hop_length=hop_length, onesided=True, center=True, window=window) if total_audio is None: total_audio = audio_out_denoised else: total_audio = torch.cat((total_audio, audio_out_denoised), 0) else: total_audio = torch.istft(audio_spec, n_fft, hop_length=hop_length, onesided=True, center=True, window=window) scipy.io.wavfile.write( ARGS.output_dir + "/denoised_" + str(i) + "_" + filename[0].split("/")[-1].split(".")[0] + ".wav", sr, total_audio.numpy().T) #before or after writing intensity scaling to chose dB value scipy.io.wavfile.write( ARGS.output_dir + "/denoised_" + str(i) + "_" + filename[0].split("/")[-1].split(".")[0] + ".wav", sr, total_audio.numpy().T) log.debug("Finished proccessing") log.close()
print(vscore) print("Viterbi paths:") print(vpaths) logger.log(step, tr_nll.data[0], val_nll.data[0], tr_nll.data[0] / batch_size, val_nll.data[0] / batch_size) # serialize model occasionally: if step % save_every == 0: logger.save(step, crf) step += 1 if step > max_iters: raise StopIteration del dataset #--- handle keyboard interrupts: except KeyboardInterrupt: del dataset logger.close() print("-" * 80) print("Halted training; reached {} training iterations.".format(step)) except StopIteration: del dataset logger.close() print("-" * 80) print("Finished training; reached max iterations of {}.".format(max_iters)) except Exception as e: print("Something went wrong:") print(e) del dataset logger.close()