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| | | #!/usr/bin/env python3 |
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| | | # !/usr/bin/env python3 |
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| | | # -*- encoding: utf-8 -*- |
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| | | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. |
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| | | # MIT License (https://opensource.org/licenses/MIT) |
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| | | |
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| | | import argparse |
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| | | import logging |
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| | | import os |
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| | | import sys |
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| | | from typing import Union, Dict, Any |
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| | | from typing import List |
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| | | from typing import Optional |
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| | | from typing import Sequence |
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| | | from typing import Tuple |
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| | | from typing import Union |
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| | | |
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| | | import numpy as np |
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| | | import soundfile |
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| | | import torch |
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| | | from scipy.signal import medfilt |
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| | | |
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| | | from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND |
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| | | from funasr.datasets.iterable_dataset import load_bytes |
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| | | from funasr.build_utils.build_streaming_iterator import build_streaming_iterator |
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| | | from funasr.torch_utils.set_all_random_seed import set_all_random_seed |
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| | | from funasr.utils import config_argparse |
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| | | from funasr.utils.cli_utils import get_commandline_args |
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| | | from funasr.utils.types import str2bool |
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| | | from funasr.utils.types import str2triple_str |
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| | | from funasr.utils.types import str_or_none |
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| | | |
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| | | def inference_sond( |
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| | | diar_train_config: str, |
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| | | diar_model_file: str, |
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| | | output_dir: Optional[str] = None, |
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| | | batch_size: int = 1, |
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| | | dtype: str = "float32", |
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| | | ngpu: int = 0, |
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| | | seed: int = 0, |
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| | | num_workers: int = 0, |
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| | | log_level: Union[int, str] = "INFO", |
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| | | key_file: Optional[str] = None, |
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| | | model_tag: Optional[str] = None, |
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| | | allow_variable_data_keys: bool = True, |
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| | | streaming: bool = False, |
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| | | smooth_size: int = 83, |
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| | | dur_threshold: int = 10, |
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| | | out_format: str = "vad", |
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| | | param_dict: Optional[dict] = None, |
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| | | mode: str = "sond", |
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| | | **kwargs, |
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| | | ): |
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| | | ncpu = kwargs.get("ncpu", 1) |
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| | | torch.set_num_threads(ncpu) |
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| | | if batch_size > 1: |
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| | | raise NotImplementedError("batch decoding is not implemented") |
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| | | if ngpu > 1: |
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| | | raise NotImplementedError("only single GPU decoding is supported") |
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| | | |
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| | | logging.basicConfig( |
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| | | level=log_level, |
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| | | format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", |
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| | | ) |
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| | | logging.info("param_dict: {}".format(param_dict)) |
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| | | |
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| | | if ngpu >= 1 and torch.cuda.is_available(): |
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| | | device = "cuda" |
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| | | else: |
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| | | device = "cpu" |
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| | | |
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| | | # 1. Set random-seed |
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| | | set_all_random_seed(seed) |
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| | | |
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| | | # 2a. Build speech2xvec [Optional] |
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| | | if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict[ |
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| | | "extract_profile"]: |
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| | | assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict." |
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| | | assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict." |
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| | | sv_train_config = param_dict["sv_train_config"] |
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| | | sv_model_file = param_dict["sv_model_file"] |
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| | | if "model_dir" in param_dict: |
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| | | sv_train_config = os.path.join(param_dict["model_dir"], sv_train_config) |
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| | | sv_model_file = os.path.join(param_dict["model_dir"], sv_model_file) |
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| | | from funasr.bin.sv_infer import Speech2Xvector |
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| | | speech2xvector_kwargs = dict( |
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| | | sv_train_config=sv_train_config, |
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| | | sv_model_file=sv_model_file, |
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| | | device=device, |
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| | | dtype=dtype, |
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| | | streaming=streaming, |
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| | | embedding_node="resnet1_dense" |
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| | | ) |
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| | | logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs)) |
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| | | speech2xvector = Speech2Xvector(**speech2xvector_kwargs) |
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| | | speech2xvector.sv_model.eval() |
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| | | |
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| | | # 2b. Build speech2diar |
