#!/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()