|
|
@@ -0,0 +1,432 @@
|
|
|
+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
|
|
|
+
|
|
|
+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
|
|
|
+}
|
|
|
+
|
|
|
+def time_stamp_lfr6_advance(us_alphas, us_cif_peak, char_list):
|
|
|
+ START_END_THRESHOLD = 5
|
|
|
+ MAX_TOKEN_DURATION = 12
|
|
|
+ TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
|
|
|
+ if len(us_cif_peak.shape) == 2:
|
|
|
+ alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
|
|
|
+ else:
|
|
|
+ alphas, cif_peak = us_alphas, us_cif_peak
|
|
|
+ num_frames = cif_peak.shape[0]
|
|
|
+ if char_list[-1] == '</s>':
|
|
|
+ char_list = char_list[:-1]
|
|
|
+ # char_list = [i for i in text]
|
|
|
+ timestamp_list = []
|
|
|
+ new_char_list = []
|
|
|
+ # for bicif model trained with large data, cif2 actually fires when a character starts
|
|
|
+ # so treat the frames between two peaks as the duration of the former token
|
|
|
+ fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 3.2 # total offset
|
|
|
+ num_peak = len(fire_place)
|
|
|
+ assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
|
|
|
+ # begin silence
|
|
|
+ if fire_place[0] > START_END_THRESHOLD:
|
|
|
+ # char_list.insert(0, '<sil>')
|
|
|
+ timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
|
|
|
+ new_char_list.append('<sil>')
|
|
|
+ # tokens timestamp
|
|
|
+ for i in range(len(fire_place)-1):
|
|
|
+ new_char_list.append(char_list[i])
|
|
|
+ if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
|
|
|
+ timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
|
|
|
+ else:
|
|
|
+ # cut the duration to token and sil of the 0-weight frames last long
|
|
|
+ _split = fire_place[i] + MAX_TOKEN_DURATION
|
|
|
+ timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
|
|
|
+ timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
|
|
|
+ new_char_list.append('<sil>')
|
|
|
+ # tail token and end silence
|
|
|
+ # new_char_list.append(char_list[-1])
|
|
|
+ if num_frames - fire_place[-1] > START_END_THRESHOLD:
|
|
|
+ _end = (num_frames + fire_place[-1]) * 0.5
|
|
|
+ # _end = fire_place[-1]
|
|
|
+ timestamp_list[-1][1] = _end*TIME_RATE
|
|
|
+ timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
|
|
|
+ new_char_list.append("<sil>")
|
|
|
+ else:
|
|
|
+ timestamp_list[-1][1] = num_frames*TIME_RATE
|
|
|
+ assert len(new_char_list) == len(timestamp_list)
|
|
|
+ res_str = ""
|
|
|
+ for char, timestamp in zip(new_char_list, timestamp_list):
|
|
|
+ res_str += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
|
|
|
+ res = []
|
|
|
+ for char, timestamp in zip(new_char_list, timestamp_list):
|
|
|
+ if char != '<sil>':
|
|
|
+ res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
|
|
|
+ return res_str, res
|
|
|
+
|
|
|
+
|
|
|
+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 = time_stamp_lfr6_advance(us_alphas[batch_id], us_cif_peak[batch_id], token)
|
|
|
+ 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()
|
Xian Shi