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| | | speech2diar_kwargs = dict( |
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| | | diar_train_config=diar_train_config, |
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| | | diar_model_file=diar_model_file, |
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| | | device=device, |
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| | | dtype=dtype, |
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| | | streaming=streaming, |
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| | | smooth_size=smooth_size, |
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| | | dur_threshold=dur_threshold, |
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| | | ) |
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| | | logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs)) |
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| | | speech2diar = Speech2DiarizationSOND(**speech2diar_kwargs) |
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| | | speech2diar.diar_model.eval() |
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| | | |
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| | | def output_results_str(results: dict, uttid: str): |
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| | | rst = [] |
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| | | mid = uttid.rsplit("-", 1)[0] |
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| | | for key in results: |
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| | | results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]] |
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| | | if out_format == "vad": |
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| | | for spk, segs in results.items(): |
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| | | rst.append("{} {}".format(spk, segs)) |
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| | | else: |
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| | | template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>" |
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| | | for spk, segs in results.items(): |
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| | | rst.extend([template.format(mid, st, ed, spk) for st, ed in segs]) |
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| | | |
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| | | return "\n".join(rst) |
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| | | |
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| | | def _forward( |
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| | | data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None, |
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| | | raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None, |
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| | | output_dir_v2: Optional[str] = None, |
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| | | param_dict: Optional[dict] = None, |
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| | | ): |
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| | | logging.info("param_dict: {}".format(param_dict)) |
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| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
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| | | if isinstance(raw_inputs, (list, tuple)): |
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| | | if not isinstance(raw_inputs[0], List): |
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| | | raw_inputs = [raw_inputs] |
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| | | |
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| | | assert all([len(example) >= 2 for example in raw_inputs]), \ |
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| | | "The length of test case in raw_inputs must larger than 1 (>=2)." |
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| | | |
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| | | def prepare_dataset(): |
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| | | for idx, example in enumerate(raw_inputs): |
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| | | # read waveform file |
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| | | example = [load_bytes(x) if isinstance(x, bytes) else x |
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| | | for x in example] |
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| | | example = [soundfile.read(x)[0] if isinstance(x, str) else x |
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| | | for x in example] |
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| | | # convert torch tensor to numpy array |
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| | | example = [x.numpy() if isinstance(example[0], torch.Tensor) else x |
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| | | for x in example] |
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| | | speech = example[0] |
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| | | logging.info("Extracting profiles for {} waveforms".format(len(example) - 1)) |
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| | | profile = [speech2xvector.calculate_embedding(x) for x in example[1:]] |
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| | | profile = torch.cat(profile, dim=0) |
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| | | yield ["test{}".format(idx)], {"speech": [speech], "profile": [profile]} |
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| | | |
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| | | loader = prepare_dataset() |
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| | | else: |
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| | | raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ") |
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| | | else: |
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| | | # 3. Build data-iterator |
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| | | loader = build_streaming_iterator( |
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| | | task_name="diar", |
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| | | preprocess_args=None, |
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| | | data_path_and_name_and_type=data_path_and_name_and_type, |
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| | | dtype=dtype, |
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| | | batch_size=batch_size, |
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| | | key_file=key_file, |
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| | | num_workers=num_workers, |
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| | | use_collate_fn=False, |
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| | | ) |
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| | | |
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| | | # 7. Start for-loop |
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| | | output_path = output_dir_v2 if output_dir_v2 is not None else output_dir |
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| | | if output_path is not None: |
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| | | os.makedirs(output_path, exist_ok=True) |
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| | | output_writer = open("{}/result.txt".format(output_path), "w") |
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| | | pse_label_writer = open("{}/labels.txt".format(output_path), "w") |
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| | | logging.info("Start to diarize...") |
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| | | result_list = [] |
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| | | for idx, (keys, batch) in enumerate(loader): |
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| | | assert isinstance(batch, dict), type(batch) |
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| | | assert all(isinstance(s, str) for s in keys), keys |
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| | | _bs = len(next(iter(batch.values()))) |
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| | | assert len(keys) == _bs, f"{len(keys)} != {_bs}" |
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| | | batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} |
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| | | |
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| | | results, pse_labels = speech2diar(**batch) |
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| | | # Only supporting batch_size==1 |
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| | | key, value = keys[0], output_results_str(results, keys[0]) |
