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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import sys
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from collections import OrderedDict
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Union
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import paddle
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import soundfile
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from yacs.config import CfgNode
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from ..executor import BaseExecutor
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from ..log import logger
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from ..utils import cli_register
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from ..utils import stats_wrapper
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from .pretrained_models import model_alias
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from .pretrained_models import pretrained_models
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from paddleaudio.backends import load as load_audio
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from paddleaudio.compliance.librosa import melspectrogram
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.vector.io.batch import feature_normalize
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from paddlespeech.vector.modules.sid_model import SpeakerIdetification
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@cli_register(
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name="paddlespeech.vector",
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description="Speech to vector embedding infer command.")
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class VectorExecutor(BaseExecutor):
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def __init__(self):
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super().__init__()
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self.model_alias = model_alias
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self.pretrained_models = pretrained_models
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self.parser = argparse.ArgumentParser(
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prog="paddlespeech.vector", add_help=True)
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self.parser.add_argument(
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"--model",
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type=str,
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default="ecapatdnn_voxceleb12",
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choices=["ecapatdnn_voxceleb12"],
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help="Choose model type of vector task.")
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self.parser.add_argument(
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"--task",
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type=str,
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default="spk",
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choices=["spk", "score"],
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help="task type in vector domain")
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self.parser.add_argument(
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"--input",
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type=str,
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default=None,
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help="Audio file to extract embedding.")
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self.parser.add_argument(
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"--sample_rate",
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type=int,
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default=16000,
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choices=[16000],
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help="Choose the audio sample rate of the model. 8000 or 16000")
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self.parser.add_argument(
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"--ckpt_path",
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type=str,
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default=None,
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help="Checkpoint file of model.")
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self.parser.add_argument(
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'--config',
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type=str,
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default=None,
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help='Config of asr task. Use deault config when it is None.')
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self.parser.add_argument(
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"--device",
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type=str,
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default=paddle.get_device(),
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help="Choose device to execute model inference.")
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self.parser.add_argument(
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'-d',
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'--job_dump_result',
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action='store_true',
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help='Save job result into file.')
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self.parser.add_argument(
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'-v',
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'--verbose',
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action='store_true',
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help='Increase logger verbosity of current task.')
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def execute(self, argv: List[str]) -> bool:
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"""Command line entry for vector model
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Args:
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argv (List[str]): command line args list
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Returns:
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bool:
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False: some audio occurs error
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True: all audio process success
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"""
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# stage 0: parse the args and get the required args
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parser_args = self.parser.parse_args(argv)
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model = parser_args.model
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sample_rate = parser_args.sample_rate
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config = parser_args.config
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ckpt_path = parser_args.ckpt_path
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device = parser_args.device
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# stage 1: configurate the verbose flag
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if not parser_args.verbose:
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self.disable_task_loggers()
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# stage 2: read the input data and store them as a list
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task_source = self.get_task_source(parser_args.input)
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logger.info(f"task source: {task_source}")
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# stage 3: process the audio one by one
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# we do action according the task type
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task_result = OrderedDict()
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has_exceptions = False
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for id_, input_ in task_source.items():
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try:
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# extract the speaker audio embedding
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if parser_args.task == "spk":
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logger.info("do vector spk task")
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res = self(input_, model, sample_rate, config, ckpt_path,
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device)
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task_result[id_] = res
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elif parser_args.task == "score":
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logger.info("do vector score task")
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logger.info(f"input content {input_}")
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if len(input_.split()) != 2:
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logger.error(
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f"vector score task input {input_} wav num is not two,"
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"that is {len(input_.split())}")
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sys.exit(-1)
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# get the enroll and test embedding
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enroll_audio, test_audio = input_.split()
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logger.info(
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f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}"
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)
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enroll_embedding = self(enroll_audio, model, sample_rate,
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config, ckpt_path, device)
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test_embedding = self(test_audio, model, sample_rate,
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config, ckpt_path, device)
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# get the score
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res = self.get_embeddings_score(enroll_embedding,
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test_embedding)
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task_result[id_] = res
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except Exception as e:
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has_exceptions = True
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task_result[id_] = f'{e.__class__.__name__}: {e}'
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logger.info("task result as follows: ")
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logger.info(f"{task_result}")
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# stage 4: process the all the task results
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self.process_task_results(parser_args.input, task_result,
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parser_args.job_dump_result)
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# stage 5: return the exception flag
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# if return False, somen audio process occurs error
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if has_exceptions:
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return False
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else:
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return True
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def _get_job_contents(
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self, job_input: os.PathLike) -> Dict[str, Union[str, os.PathLike]]:
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"""
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Read a job input file and return its contents in a dictionary.
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Refactor from the Executor._get_job_contents
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Args:
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job_input (os.PathLike): The job input file.
