple
from typing import Union
@ -60,7 +59,6 @@ class LightweightSincConvs(AbsPreEncoder):
windowing_type: Choice of windowing function.
scale_type: Choice of filter-bank initialization scale.
"""
assert check_argument_types()
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
@ -268,7 +266,6 @@ class SpatialDropout(torch.nn.Module):
dropout_probability: Dropout probability.
shape (tuple, list): Shape of input tensors.
"""
assert check_argument_types()
super().__init__()
if shape is None:
shape = (0, 2, 1)

2
funasr/models/seq_rnn_lm.py
View File

@ -4,7 +4,6 @@ from typing import Union
import torch
import torch.nn as nn
from typeguard import check_argument_types
from funasr.train.abs_model import AbsLM
@ -27,7 +26,6 @@ class SequentialRNNLM(AbsLM):
rnn_type: str = "lstm",
ignore_id: int = 0,
):
assert check_argument_types()
super().__init__()
ninp = unit

2
funasr/modules/eend_ola/utils/report.py
View File

@ -2,7 +2,7 @@ import copy
import numpy as np
import time
import torch
from eend.utils.power import create_powerlabel
from funasr.modules.eend_ola.utils.power import create_powerlabel
from itertools import combinations
metrics = [

2
funasr/optimizers/sgd.py
View File

@ -1,5 +1,4 @@
import torch
from typeguard import check_argument_types
class SGD(torch.optim.SGD):
@ -21,7 +20,6 @@ class SGD(torch.optim.SGD):
weight_decay: float = 0.0,
nesterov: bool = False,
):
assert check_argument_types()
super().__init__(
params,
lr=lr,

