#!/usr/bin/env python3
|
# -*- encoding: utf-8 -*-
|
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
# MIT License (https://opensource.org/licenses/MIT)
|
|
|
import argparse
|
import logging
|
import os
|
import sys
|
from typing import Optional
|
from typing import Union
|
|
import numpy as np
|
import torch
|
|
from funasr.bin.tp_infer import Speech2Timestamp
|
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
|
from funasr.datasets.preprocessor import LMPreprocessor
|
from funasr.fileio.datadir_writer import DatadirWriter
|
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.timestamp_tools import ts_prediction_lfr6_standard
|
from funasr.utils.types import str2bool
|
from funasr.utils.types import str2triple_str
|
from funasr.utils.types import str_or_none
|
|
|
def inference_tp(
|
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,
|
):
|
ncpu = kwargs.get("ncpu", 1)
|
torch.set_num_threads(ncpu)
|
|
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 = Speech2Timestamp(**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,
|
)
|
|
if output_dir is not None:
|
writer = DatadirWriter(output_dir)
|
tp_writer = writer[f"timestamp_prediction"]
|
# ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
|
else:
|
tp_writer = None
|
|
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
|
):
|
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
|
writer = None
|
if output_path is not None:
|
writer = DatadirWriter(output_path)
|
tp_writer = writer[f"timestamp_prediction"]
|
else:
|
tp_writer = None
|
# 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 = build_streaming_iterator(
|
task_name="asr",
|
preprocess_args=speechtext2timestamp.tp_train_args,
|
data_path_and_name_and_type=data_path_and_name_and_type,
|
dtype=dtype,
|
batch_size=batch_size,
|
key_file=key_file,
|
num_workers=num_workers,
|
preprocess_fn=preprocessor,
|
)
|
|
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}
|
if tp_writer is not None:
|
tp_writer["tp_sync"][key + '#'] = ts_str
|
tp_writer["tp_time"][key + '#'] = str(ts_list)
|
tp_result_list.append(item)
|
return tp_result_list
|
|
return _forward
|
|
|
def inference_launch(mode, **kwargs):
|
if mode == "tp_norm":
|
return inference_tp(**kwargs)
|
else:
|
logging.info("Unknown decoding mode: {}".format(mode))
|
return None
|
|
|
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(
|
"--njob",
|
type=int,
|
default=1,
|
help="The number of jobs for each gpu",
|
)
|
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=1,
|
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=True,
|
action="append",
|
)
|
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("The inference configuration related")
|
group.add_argument(
|
"--batch_size",
|
type=int,
|
default=1,
|
help="The batch size for inference",
|
)
|
return parser
|
|
|
def main(cmd=None):
|
print(get_commandline_args(), file=sys.stderr)
|
parser = get_parser()
|
parser.add_argument(
|
"--mode",
|
type=str,
|
default="tp_norm",
|
help="The decoding mode",
|
)
|
args = parser.parse_args(cmd)
|
kwargs = vars(args)
|
kwargs.pop("config", None)
|
|
# set logging messages
|
logging.basicConfig(
|
level=args.log_level,
|
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
|
)
|
logging.info("Decoding args: {}".format(kwargs))
|
|
# gpu setting
|
if args.ngpu > 0:
|
jobid = int(args.output_dir.split(".")[-1])
|
gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
|
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
|
|
inference_pipeline = inference_launch(**kwargs)
|
return inference_pipeline(kwargs["data_path_and_name_and_type"])
|
|
|
if __name__ == "__main__":
|
main()
|
