import argparse
|
import logging
|
from optparse import Option
|
import sys
|
import json
|
from pathlib import Path
|
from typing import Any
|
from typing import List
|
from typing import Optional
|
from typing import Sequence
|
from typing import Tuple
|
from typing import Union
|
from typing import Dict
|
|
import numpy as np
|
import torch
|
from typeguard import check_argument_types
|
|
from funasr.fileio.datadir_writer import DatadirWriter
|
from funasr.datasets.preprocessor import LMPreprocessor
|
from funasr.tasks.asr import ASRTaskAligner as ASRTask
|
from funasr.torch_utils.device_funcs import to_device
|
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
|
from funasr.utils import config_argparse
|
from funasr.utils.cli_utils import get_commandline_args
|
from funasr.utils.types import str2bool
|
from funasr.utils.types import str2triple_str
|
from funasr.utils.types import str_or_none
|
from funasr.models.frontend.wav_frontend import WavFrontend
|
from funasr.text.token_id_converter import TokenIDConverter
|
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
|
|
|
header_colors = '\033[95m'
|
end_colors = '\033[0m'
|
|
global_asr_language: str = 'zh-cn'
|
global_sample_rate: Union[int, Dict[Any, int]] = {
|
'audio_fs': 16000,
|
'model_fs': 16000
|
}
|
|
|
class SpeechText2Timestamp:
|
def __init__(
|
self,
|
timestamp_infer_config: Union[Path, str] = None,
|
timestamp_model_file: Union[Path, str] = None,
|
timestamp_cmvn_file: Union[Path, str] = None,
|
device: str = "cpu",
|
dtype: str = "float32",
|
**kwargs,
|
):
|
assert check_argument_types()
|
# 1. Build ASR model
|
tp_model, tp_train_args = ASRTask.build_model_from_file(
|
timestamp_infer_config, timestamp_model_file, device
|
)
|
if 'cuda' in device:
|
tp_model = tp_model.cuda() # force model to cuda
|
|
frontend = None
|
if tp_train_args.frontend is not None:
|
frontend = WavFrontend(cmvn_file=timestamp_cmvn_file, **tp_train_args.frontend_conf)
|
|
logging.info("tp_model: {}".format(tp_model))
|
logging.info("tp_train_args: {}".format(tp_train_args))
|
tp_model.to(dtype=getattr(torch, dtype)).eval()
|
|
logging.info(f"Decoding device={device}, dtype={dtype}")
|
|
|
self.tp_model = tp_model
|
self.tp_train_args = tp_train_args
|
|
token_list = self.tp_model.token_list
|
self.converter = TokenIDConverter(token_list=token_list)
|
|
self.device = device
|
self.dtype = dtype
|
self.frontend = frontend
|
self.encoder_downsampling_factor = 1
|
if tp_train_args.encoder_conf["input_layer"] == "conv2d":
|
self.encoder_downsampling_factor = 4
|
|
@torch.no_grad()
|
def __call__(
|
self,
|
speech: Union[torch.Tensor, np.ndarray],
|
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):
|
speech = torch.tensor(speech)
|
if self.frontend is not None:
|
feats, feats_len = self.frontend.forward(speech, speech_lengths)
|
feats = to_device(feats, device=self.device)
|
feats_len = feats_len.int()
|
self.tp_model.frontend = None
|
else:
|
feats = speech
|
feats_len = speech_lengths
|
|
# lfr_factor = max(1, (feats.size()[-1]//80)-1)
|
batch = {"speech": feats, "speech_lengths": feats_len}
|
|
# a. To device
|
batch = to_device(batch, device=self.device)
|
|
# b. Forward Encoder
|
enc, enc_len = self.tp_model.encode(**batch)
|
if isinstance(enc, tuple):
|
enc = enc[0]
|
|
# c. Forward Predictor
|
_, _, us_alphas, us_cif_peak = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
|
return us_alphas, us_cif_peak
|
|
|
def inference(
|
batch_size: int,
|
ngpu: int,
|