214
funasr/runtime/SDK_advanced.md Normal file
View File

@ -0,0 +1,214 @@
# FunASR离线文件转写服务开发指南
FunASR提供可一键本地或者云端服务器部署的中文离线文件转写服务，内核为FunASR已开源runtime-SDK。FunASR-runtime结合了达摩院语音实验室在Modelscope社区开源的语音端点检测(VAD)、Paraformer-large语音识别(ASR)、标点检测(PUNC) 等相关能力，可以准确、高效的对音频进行高并发转写。
本文档为FunASR离线文件转写服务开发指南。如果您想快速体验离线文件转写服务，请参考FunASR离线文件转写服务一键部署示例（[点击此处](./SDK_tutorial.md)）。
## Docker安装
下述步骤为手动安装docker及docker镜像的步骤，如您docker镜像已启动，可以忽略本步骤：
### docker环境安装
```shell
# Ubuntu：
curl -fsSL https://test.docker.com -o test-docker.sh
sudo sh test-docker.sh
# Debian：
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
# CentOS：
curl -fsSL https://get.docker.com | bash -s docker --mirror Aliyun
# MacOS：
brew install --cask --appdir=/Applications docker
```
安装详见：https://alibaba-damo-academy.github.io/FunASR/en/installation/docker.html
### docker启动
```shell
sudo systemctl start docker
```
### 镜像拉取及启动
通过下述命令拉取并启动FunASR runtime-SDK的docker镜像：
```shell
sudo docker pull registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.0.1
sudo docker run -p 10095:10095 -it --privileged=true -v /root:/workspace/models registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.0.1
```
```text
-p <宿主机端口>:<映射到docker端口>
如示例，宿主机(ecs)端口10095映射到docker端口10095上。前提是确保ecs安全规则打开了10095端口。
-v <宿主机路径>:<挂载至docker路径>
如示例，宿主机路径/root挂载至docker路径/workspace/models
```
## 服务端启动
docker启动之后，启动 funasr-wss-server服务程序：
funasr-wss-server支持从Modelscope下载模型，需要设置同时设置模型下载地址（--download-model-dir）及model ID（--model-dir、--vad-dir、--punc-dir）,示例如下：
```shell
cd /workspace/FunASR/funasr/runtime/websocket/build/bin
./funasr-wss-server \
--download-model-dir /workspace/models \
--model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
--vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
--punc-dir damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx \
--decoder-thread-num 32 \
--io-thread-num 8 \
--port 10095 \
--certfile ../../../ssl_key/server.crt \
--keyfile ../../../ssl_key/server.key
```
```text
--download-model-dir #模型下载地址，通过设置model ID从Modelscope下载模型
--model-dir # modelscope model ID
--quantize # True为量化ASR模型，False为非量化ASR模型，默认是True
--vad-dir # modelscope model ID
--vad-quant # True为量化VAD模型，False为非量化VAD模型，默认是True
--punc-dir # modelscope model ID
--punc-quant # True为量化PUNC模型，False为非量化PUNC模型，默认是True
--port # 服务端监听的端口号，默认为 8889
--decoder-thread-num # 服务端启动的推理线程数，默认为 8
--io-thread-num # 服务端启动的IO线程数，默认为 1
--certfile <string> # ssl的证书文件，默认为：../../../ssl_key/server.crt
--keyfile <string> # ssl的密钥文件，默认为：../../../ssl_key/server.key
```
funasr-wss-server同时也支持从本地路径加载模型（本地模型资源准备详见[模型资源准备](#anchor-1)），需要设置设置模型本地路径（--download-model-dir）示例如下：
```shell
cd /workspace/FunASR/funasr/runtime/websocket/build/bin
./funasr-wss-server \
--model-dir /workspace/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
--vad-dir /workspace/models/damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
--punc-dir /workspace/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-onnx \
--decoder-thread-num 32 \
--io-thread-num 8 \
--port 10095 \
--certfile ../../../ssl_key/server.crt \
--keyfile ../../../ssl_key/server.key
```
```text
--model-dir # ASR模型路径D，默认为：/workspace/models/asr
--quantize # True为量化ASR模型，False为非量化ASR模型，默认是True
--vad-dir # VAD模型路径，默认为：/workspace/models/vad
--vad-quant # True为量化VAD模型，False为非量化VAD模型，默认是True
--punc-dir # PUNC模型路径，默认为：/workspace/models/punc
--punc-quant # True为量化PUNC模型，False为非量化PUNC模型，默认是True
--port # 服务端监听的端口号，默认为 8889
--decoder-thread-num # 服务端启动的推理线程数，默认为 8
--io-thread-num # 服务端启动的IO线程数，默认为 1
--certfile <string> # ssl的证书文件，默认为：../../../ssl_key/server.crt
--keyfile <string> # ssl的密钥文件，默认为：../../../ssl_key/server.key
```
## <a id="anchor-1">模型资源准备</a>
如果您选择通过funasr-wss-server从Modelscope下载模型，可以跳过本步骤。
FunASR离线文件转写服务中的vad、asr和punc模型资源均来自Modelscope，模型地址详见下表：
| 模型 | Modelscope链接 |
|------|------------------------------------------------------------------------------------------------------------------|
| VAD | https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary |
