Spaces:
Runtime error
Runtime error
File size: 8,119 Bytes
5019931 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
import logging
import os
import time
from typing import List, NoReturn
import librosa
import numpy as np
import pysepm
import pytorch_lightning as pl
import torch.nn as nn
from pesq import pesq
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, read_yaml
def get_voicebank_demand_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']
evaluation_audios_dir = os.path.join(workspace, "evaluation_audios", task_name)
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]
assert target_source_type == 'speech'
# 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,
input_channels=input_channels,
sample_rate=sample_rate,
evaluation_audios_dir=evaluation_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]
return callbacks
class EvaluationCallback(pl.Callback):
def __init__(
self,
model: nn.Module,
input_channels: int,
evaluation_audios_dir,
sample_rate: int,
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
sample_rate: int
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.mono = True
self.sample_rate = sample_rate
self.segment_samples = segment_samples
self.evaluate_step_frequency = evaluate_step_frequency
self.logger = logger
self.statistics_container = statistics_container
self.clean_dir = os.path.join(evaluation_audios_dir, "clean_testset_wav")
self.noisy_dir = os.path.join(evaluation_audios_dir, "noisy_testset_wav")
self.EVALUATION_SAMPLE_RATE = 16000 # Evaluation sample rate of the
# Voicebank-Demand task.
# 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:
audio_names = sorted(
[
audio_name
for audio_name in sorted(os.listdir(self.clean_dir))
if audio_name.endswith('.wav')
]
)
error_str = "Directory {} does not contain audios for evaluation!".format(
self.clean_dir
)
assert len(audio_names) > 0, error_str
pesqs, csigs, cbaks, covls, ssnrs = [], [], [], [], []
logging.info("--- Step {} ---".format(global_step))
logging.info("Total {} pieces for evaluation:".format(len(audio_names)))
eval_time = time.time()
for n, audio_name in enumerate(audio_names):
# Load audio.
clean_path = os.path.join(self.clean_dir, audio_name)
mixture_path = os.path.join(self.noisy_dir, audio_name)
mixture, _ = librosa.core.load(
mixture_path, sr=self.sample_rate, mono=self.mono
)
if mixture.ndim == 1:
mixture = mixture[None, :]
# (channels_num, audio_length)
# Separate.
input_dict = {'waveform': mixture}
sep_wav = self.separator.separate(input_dict)
# (channels_num, audio_length)
# Target
clean, _ = librosa.core.load(
clean_path, sr=self.EVALUATION_SAMPLE_RATE, mono=self.mono
)
# to mono
sep_wav = np.squeeze(sep_wav)
# Resample for evaluation.
sep_wav = librosa.resample(
sep_wav,
orig_sr=self.sample_rate,
target_sr=self.EVALUATION_SAMPLE_RATE,
)
sep_wav = librosa.util.fix_length(sep_wav, size=len(clean), axis=0)
# (channels, audio_length)
# Evaluate metrics
pesq_ = pesq(self.EVALUATION_SAMPLE_RATE, clean, sep_wav, 'wb')
(csig, cbak, covl) = pysepm.composite(
clean, sep_wav, self.EVALUATION_SAMPLE_RATE
)
ssnr = pysepm.SNRseg(clean, sep_wav, self.EVALUATION_SAMPLE_RATE)
pesqs.append(pesq_)
csigs.append(csig)
cbaks.append(cbak)
covls.append(covl)
ssnrs.append(ssnr)
print(
'{}, {}, PESQ: {:.3f}, CSIG: {:.3f}, CBAK: {:.3f}, COVL: {:.3f}, SSNR: {:.3f}'.format(
n, audio_name, pesq_, csig, cbak, covl, ssnr
)
)
logging.info("-----------------------------")
logging.info('Avg PESQ: {:.3f}'.format(np.mean(pesqs)))
logging.info('Avg CSIG: {:.3f}'.format(np.mean(csigs)))
logging.info('Avg CBAK: {:.3f}'.format(np.mean(cbaks)))
logging.info('Avg COVL: {:.3f}'.format(np.mean(covls)))
logging.info('Avg SSNR: {:.3f}'.format(np.mean(ssnrs)))
logging.info("Evlauation time: {:.3f}".format(time.time() - eval_time))
statistics = {"pesq": np.mean(pesqs)}
self.statistics_container.append(global_step, statistics, 'test')
self.statistics_container.dump()
|