# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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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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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 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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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 pathlib import Path
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from typing import 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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import numpy as np
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import torch
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from kaldiio import WriteHelper
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.tasks.sv import SVTask
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from funasr.torch_utils.device_funcs import to_device
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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.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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from funasr.utils.misc import statistic_model_parameters
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from funasr.bin.sv_infer import Speech2Xvector
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def inference_sv(
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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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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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sv_train_config: Optional[str] = "sv.yaml",
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sv_model_file: Optional[str] = "sv.pb",
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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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embedding_node: str = "resnet1_dense",
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sv_threshold: float = 0.9465,
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param_dict: Optional[dict] = None,
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**kwargs,
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):
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assert check_argument_types()
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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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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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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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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build 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=embedding_node
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)
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logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
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speech2xvector = Speech2Xvector.from_pretrained(
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model_tag=model_tag,
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**speech2xvector_kwargs,
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)
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speech2xvector.sv_model.eval()
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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: Union[np.ndarray, torch.Tensor] = 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, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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# 3. Build data-iterator
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loader = SVTask.build_streaming_iterator(
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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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preprocess_fn=None,
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collate_fn=None,
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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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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embd_writer, ref_embd_writer, score_writer = None, None, None
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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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embd_writer = WriteHelper("ark,scp:{}/xvector.ark,{}/xvector.scp".format(output_path, output_path))
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sv_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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embedding, ref_embedding, score = speech2xvector(**batch)
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# Only supporting batch_size==1
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key = keys[0]
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normalized_score = 0.0
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if score is not None:
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score = score.item()
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normalized_score = max(score - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
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item = {"key": key, "value": normalized_score}
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else:
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item = {"key": key, "value": embedding.squeeze(0).cpu().numpy()}
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sv_result_list.append(item)
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if output_path is not None:
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embd_writer(key, embedding[0].cpu().numpy())
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if ref_embedding is not None:
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if ref_embd_writer is None:
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ref_embd_writer = WriteHelper(
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"ark,scp:{}/ref_xvector.ark,{}/ref_xvector.scp".format(output_path, output_path)
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)
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score_writer = open(os.path.join(output_path, "score.txt"), "w")
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ref_embd_writer(key, ref_embedding[0].cpu().numpy())
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score_writer.write("{} {:.6f}\n".format(key, normalized_score))
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if output_path is not None:
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embd_writer.close()
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if ref_embd_writer is not None:
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ref_embd_writer.close()
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score_writer.close()
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return sv_result_list
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "sv":
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return inference_sv(**kwargs)
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Speaker Verification",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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# Note(kamo): Use '_' instead of '-' as separator.
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# '-' is confusing if written in yaml.
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=False)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument(
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"--njob",
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type=int,
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default=1,
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help="The number of jobs for each gpu",
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)
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parser.add_argument(
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"--gpuid_list",
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type=str,
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default="",
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help="The visible gpus",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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required=False,
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action="append",
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)
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=True)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--vad_infer_config",
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type=str,
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help="VAD infer configuration",
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)
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group.add_argument(
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"--vad_model_file",
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type=str,
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help="VAD model parameter file",
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)
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group.add_argument(
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"--sv_train_config",
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type=str,
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help="ASR training configuration",
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)
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group.add_argument(
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"--sv_model_file",
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type=str,
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help="ASR model parameter file",
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)
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group.add_argument(
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"--cmvn_file",
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type=str,
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help="Global CMVN file",
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)
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group.add_argument(
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"--model_tag",
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type=str,
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help="Pretrained model tag. If specify this option, *_train_config and "
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"*_file will be overwritten",
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)
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group = parser.add_argument_group("The inference configuration related")
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group.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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group.add_argument(
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"--sv_threshold",
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type=float,
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default=0.9465,
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help="The threshold for verification"
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)
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parser.add_argument(
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"--embedding_node",
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type=str,
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default="resnet1_dense",
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help="The network node to extract embedding"
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)
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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parser.add_argument(
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"--mode",
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type=str,
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default="sv",
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help="The decoding mode",
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)
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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# set logging messages
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logging.basicConfig(
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level=args.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("Decoding args: {}".format(kwargs))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_dir.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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