| ASR | https://www.modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary |
| PUNC | https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary |
离线文件转写服务中部署的是量化后的ONNX模型，下面介绍下如何导出ONNX模型及其量化：您可以选择从Modelscope导出ONNX模型、从本地文件导出ONNX模型或者从finetune后的资源导出模型：
### 从Modelscope导出ONNX模型
从Modelscope网站下载对应model name的模型，然后导出量化后的ONNX模型：
```shell
python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx --quantize True
```
```text
--model-name Modelscope上的模型名称，例如damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
--export-dir ONNX模型导出地址
--type 模型类型，目前支持 ONNX、torch
--quantize int8模型量化
```
### 从本地文件导出ONNX模型
设置model name为模型本地路径，导出量化后的ONNX模型：
```shell
python -m funasr.export.export_model --model-name /workspace/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx --quantize True
```
```text
--model-name 模型本地路径，例如/workspace/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
--export-dir ONNX模型导出地址
--type 模型类型，目前支持 ONNX、torch
--quantize int8模型量化
```
### 从finetune后的资源导出模型
假如您想部署finetune后的模型，可以参考如下步骤：
将您finetune后需要部署的模型（例如10epoch.pb），重命名为model.pb，并将原modelscope中模型model.pb替换掉，假如替换后的模型路径为/path/to/finetune/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch，通过下述命令把finetune后的模型转成onnx模型：
```shell
python -m funasr.export.export_model --model-name /path/to/finetune/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx --quantize True
```
## 客户端启动
在服务器上完成FunASR离线文件转写服务部署以后，可以通过如下的步骤来测试和使用离线文件转写服务。目前FunASR-bin支持多种方式启动客户端，如下是基于python-client、c++-client的命令行实例及自定义客户端Websocket通信协议：
### python-client
```shell
python wss_client_asr.py --host "127.0.0.1" --port 10095 --mode offline --audio_in "./data/wav.scp" --send_without_sleep --output_dir "./results"
```
```text
--host # 服务端ip地址，本机测试可设置为 127.0.0.1
--port # 服务端监听端口号
--audio_in # 音频输入，输入可以是：wav路径 或者 wav.scp路径（kaldi格式的wav list，wav_id \t wav_path）
--output_dir # 识别结果输出路径
--ssl # 是否使用SSL加密，默认使用
--mode # offline模式
```
### c++-client：
```shell
. /funasr-wss-client --server-ip 127.0.0.1 --port 10095 --wav-path test.wav --thread-num 1 --is-ssl 1
```
```text
--server-ip # 服务端ip地址，本机测试可设置为 127.0.0.1
--port # 服务端监听端口号
--wav-path # 音频输入，输入可以是：wav路径 或者 wav.scp路径（kaldi格式的wav list，wav_id \t wav_path）
--thread-num # 客户端线程数
--is-ssl # 是否使用SSL加密，默认使用
```
### 自定义客户端：
如果您想定义自己的client，websocket通信协议为：
```text
# 首次通信
{"mode": "offline", "wav_name": wav_name, "is_speaking": True}
# 发送wav数据
bytes数据
# 发送结束标志
{"is_speaking": False}
```
## 如何定制服务部署
FunASR-runtime的代码已开源，如果服务端和客户端不能很好的满足您的需求，您可以根据自己的需求进行进一步的开发：
### c++ 客户端：
https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/websocket
###python 客户端：
https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/websocket
### c++ 服务端：
#### VAD
```c++
// VAD模型的使用分为FsmnVadInit和FsmnVadInfer两个步骤：
FUNASR_HANDLE vad_hanlde=FsmnVadInit(model_path, thread_num);
// 其中：model_path 包含"model-dir"、"quantize"，thread_num为onnx线程数；
FUNASR_RESULT result=FsmnVadInfer(vad_hanlde, wav_file.c_str(), NULL, 16000);
// 其中：vad_hanlde为FunOfflineInit返回值，wav_file为音频路径，sampling_rate为采样率(默认16k)
```
使用示例详见：https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline-vad.cpp
#### ASR
```text
// ASR模型的使用分为FunOfflineInit和FunOfflineInfer两个步骤：
FUNASR_HANDLE asr_hanlde=FunOfflineInit(model_path, thread_num);
// 其中：model_path 包含"model-dir"、"quantize"，thread_num为onnx线程数；
FUNASR_RESULT result=FunOfflineInfer(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL, 16000);
// 其中：asr_hanlde为FunOfflineInit返回值，wav_file为音频路径，sampling_rate为采样率(默认16k)
```
使用示例详见：https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline.cpp
#### PUNC
```text
// PUNC模型的使用分为CTTransformerInit和CTTransformerInfer两个步骤：
FUNASR_HANDLE punc_hanlde=CTTransformerInit(model_path, thread_num);
// 其中：model_path 包含"model-dir"、"quantize"，thread_num为onnx线程数；
FUNASR_RESULT result=CTTransformerInfer(punc_hanlde, txt_str.c_str(), RASR_NONE, NULL);
// 其中：punc_hanlde为CTTransformerInit返回值，txt_str为文本
```
使用示例详见：https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/onnxruntime/bin/funasr-onnx-offline-punc.cpp

