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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-01-31 18:52:53 +08:00
add timestamp and epoch elapsed time
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@ -41,6 +41,18 @@ from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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global_step = 0
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import datetime
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class EpochRecorder:
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def __init__(self):
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self.last_time = ttime()
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def record(self):
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now_time = ttime()
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elapsed_time = now_time - self.last_time
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hr_and_min: str = str(datetime.timedelta(seconds=elapsed_time))
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current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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return f"[{current_time}] | ({hr_and_min})"
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def main():
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# n_gpus = torch.cuda.device_count()
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@ -314,6 +326,7 @@ def train_and_evaluate(
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data_iterator = enumerate(train_loader)
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# Run steps
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epoch_recorder = EpochRecorder()
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for batch_idx, info in data_iterator:
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# Data
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## Unpack
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@ -513,7 +526,7 @@ def train_and_evaluate(
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)
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if rank == 0:
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logger.info("====> Epoch: {}".format(epoch))
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logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
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if epoch >= hps.total_epoch and rank == 0:
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logger.info("Training is done. The program is closed.")
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from process_ckpt import savee # def savee(ckpt,sr,if_f0,name,epoch):
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