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@ -15,6 +15,7 @@ import argparse
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import os
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import os
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import sys
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import sys
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from collections import OrderedDict
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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 List
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from typing import Optional
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from typing import Optional
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from typing import Union
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from typing import Union
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@ -79,7 +80,7 @@ class VectorExecutor(BaseExecutor):
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"--task",
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"--task",
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type=str,
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type=str,
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default="spk",
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default="spk",
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choices=["spk"],
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choices=["spk", "score"],
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help="task type in vector domain")
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help="task type in vector domain")
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self.parser.add_argument(
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self.parser.add_argument(
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"--input",
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"--input",
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@ -147,13 +148,40 @@ class VectorExecutor(BaseExecutor):
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logger.info(f"task source: {task_source}")
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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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# 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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task_result = OrderedDict()
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has_exceptions = False
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has_exceptions = False
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for id_, input_ in task_source.items():
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for id_, input_ in task_source.items():
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try:
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try:
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res = self(input_, model, sample_rate, config, ckpt_path,
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# extract the speaker audio embedding
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device)
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if parser_args.task == "spk":
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task_result[id_] = res
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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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except Exception as e:
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has_exceptions = True
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has_exceptions = True
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task_result[id_] = f'{e.__class__.__name__}: {e}'
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task_result[id_] = f'{e.__class__.__name__}: {e}'
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@ -172,6 +200,49 @@ class VectorExecutor(BaseExecutor):
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else:
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else:
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return True
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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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@stats_wrapper
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def __call__(self,
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def __call__(self,
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audio_file: os.PathLike,
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audio_file: os.PathLike,
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Honei
2 years ago
committed by
GitHub