2
funasr/runtime/python/libtorch/funasr_torch/utils/frontend.py
View File

@ -3,7 +3,6 @@ from pathlib import Path
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
import numpy as np
from typeguard import check_argument_types
import kaldi_native_fbank as knf
root_dir = Path(__file__).resolve().parent
@ -28,7 +27,6 @@ class WavFrontend():
dither: float = 1.0,
**kwargs,
) -> None:
check_argument_types()
opts = knf.FbankOptions()
opts.frame_opts.samp_freq = fs

3
funasr/runtime/python/libtorch/funasr_torch/utils/utils.py
View File

@ -9,7 +9,6 @@ from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
import numpy as np
import yaml
from typeguard import check_argument_types
import warnings
@ -21,7 +20,6 @@ logger_initialized = {}
class TokenIDConverter():
def __init__(self, token_list: Union[List, str],
):
check_argument_types()
self.token_list = token_list
self.unk_symbol = token_list[-1]
@ -51,7 +49,6 @@ class CharTokenizer():
space_symbol: str = "<space>",
remove_non_linguistic_symbols: bool = False,
):
check_argument_types()
self.space_symbol = space_symbol
self.non_linguistic_symbols = self.load_symbols(symbol_value)

2
funasr/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
View File

@ -4,7 +4,6 @@ from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
import copy
import numpy as np
from typeguard import check_argument_types
import kaldi_native_fbank as knf
root_dir = Path(__file__).resolve().parent
@ -29,7 +28,6 @@ class WavFrontend():
dither: float = 1.0,
**kwargs,
) -> None:
check_argument_types()
opts = knf.FbankOptions()
opts.frame_opts.samp_freq = fs

3
funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
View File

@ -10,7 +10,6 @@ import numpy as np
import yaml
from onnxruntime import (GraphOptimizationLevel, InferenceSession,
SessionOptions, get_available_providers, get_device)
from typeguard import check_argument_types
import warnings
@ -22,7 +21,6 @@ logger_initialized = {}
class TokenIDConverter():
def __init__(self, token_list: Union[List, str],
):
check_argument_types()
self.token_list = token_list
self.unk_symbol = token_list[-1]
@ -52,7 +50,6 @@ class CharTokenizer():
space_symbol: str = "<space>",
remove_non_linguistic_symbols: bool = False,
):
check_argument_types()
self.space_symbol = space_symbol
self.non_linguistic_symbols = self.load_symbols(symbol_value)

1
funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py
View File

@ -109,7 +109,6 @@ class WavFrontend():
lfr_n: int = 6,
dither: float = 1.0
) -> None:
# check_argument_types()
self.fs = fs
self.window = window

4
funasr/samplers/build_batch_sampler.py
View File

@ -4,8 +4,6 @@ from typing import Sequence
from typing import Tuple
from typing import Union
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr.samplers.abs_sampler import AbsSampler
from funasr.samplers.folded_batch_sampler import FoldedBatchSampler
@ -104,7 +102,6 @@ def build_batch_sampler(
padding: Whether sequences are input as a padded tensor or not.
used for "numel" mode
"""
assert check_argument_types()
if len(shape_files) == 0:
raise ValueError("No shape file are given")
@ -164,5 +161,4 @@ def build_batch_sampler(
else:
raise ValueError(f"Not supported: {type}")
assert check_return_type(retval)
return retval

2
funasr/samplers/folded_batch_sampler.py
View File

@ -4,7 +4,6 @@ from typing import Sequence
from typing import Tuple
from typing import Union
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
from funasr.fileio.read_text import read_2column_text
@ -23,7 +22,6 @@ class FoldedBatchSampler(AbsSampler):
drop_last: bool = False,
utt2category_file: str = None,
):
assert check_argument_types()
assert batch_size > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(