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Returns:
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Dict[str, str]: Contents of job input.
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"""
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job_contents = OrderedDict()
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with open(job_input) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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k = line.split(' ')[0]
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v = ' '.join(line.split(' ')[1:])
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job_contents[k] = v
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return job_contents
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def get_embeddings_score(self, enroll_embedding, test_embedding):
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"""get the enroll embedding and test embedding score
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Args:
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enroll_embedding (numpy.array): shape: (emb_size), enroll audio embedding
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test_embedding (numpy.array): shape: (emb_size), test audio embedding
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Returns:
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score: the score between enroll embedding and test embedding
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"""
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if not hasattr(self, "score_func"):
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self.score_func = paddle.nn.CosineSimilarity(axis=0)
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logger.info("create the cosine score function ")
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score = self.score_func(
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paddle.to_tensor(enroll_embedding),
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paddle.to_tensor(test_embedding))
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return score.item()
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@stats_wrapper
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def __call__(self,
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audio_file: os.PathLike,
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model: str='ecapatdnn_voxceleb12',
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sample_rate: int=16000,
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config: os.PathLike=None,
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ckpt_path: os.PathLike=None,
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device=paddle.get_device()):
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"""Extract the audio embedding
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Args:
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audio_file (os.PathLike): audio path,
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whose format must be wav and sample rate must be matched the model
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model (str, optional): mode type, which is been loaded from the pretrained model list.
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Defaults to 'ecapatdnn-voxceleb12'.
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sample_rate (int, optional): model sample rate. Defaults to 16000.
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config (os.PathLike, optional): yaml config. Defaults to None.
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ckpt_path (os.PathLike, optional): pretrained model path. Defaults to None.
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device (optional): paddle running host device. Defaults to paddle.get_device().
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Returns:
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dict: return the audio embedding and the embedding shape
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"""
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# stage 0: check the audio format
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audio_file = os.path.abspath(audio_file)
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if not self._check(audio_file, sample_rate):
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sys.exit(-1)
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# stage 1: set the paddle runtime host device
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logger.info(f"device type: {device}")
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paddle.device.set_device(device)
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# stage 2: read the specific pretrained model
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self._init_from_path(model, sample_rate, config, ckpt_path)
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# stage 3: preprocess the audio and get the audio feat
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self.preprocess(model, audio_file)
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# stage 4: infer the model and get the audio embedding
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self.infer(model)
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# stage 5: process the result and set them to output dict
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res = self.postprocess()
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return res
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def _init_from_path(self,
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model_type: str='ecapatdnn_voxceleb12',
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sample_rate: int=16000,
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None):
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"""Init the neural network from the model path
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Args:
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model_type (str, optional): model tag in the pretrained model list.
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Defaults to 'ecapatdnn_voxceleb12'.
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sample_rate (int, optional): model sample rate.
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Defaults to 16000.
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cfg_path (Optional[os.PathLike], optional): yaml config file path.
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Defaults to None.
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ckpt_path (Optional[os.PathLike], optional): the pretrained model path, which is stored in the disk.
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Defaults to None.
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"""
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# stage 0: avoid to init the mode again
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if hasattr(self, "model"):
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logger.info("Model has been initialized")
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return
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# stage 1: get the model and config path
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# if we want init the network from the model stored in the disk,
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# we must pass the config path and the ckpt model path
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if cfg_path is None or ckpt_path is None:
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# get the mode from pretrained list
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sample_rate_str = "16k" if sample_rate == 16000 else "8k"
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tag = model_type + "-" + sample_rate_str
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logger.info(f"load the pretrained model: {tag}")
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# get the model from the pretrained list
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# we download the pretrained model and store it in the res_path
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res_path = self._get_pretrained_path(tag)
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self.res_path = res_path
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self.cfg_path = os.path.join(
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res_path, self.pretrained_models[tag]['cfg_path'])
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self.ckpt_path = os.path.join(
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res_path,
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self.pretrained_models[tag]['ckpt_path'] + '.pdparams')
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else:
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# get the model from disk
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self.cfg_path = os.path.abspath(cfg_path)
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self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams")
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self.res_path = os.path.dirname(
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os.path.dirname(os.path.abspath(self.cfg_path)))
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logger.info(f"start to read the ckpt from {self.ckpt_path}")
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logger.info(f"read the config from {self.cfg_path}")
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logger.info(f"get the res path {self.res_path}")
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# stage 2: read and config and init the model body
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self.config = CfgNode(new_allowed=True)
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self.config.merge_from_file(self.cfg_path)
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# stage 3: get the model name to instance the model network with dynamic_import
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logger.info("start to dynamic import the model class")
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model_name = model_type[:model_type.rindex('_')]
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logger.info(f"model name {model_name}")
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model_class = dynamic_import(model_name, self.model_alias)
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model_conf = self.config.model