log_level: Union[int, str],
|
data_path_and_name_and_type,
|
timestamp_infer_config: Optional[str],
|
timestamp_model_file: Optional[str],
|
timestamp_cmvn_file: Optional[str] = None,
|
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
key_file: Optional[str] = None,
|
allow_variable_data_keys: bool = False,
|
output_dir: Optional[str] = None,
|
dtype: str = "float32",
|
seed: int = 0,
|
num_workers: int = 1,
|
split_with_space: bool = True,
|
seg_dict_file: Optional[str] = None,
|
**kwargs,
|
):
|
inference_pipeline = inference_modelscope(
|
batch_size=batch_size,
|
ngpu=ngpu,
|
log_level=log_level,
|
timestamp_infer_config=timestamp_infer_config,
|
timestamp_model_file=timestamp_model_file,
|
timestamp_cmvn_file=timestamp_cmvn_file,
|
key_file=key_file,
|
allow_variable_data_keys=allow_variable_data_keys,
|
output_dir=output_dir,
|
dtype=dtype,
|
seed=seed,
|
num_workers=num_workers,
|
split_with_space=split_with_space,
|
seg_dict_file=seg_dict_file,
|
**kwargs,
|
)
|
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
|
|
|
def inference_modelscope(
|
batch_size: int,
|
ngpu: int,
|
log_level: Union[int, str],
|
# data_path_and_name_and_type,
|
timestamp_infer_config: Optional[str],
|
timestamp_model_file: Optional[str],
|
timestamp_cmvn_file: Optional[str] = None,
|
# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
key_file: Optional[str] = None,
|
allow_variable_data_keys: bool = False,
|
output_dir: Optional[str] = None,
|
dtype: str = "float32",
|
seed: int = 0,
|
num_workers: int = 1,
|
split_with_space: bool = True,
|
seg_dict_file: Optional[str] = None,
|
**kwargs,
|
):
|
assert check_argument_types()
|
if batch_size > 1:
|
raise NotImplementedError("batch decoding is not implemented")
|
if ngpu > 1:
|
raise NotImplementedError("only single GPU decoding is supported")
|
|
logging.basicConfig(
|
level=log_level,
|
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
|
)
|
|
if ngpu >= 1 and torch.cuda.is_available():
|
device = "cuda"
|
else:
|
device = "cpu"
|
# 1. Set random-seed
|
set_all_random_seed(seed)
|
|
# 2. Build speech2vadsegment
|
speechtext2timestamp_kwargs = dict(
|
timestamp_infer_config=timestamp_infer_config,
|
timestamp_model_file=timestamp_model_file,
|
timestamp_cmvn_file=timestamp_cmvn_file,
|
device=device,
|
dtype=dtype,
|
)
|
logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
|
speechtext2timestamp = SpeechText2Timestamp(**speechtext2timestamp_kwargs)
|
|
preprocessor = LMPreprocessor(
|
train=False,
|
token_type=speechtext2timestamp.tp_train_args.token_type,
|
token_list=speechtext2timestamp.tp_train_args.token_list,
|
bpemodel=None,
|
text_cleaner=None,
|
g2p_type=None,
|
text_name="text",
|
non_linguistic_symbols=speechtext2timestamp.tp_train_args.non_linguistic_symbols,
|
split_with_space=split_with_space,
|
seg_dict_file=seg_dict_file,
|
)
|
|
def _forward(
|
data_path_and_name_and_type,
|
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
output_dir_v2: Optional[str] = None,
|
fs: dict = None,
|
param_dict: dict = None,
|
**kwargs
|
):
|
# 3. Build data-iterator
|
if data_path_and_name_and_type is None and raw_inputs is not None:
|
if isinstance(raw_inputs, torch.Tensor):
|
raw_inputs = raw_inputs.numpy()
|
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
|
|
loader = ASRTask.build_streaming_iterator(
|
data_path_and_name_and_type,
|
dtype=dtype,
|
batch_size=batch_size,
|
key_file=key_file,
|
num_workers=num_workers,
|
preprocess_fn=preprocessor,
|