2
funasr/samplers/length_batch_sampler.py
View File

@ -4,7 +4,6 @@ from typing import Dict
from typing import Tuple
from typing import Union
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
from funasr.samplers.abs_sampler import AbsSampler
@ -21,7 +20,6 @@ class LengthBatchSampler(AbsSampler):
drop_last: bool = False,
padding: bool = True,
):
assert check_argument_types()
assert batch_bins > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(

2
funasr/samplers/num_elements_batch_sampler.py
View File

@ -4,7 +4,6 @@ from typing import Tuple
from typing import Union
import numpy as np
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
from funasr.samplers.abs_sampler import AbsSampler
@ -21,7 +20,6 @@ class NumElementsBatchSampler(AbsSampler):
drop_last: bool = False,
padding: bool = True,
):
assert check_argument_types()
assert batch_bins > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(

2
funasr/samplers/sorted_batch_sampler.py
View File

@ -2,7 +2,6 @@ import logging
from typing import Iterator
from typing import Tuple
from typeguard import check_argument_types
from funasr.fileio.read_text import load_num_sequence_text
from funasr.samplers.abs_sampler import AbsSampler
@ -26,7 +25,6 @@ class SortedBatchSampler(AbsSampler):
sort_batch: str = "ascending",
drop_last: bool = False,
):
assert check_argument_types()
assert batch_size > 0
self.batch_size = batch_size
self.shape_file = shape_file

2
funasr/samplers/unsorted_batch_sampler.py
View File

@ -2,7 +2,6 @@ import logging
from typing import Iterator
from typing import Tuple
from typeguard import check_argument_types
from funasr.fileio.read_text import read_2column_text
from funasr.samplers.abs_sampler import AbsSampler
@ -28,7 +27,6 @@ class UnsortedBatchSampler(AbsSampler):
drop_last: bool = False,
utt2category_file: str = None,
):
assert check_argument_types()
assert batch_size > 0
self.batch_size = batch_size
self.key_file = key_file

2
funasr/schedulers/noam_lr.py
View File

@ -4,7 +4,6 @@ import warnings
import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types
from funasr.schedulers.abs_scheduler import AbsBatchStepScheduler
@ -31,7 +30,6 @@ class NoamLR(_LRScheduler, AbsBatchStepScheduler):
warmup_steps: Union[int, float] = 25000,
last_epoch: int = -1,
):
assert check_argument_types()
self.model_size = model_size
self.warmup_steps = warmup_steps

2
funasr/schedulers/tri_stage_scheduler.py
View File

@ -8,7 +8,6 @@ from typing import Optional, List
import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types
from funasr.schedulers.abs_scheduler import AbsBatchStepScheduler
@ -22,7 +21,6 @@ class TriStageLR(_LRScheduler, AbsBatchStepScheduler):
init_lr_scale: float = 0.01,
final_lr_scale: float = 0.01,
):
assert check_argument_types()
self.optimizer = optimizer
self.last_epoch = last_epoch
self.phase_ratio = phase_ratio

2
funasr/schedulers/warmup_lr.py
View File

@ -3,7 +3,6 @@ from typing import Union
import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types
from funasr.schedulers.abs_scheduler import AbsBatchStepScheduler
@ -30,7 +29,6 @@ class WarmupLR(_LRScheduler, AbsBatchStepScheduler):
warmup_steps: Union[int, float] = 25000,
last_epoch: int = -1,
):
assert check_argument_types()
self.warmup_steps = warmup_steps
# __init__() must be invoked before setting field