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backbone = model_class(**model_conf)
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model = SpeakerIdetification(
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backbone=backbone, num_class=self.config.num_speakers)
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self.model = model
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self.model.eval()
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# stage 4: load the model parameters
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logger.info("start to set the model parameters to model")
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model_dict = paddle.load(self.ckpt_path)
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self.model.set_state_dict(model_dict)
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logger.info("create the model instance success")
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@paddle.no_grad()
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def infer(self, model_type: str):
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"""Infer the model to get the embedding
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Args:
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model_type (str): speaker verification model type
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"""
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# stage 0: get the feat and length from _inputs
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feats = self._inputs["feats"]
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lengths = self._inputs["lengths"]
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logger.info("start to do backbone network model forward")
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logger.info(
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f"feats shape:{feats.shape}, lengths shape: {lengths.shape}")
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# stage 1: get the audio embedding
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# embedding from (1, emb_size, 1) -> (emb_size)
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embedding = self.model.backbone(feats, lengths).squeeze().numpy()
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logger.info(f"embedding size: {embedding.shape}")
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# stage 2: put the embedding and dim info to _outputs property
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# the embedding type is numpy.array
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self._outputs["embedding"] = embedding
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def postprocess(self) -> Union[str, os.PathLike]:
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"""Return the audio embedding info
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Returns:
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Union[str, os.PathLike]: audio embedding info
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"""
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embedding = self._outputs["embedding"]
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return embedding
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def preprocess(self, model_type: str, input_file: Union[str, os.PathLike]):
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"""Extract the audio feat
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Args:
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model_type (str): speaker verification model type
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input_file (Union[str, os.PathLike]): audio file path
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"""
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audio_file = input_file
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if isinstance(audio_file, (str, os.PathLike)):
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logger.info(f"Preprocess audio file: {audio_file}")
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# stage 1: load the audio sample points
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# Note: this process must match the training process
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waveform, sr = load_audio(audio_file)
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logger.info(f"load the audio sample points, shape is: {waveform.shape}")
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# stage 2: get the audio feat
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# Note: Now we only support fbank feature
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try:
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feat = melspectrogram(
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x=waveform,
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sr=self.config.sr,
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n_mels=self.config.n_mels,
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window_size=self.config.window_size,
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hop_length=self.config.hop_size)
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logger.info(f"extract the audio feat, shape is: {feat.shape}")
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except Exception as e:
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logger.info(f"feat occurs exception {e}")
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sys.exit(-1)
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feat = paddle.to_tensor(feat).unsqueeze(0)
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# in inference period, the lengths is all one without padding
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lengths = paddle.ones([1])
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# stage 3: we do feature normalize,
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# Now we assume that the feat must do normalize
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feat = feature_normalize(feat, mean_norm=True, std_norm=False)
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# stage 4: store the feat and length in the _inputs,
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# which will be used in other function
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logger.info(f"feats shape: {feat.shape}")
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self._inputs["feats"] = feat
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self._inputs["lengths"] = lengths
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logger.info("audio extract the feat success")
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def _check(self, audio_file: str, sample_rate: int):
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"""Check if the model sample match the audio sample rate
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Args:
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audio_file (str): audio file path, which will be extracted the embedding
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sample_rate (int): the desired model sample rate
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Returns:
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bool: return if the audio sample rate matches the model sample rate
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"""
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self.sample_rate = sample_rate
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if self.sample_rate != 16000 and self.sample_rate != 8000:
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logger.error(
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"invalid sample rate, please input --sr 8000 or --sr 16000")
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return False
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if isinstance(audio_file, (str, os.PathLike)):
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if not os.path.isfile(audio_file):
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logger.error("Please input the right audio file path")
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return False
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logger.info("checking the aduio file format......")
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try:
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audio, audio_sample_rate = soundfile.read(
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audio_file, dtype="float32", always_2d=True)
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except Exception as e:
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logger.exception(e)
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logger.error(
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"can not open the audio file, please check the audio file format is 'wav'. \n \
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you can try to use sox to change the file format.\n \
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For example: \n \
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sample rate: 16k \n \
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sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
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sample rate: 8k \n \
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sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
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")
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return False
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logger.info(f"The sample rate is {audio_sample_rate}")
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if audio_sample_rate != self.sample_rate:
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logger.error("The sample rate of the input file is not {}.\n \
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The program will resample the wav file to {}.\n \
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If the result does not meet your expectations,\n \
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Please input the 16k 16 bit 1 channel wav file. \
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".format(self.sample_rate, self.sample_rate))
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sys.exit(-1)
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else:
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logger.info("The audio file format is right")
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return True
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