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| | | item = {"key": key, "value": value} |
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| | | result_list.append(item) |
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| | | if output_path is not None: |
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| | | output_writer.write(value) |
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| | | output_writer.flush() |
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| | | pse_label_writer.write("{} {}\n".format(key, " ".join(pse_labels))) |
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| | | pse_label_writer.flush() |
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| | | |
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| | | if idx % 100 == 0: |
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| | | logging.info("Processing {:5d}: {}".format(idx, key)) |
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| | | |
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| | | if output_path is not None: |
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| | | output_writer.close() |
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| | | pse_label_writer.close() |
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| | | |
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| | | return result_list |
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| | | |
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| | | return _forward |
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| | | |
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| | | |
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| | | def inference_eend( |
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| | | diar_train_config: str, |
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| | | diar_model_file: str, |
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| | | output_dir: Optional[str] = None, |
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| | | batch_size: int = 1, |
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| | | dtype: str = "float32", |
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| | | ngpu: int = 1, |
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| | | num_workers: int = 0, |
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| | | log_level: Union[int, str] = "INFO", |
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| | | key_file: Optional[str] = None, |
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| | | model_tag: Optional[str] = None, |
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| | | allow_variable_data_keys: bool = True, |
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| | | streaming: bool = False, |
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| | | param_dict: Optional[dict] = None, |
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| | | **kwargs, |
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| | | ): |
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| | | ncpu = kwargs.get("ncpu", 1) |
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| | | torch.set_num_threads(ncpu) |
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| | | if batch_size > 1: |
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| | | raise NotImplementedError("batch decoding is not implemented") |
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| | | if ngpu > 1: |
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| | | raise NotImplementedError("only single GPU decoding is supported") |
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| | | |
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| | | logging.basicConfig( |
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| | | level=log_level, |
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| | | format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", |
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| | | ) |
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| | | logging.info("param_dict: {}".format(param_dict)) |
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| | | |
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| | | if ngpu >= 1 and torch.cuda.is_available(): |
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| | | device = "cuda" |
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| | | else: |
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| | | device = "cpu" |
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| | | |
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| | | # 1. Build speech2diar |
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| | | speech2diar_kwargs = dict( |
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| | | diar_train_config=diar_train_config, |
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| | | diar_model_file=diar_model_file, |
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| | | device=device, |
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| | | dtype=dtype, |
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| | | ) |
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| | | logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs)) |
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| | | speech2diar = Speech2DiarizationEEND(**speech2diar_kwargs) |
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| | | speech2diar.diar_model.eval() |
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| | | |
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| | | def output_results_str(results: dict, uttid: str): |
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| | | rst = [] |
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| | | mid = uttid.rsplit("-", 1)[0] |
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| | | for key in results: |
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| | | results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]] |
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| | | template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>" |
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| | | for spk, segs in results.items(): |
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| | | rst.extend([template.format(mid, st, ed, spk) for st, ed in segs]) |
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| | | |
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| | | return "\n".join(rst) |
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| | | |
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| | | def _forward( |
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| | | data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None, |
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| | | raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None, |
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| | | output_dir_v2: Optional[str] = None, |
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| | | param_dict: Optional[dict] = None, |
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| | | ): |
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| | | # 2. Build data-iterator |
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| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
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| | | if isinstance(raw_inputs, torch.Tensor): |
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| | | raw_inputs = raw_inputs.numpy() |
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| | | data_path_and_name_and_type = [raw_inputs[0], "speech", "sound"] |
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| | | loader = build_streaming_iterator( |
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| | | task_name="diar", |
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| | | preprocess_args=None, |
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| | | data_path_and_name_and_type=data_path_and_name_and_type, |
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| | | dtype=dtype, |
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| | | batch_size=batch_size, |
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| | | key_file=key_file, |
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| | | num_workers=num_workers, |
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| | | ) |
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| | | |
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| | | # 3. Start for-loop |
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| | | output_path = output_dir_v2 if output_dir_v2 is not None else output_dir |
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| | | if output_path is not None: |
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| | | os.makedirs(output_path, exist_ok=True) |
