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| from denoiser.enhance import * | |
| def enhance_new(args, in_file, out_file, model=None, local_out_dir=None): | |
| # Load model | |
| if not model: | |
| model = pretrained.get_model(args).to(args.device) | |
| model.eval() | |
| dset = Audioset([(in_file, None)], with_path=True, | |
| sample_rate=model.sample_rate, channels=model.chin, convert=True) | |
| if dset is None: | |
| return | |
| loader = distrib.loader(dset, batch_size=1) | |
| distrib.barrier() | |
| with ProcessPoolExecutor(1) as pool: | |
| iterator = LogProgress(logger, loader, name="Generate enhanced files") | |
| pendings = [] | |
| for data in iterator: | |
| # Get batch data | |
| noisy_signals, filenames = data | |
| noisy_signals = noisy_signals.to(args.device) | |
| # Forward | |
| estimate = get_estimate(model, noisy_signals, args) | |
| for estimate, noisy, filename in zip(estimate, noisy_signals, filenames): | |
| write(estimate, out_file, sr=model.sample_rate) | |
| if pendings: | |
| print('Waiting for pending jobs...') | |
| for pending in LogProgress(logger, pendings, updates=5, name="Generate enhanced files"): | |
| pending.result() | |