16
funasr/tasks/abs_task.py
View File

@ -32,8 +32,6 @@ import torch.optim
import yaml
from funasr.models.base_model import FunASRModel
from torch.utils.data import DataLoader
from typeguard import check_argument_types
from typeguard import check_return_type
from funasr import __version__
from funasr.datasets.dataset import AbsDataset
@ -269,7 +267,6 @@ class AbsTask(ABC):
@classmethod
def get_parser(cls) -> config_argparse.ArgumentParser:
assert check_argument_types()
class ArgumentDefaultsRawTextHelpFormatter(
argparse.RawTextHelpFormatter,
@ -959,7 +956,6 @@ class AbsTask(ABC):
cls.trainer.add_arguments(parser)
cls.add_task_arguments(parser)
assert check_return_type(parser)
return parser
@classmethod
@ -1007,7 +1003,6 @@ class AbsTask(ABC):
return _cls
# This method is used only for --print_config
assert check_argument_types()
parser = cls.get_parser()
args, _ = parser.parse_known_args()
config = vars(args)
@ -1047,7 +1042,6 @@ class AbsTask(ABC):
@classmethod
def check_required_command_args(cls, args: argparse.Namespace):
assert check_argument_types()
if hasattr(args, "required"):
for k in vars(args):
if "-" in k:
@ -1077,7 +1071,6 @@ class AbsTask(ABC):
inference: bool = False,
) -> None:
"""Check if the dataset satisfy the requirement of current Task"""
assert check_argument_types()
mes = (
f"If you intend to use an additional input, modify "
f'"{cls.__name__}.required_data_names()" or '
@ -1104,14 +1097,12 @@ class AbsTask(ABC):
@classmethod
def print_config(cls, file=sys.stdout) -> None:
assert check_argument_types()
# Shows the config: e.g. python train.py asr --print_config
config = cls.get_default_config()
file.write(yaml_no_alias_safe_dump(config, indent=4, sort_keys=False))
@classmethod
def main(cls, args: argparse.Namespace = None, cmd: Sequence[str] = None):
assert check_argument_types()
print(get_commandline_args(), file=sys.stderr)
if args is None:
parser = cls.get_parser()
@ -1148,7 +1139,6 @@ class AbsTask(ABC):
@classmethod
def main_worker(cls, args: argparse.Namespace):
assert check_argument_types()
# 0. Init distributed process
distributed_option = build_dataclass(DistributedOption, args)
@ -1556,7 +1546,6 @@ class AbsTask(ABC):
- 4 epoch with "--num_iters_per_epoch" == 4
"""
assert check_argument_types()
iter_options = cls.build_iter_options(args, distributed_option, mode)
# Overwrite iter_options if any kwargs is given
@ -1589,7 +1578,6 @@ class AbsTask(ABC):
def build_sequence_iter_factory(
cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
) -> AbsIterFactory:
assert check_argument_types()
if hasattr(args, "frontend_conf"):
if args.frontend_conf is not None and "fs" in args.frontend_conf:
@ -1683,7 +1671,6 @@ class AbsTask(ABC):
iter_options: IteratorOptions,
mode: str,
) -> AbsIterFactory:
assert check_argument_types()
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
@ -1788,7 +1775,6 @@ class AbsTask(ABC):
def build_multiple_iter_factory(
cls, args: argparse.Namespace, distributed_option: DistributedOption, mode: str
):
assert check_argument_types()
iter_options = cls.build_iter_options(args, distributed_option, mode)
assert len(iter_options.data_path_and_name_and_type) > 0, len(
iter_options.data_path_and_name_and_type
@ -1885,7 +1871,6 @@ class AbsTask(ABC):
inference: bool = False,
) -> DataLoader:
"""Build DataLoader using iterable dataset"""
assert check_argument_types()
# For backward compatibility for pytorch DataLoader
if collate_fn is not None:
kwargs = dict(collate_fn=collate_fn)
@ -1935,7 +1920,6 @@ class AbsTask(ABC):
device: Device type, "cpu", "cuda", or "cuda:N".
"""
assert check_argument_types()
if config_file is None:
assert model_file is not None, (
"The argument 'model_file' must be provided "

Some files were not shown because too many files have changed in this diff Show More