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| | | output_writer = open("{}/result.txt".format(output_path), "w") |
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| | | result_list = [] |
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| | | for keys, batch in loader: |
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| | | assert isinstance(batch, dict), type(batch) |
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| | | assert all(isinstance(s, str) for s in keys), keys |
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| | | _bs = len(next(iter(batch.values()))) |
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| | | assert len(keys) == _bs, f"{len(keys)} != {_bs}" |
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| | | # batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} |
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| | | |
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| | | results = speech2diar(**batch) |
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| | | |
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| | | # post process |
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| | | a = results[0][0].cpu().numpy() |
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| | | a = medfilt(a, (11, 1)) |
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| | | rst = [] |
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| | | for spkid, frames in enumerate(a.T): |
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| | | frames = np.pad(frames, (1, 1), 'constant') |
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| | | changes, = np.where(np.diff(frames, axis=0) != 0) |
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| | | fmt = "SPEAKER {:s} 1 {:7.2f} {:7.2f} <NA> <NA> {:s} <NA>" |
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| | | for s, e in zip(changes[::2], changes[1::2]): |
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| | | st = s / 10. |
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| | | dur = (e - s) / 10. |
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| | | rst.append(fmt.format(keys[0], st, dur, "{}_{}".format(keys[0], str(spkid)))) |
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| | | |
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| | | # Only supporting batch_size==1 |
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| | | value = "\n".join(rst) |
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| | | item = {"key": keys[0], "value": value} |
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| | | result_list.append(item) |
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| | | if output_path is not None: |
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| | | output_writer.write(value) |
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| | | output_writer.flush() |
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| | | |
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| | | if output_path is not None: |
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| | | output_writer.close() |
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| | | |
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| | | return result_list |
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| | | |
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| | | return _forward |
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| | | |
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| | | |
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| | | def inference_launch(mode, **kwargs): |
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| | | if mode == "sond": |
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| | | return inference_sond(mode=mode, **kwargs) |
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| | | elif mode == "sond_demo": |
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| | | param_dict = { |
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| | | "extract_profile": True, |
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| | | "sv_train_config": "sv.yaml", |
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| | | "sv_model_file": "sv.pb", |
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| | | } |
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| | | if "param_dict" in kwargs and kwargs["param_dict"] is not None: |
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| | | for key in param_dict: |
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| | | if key not in kwargs["param_dict"]: |
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| | | kwargs["param_dict"][key] = param_dict[key] |
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| | | else: |
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| | | kwargs["param_dict"] = param_dict |
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| | | return inference_sond(mode=mode, **kwargs) |
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| | | elif mode == "eend-ola": |
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| | | return inference_eend(mode=mode, **kwargs) |
|---|
| | | else: |
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| | | logging.info("Unknown decoding mode: {}".format(mode)) |
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| | | return None |
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| | | |
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| | | |
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| | | def get_parser(): |
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| | |
|---|
| | | help="The batch size for inference", |
|---|
| | | ) |
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| | | group.add_argument( |
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| | | "--diar_smooth_size", |
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| | | "--smooth_size", |
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| | | type=int, |
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| | | default=121, |
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| | | help="The smoothing size for post-processing" |
|---|
| | | ) |
|---|
| | | group.add_argument( |
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| | | "--dur_threshold", |
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| | | type=int, |
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| | | default=10, |
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| | | help="The threshold of minimum duration" |
|---|
| | | ) |
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| | | |
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| | | return parser |
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| | | |
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| | | |
|---|
| | | def inference_launch(mode, **kwargs): |
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| | | if mode == "sond": |
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| | | from funasr.bin.sond_inference import inference_modelscope |
|---|
| | | return inference_modelscope(mode=mode, **kwargs) |
|---|
| | | elif mode == "sond_demo": |
|---|
| | | from funasr.bin.sond_inference import inference_modelscope |
|---|
| | | param_dict = { |
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| | | "extract_profile": True, |
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| | | "sv_train_config": "sv.yaml", |
|---|
| | | "sv_model_file": "sv.pb", |
|---|
| | | } |
|---|
| | | if "param_dict" in kwargs and kwargs["param_dict"] is not None: |
|---|
| | | for key in param_dict: |
|---|
| | | if key not in kwargs["param_dict"]: |
|---|
| | | kwargs["param_dict"][key] = param_dict[key] |
|---|
| | | else: |
|---|
| | | kwargs["param_dict"] = param_dict |
|---|
| | | return inference_modelscope(mode=mode, **kwargs) |
|---|
| | | elif mode == "eend-ola": |
|---|
| | | from funasr.bin.eend_ola_inference import inference_modelscope |
|---|
| | | return inference_modelscope(mode=mode, **kwargs) |
|---|
| | | else: |
|---|
| | | logging.info("Unknown decoding mode: {}".format(mode)) |
|---|
| | | return None |
|---|
| | | |
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| | | |
|---|
| | | def main(cmd=None): |
|---|
| | |
|---|
| | | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" |
|---|
| | | os.environ["CUDA_VISIBLE_DEVICES"] = gpuid |
|---|
| | | |
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| | | inference_launch(**kwargs) |
|---|
| | | inference_pipeline = inference_launch(**kwargs) |
|---|
| | | return inference_pipeline(kwargs["data_path_and_name_and_type"]) |
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| | | |
|---|
| | | |
|---|
| | | if __name__ == "__main__": |
|---|