collate_fn=ASRTask.build_collate_fn(speechtext2timestamp.tp_train_args, False),
|
allow_variable_data_keys=allow_variable_data_keys,
|
inference=True,
|
)
|
|
tp_result_list = []
|
for keys, batch in loader:
|
assert isinstance(batch, dict), type(batch)
|
assert all(isinstance(s, str) for s in keys), keys
|
_bs = len(next(iter(batch.values())))
|
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
|
|
logging.info("timestamp predicting, utt_id: {}".format(keys))
|
_batch = {'speech':batch['speech'],
|
'speech_lengths':batch['speech_lengths'],
|
'text_lengths':batch['text_lengths']}
|
us_alphas, us_cif_peak = speechtext2timestamp(**_batch)
|
|
for batch_id in range(_bs):
|
key = keys[batch_id]
|
token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
|
ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token, force_time_shift=-3.0)
|
logging.warning(ts_str)
|
item = {'key': key, 'value': ts_str, 'timestamp':ts_list}
|
tp_result_list.append(item)
|
return tp_result_list
|
|
return _forward
|
|
|
def get_parser():
|
parser = config_argparse.ArgumentParser(
|
description="Timestamp Prediction Inference",
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
)
|
|
# Note(kamo): Use '_' instead of '-' as separator.
|
# '-' is confusing if written in yaml.
|
parser.add_argument(
|
"--log_level",
|
type=lambda x: x.upper(),
|
default="INFO",
|
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
|
help="The verbose level of logging",
|
)
|
|
parser.add_argument("--output_dir", type=str, required=False)
|
parser.add_argument(
|
"--ngpu",
|
type=int,
|
default=0,
|
help="The number of gpus. 0 indicates CPU mode",
|
)
|
parser.add_argument(
|
"--gpuid_list",
|
type=str,
|
default="",
|
help="The visible gpus",
|
)
|
parser.add_argument("--seed", type=int, default=0, help="Random seed")
|
parser.add_argument(
|
"--dtype",
|
default="float32",
|
choices=["float16", "float32", "float64"],
|
help="Data type",
|
)
|
parser.add_argument(
|
"--num_workers",
|
type=int,
|
default=0,
|
help="The number of workers used for DataLoader",
|
)
|
|
group = parser.add_argument_group("Input data related")
|
group.add_argument(
|
"--data_path_and_name_and_type",
|
type=str2triple_str,
|
required=False,
|
action="append",
|
)
|
group.add_argument("--raw_inputs", type=list, default=None)
|
# example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])
|
group.add_argument("--key_file", type=str_or_none)
|
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
|
|
group = parser.add_argument_group("The model configuration related")
|
group.add_argument(
|
"--timestamp_infer_config",
|
type=str,
|
help="VAD infer configuration",
|
)
|
group.add_argument(
|
"--timestamp_model_file",
|
type=str,
|
help="VAD model parameter file",
|
)
|
group.add_argument(
|
"--timestamp_cmvn_file",
|
type=str,
|
help="Global cmvn file",
|
)
|
|
group = parser.add_argument_group("infer related")
|
group.add_argument(
|
"--batch_size",
|
type=int,
|
default=1,
|
help="The batch size for inference",
|
)
|
group.add_argument(
|
"--seg_dict_file",
|
type=str,
|
default=None,
|
help="The batch size for inference",
|
)
|
group.add_argument(
|
"--split_with_space",
|
type=bool,
|
default=False,
|
help="The batch size for inference",
|
)
|
|
return parser
|
|
|
def main(cmd=None):
|
print(get_commandline_args(), file=sys.stderr)
|
parser = get_parser()
|
args = parser.parse_args(cmd)
|
kwargs = vars(args)
|
kwargs.pop("config", None)
|
inference(**kwargs)
|
|
|
if __name__ == "__main__":
|
main()
|
