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import logging
import os
import time
from typing import List, NoReturn
import librosa
import numpy as np
import pytorch_lightning as pl
import torch.nn as nn
from pytorch_lightning.utilities import rank_zero_only
from bytesep.callbacks.base_callbacks import SaveCheckpointsCallback
from bytesep.inference import Separator
from bytesep.utils import StatisticsContainer, calculate_sdr, read_yaml
def get_instruments_callbacks(
config_yaml: str,
workspace: str,
checkpoints_dir: str,
statistics_path: str,
logger: pl.loggers.TensorBoardLogger,
model: nn.Module,
evaluate_device: str,
) -> List[pl.Callback]:
"""Get Voicebank-Demand callbacks of a config yaml.
Args:
config_yaml: str
workspace: str
checkpoints_dir: str, directory to save checkpoints
statistics_dir: str, directory to save statistics
logger: pl.loggers.TensorBoardLogger
model: nn.Module
evaluate_device: str
Return:
callbacks: List[pl.Callback]
"""
configs = read_yaml(config_yaml)
task_name = configs['task_name']
target_source_types = configs['train']['target_source_types']
input_channels = configs['train']['channels']
mono = True if input_channels == 1 else False
test_audios_dir = os.path.join(workspace, "evaluation_audios", task_name, "test")
sample_rate = configs['train']['sample_rate']
evaluate_step_frequency = configs['train']['evaluate_step_frequency']
save_step_frequency = configs['train']['save_step_frequency']
test_batch_size = configs['evaluate']['batch_size']
test_segment_seconds = configs['evaluate']['segment_seconds']
test_segment_samples = int(test_segment_seconds * sample_rate)
assert len(target_source_types) == 1
target_source_type = target_source_types[0]
# save checkpoint callback
save_checkpoints_callback = SaveCheckpointsCallback(
model=model,
checkpoints_dir=checkpoints_dir,
save_step_frequency=save_step_frequency,
)
# statistics container
statistics_container = StatisticsContainer(statistics_path)
# evaluation callback
evaluate_test_callback = EvaluationCallback(
model=model,
target_source_type=target_source_type,
input_channels=input_channels,
sample_rate=sample_rate,
mono=mono,
evaluation_audios_dir=test_audios_dir,
segment_samples=test_segment_samples,
batch_size=test_batch_size,
device=evaluate_device,
evaluate_step_frequency=evaluate_step_frequency,
logger=logger,
statistics_container=statistics_container,
)
callbacks = [save_checkpoints_callback, evaluate_test_callback]
# callbacks = [save_checkpoints_callback]
return callbacks
class EvaluationCallback(pl.Callback):
def __init__(
self,
model: nn.Module,
input_channels: int,
evaluation_audios_dir: str,
target_source_type: str,
sample_rate: int,
mono: bool,
segment_samples: int,
batch_size: int,
device: str,
evaluate_step_frequency: int,
logger: pl.loggers.TensorBoardLogger,
statistics_container: StatisticsContainer,
):
r"""Callback to evaluate every #save_step_frequency steps.
Args:
model: nn.Module
input_channels: int
evaluation_audios_dir: str, directory containing audios for evaluation
target_source_type: str, e.g., 'violin'
sample_rate: int
mono: bool
segment_samples: int, length of segments to be input to a model, e.g., 44100*30
batch_size, int, e.g., 12
device: str, e.g., 'cuda'
evaluate_step_frequency: int, evaluate every #save_step_frequency steps
logger: pl.loggers.TensorBoardLogger
statistics_container: StatisticsContainer
"""
self.model = model
self.target_source_type = target_source_type
self.sample_rate = sample_rate
self.mono = mono
self.segment_samples = segment_samples
self.evaluate_step_frequency = evaluate_step_frequency
self.logger = logger
self.statistics_container = statistics_container
self.evaluation_audios_dir = evaluation_audios_dir
# separator
self.separator = Separator(model, self.segment_samples, batch_size, device)
@rank_zero_only
def on_batch_end(self, trainer: pl.Trainer, _) -> NoReturn:
r"""Evaluate losses on a few mini-batches. Losses are only used for
observing training, and are not final F1 metrics.
"""
global_step = trainer.global_step
if global_step % self.evaluate_step_frequency == 0:
mixture_audios_dir = os.path.join(self.evaluation_audios_dir, 'mixture')
clean_audios_dir = os.path.join(
self.evaluation_audios_dir, self.target_source_type
)
audio_names = sorted(os.listdir(mixture_audios_dir))
error_str = "Directory {} does not contain audios for evaluation!".format(
self.evaluation_audios_dir
)
assert len(audio_names) > 0, error_str
logging.info("--- Step {} ---".format(global_step))
logging.info("Total {} pieces for evaluation:".format(len(audio_names)))
eval_time = time.time()
sdrs = []
for n, audio_name in enumerate(audio_names):
# Load audio.
mixture_path = os.path.join(mixture_audios_dir, audio_name)
clean_path = os.path.join(clean_audios_dir, audio_name)
mixture, origin_fs = librosa.core.load(
mixture_path, sr=self.sample_rate, mono=self.mono
)
# Target
clean, origin_fs = librosa.core.load(
clean_path, sr=self.sample_rate, mono=self.mono
)
if mixture.ndim == 1:
mixture = mixture[None, :]
# (channels_num, audio_length)
input_dict = {'waveform': mixture}
# separate
sep_wav = self.separator.separate(input_dict)
# (channels_num, audio_length)
sdr = calculate_sdr(ref=clean, est=sep_wav)
print("{} SDR: {:.3f}".format(audio_name, sdr))
sdrs.append(sdr)
logging.info("-----------------------------")
logging.info('Avg SDR: {:.3f}'.format(np.mean(sdrs)))
logging.info("Evlauation time: {:.3f}".format(time.time() - eval_time))
statistics = {"sdr": np.mean(sdrs)}
self.statistics_container.append(global_step, statistics, 'test')
self.statistics_container.dump()
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