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changed checkpoint format
This commit is contained in:
Jan Buethe
Jean-Marc Valin
parent
733a095ba2
commit
41a4c9515d
4 changed files with 29 additions and 132 deletions
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@ -120,31 +120,9 @@ def rpa(model,device = 'cpu',data_format = 'if'):
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cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int')
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# if (model == 'penn'):
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# model_frequency, _ = penn.from_audio(
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# torch.from_numpy(audio).unsqueeze(0).float(),
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# 16000,
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# hopsize=0.01,
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# fmin=(16000.0/256),
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# fmax=500,
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# checkpoint=penn.DEFAULT_CHECKPOINT,
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# batch_size=32,
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# pad=True,
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# interp_unvoiced_at=0.065,
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# gpu=0)
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# model_frequency = model_frequency.cpu().detach().squeeze().numpy()
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# model_cents = 1200*np.log2(model_frequency/(16000/256))
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# elif (model == 'crepe'):
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# _, model_frequency, _, _ = crepe.predict(audio, 16000, viterbi=vflag,center=True,verbose=0)
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# lpcnet_file_name = '/home/ubuntu/Code/Datasets/SPEECH_DATA/lpcnet_f0_16k_residual/' + file_name + '_f0.f32'
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# period_lpcnet = np.fromfile(lpcnet_file_name, dtype='float32')
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# model_frequency = 16000/(period_lpcnet + 1.0e-6)
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# model_cents = 1200*np.log2(model_frequency/(16000/256))
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# else:
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model_cents = model(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device))
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model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy()
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# model_cents = np.roll(model_cents,-1*3)
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num_frames = min(cent.shape[0],model_cents.shape[0])
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pitch = pitch[:num_frames]
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@ -158,131 +136,62 @@ def rpa(model,device = 'cpu',data_format = 'if'):
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voicing_all[force_out_of_pitch] = 0
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C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
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# list_rca_model_all.append(sweep_rca(cent,model_cents,voicing_all,thresh,[0]))
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list_rca_model_all.append(rca(cent,model_cents,voicing_all,thresh))
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# list_rca_model_all.append(np.count_nonzero(np.where(np.abs(cent - model_cents))))
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if "mic_M" in audio_file:
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# list_rca_male_all.append(sweep_rca(cent,model_cents,voicing_all,thresh,[0]))
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list_rca_male_all.append(rca(cent,model_cents,voicing_all,thresh))
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C_all_m = C_all_m + np.where(voicing_all != 0)[0].shape[0]
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else:
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# list_rca_female_all.append(sweep_rca(cent,model_cents,voicing_all,thresh,[0]))
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list_rca_female_all.append(rca(cent,model_cents,voicing_all,thresh))
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C_all_f = C_all_f + np.where(voicing_all != 0)[0].shape[0]
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"""
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# Low pitch estimation
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voicing_lp = np.copy(voicing)
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force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 125)==True)
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voicing_lp[force_out_of_pitch] = 0
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C_lp = C_lp + np.where(voicing_lp != 0)[0].shape[0]
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# list_rca_model_lp.append(sweep_rca(cent,model_cents,voicing_lp,thresh,[0]))
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list_rca_model_lp.append(rca(cent,model_cents,voicing_lp,thresh))
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if "mic_M" in audio_file:
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# list_rca_male_lp.append(sweep_rca(cent,model_cents,voicing_lp,thresh,[0]))
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list_rca_male_lp.append(rca(cent,model_cents,voicing_lp,thresh))
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C_lp_m = C_lp_m + np.where(voicing_lp != 0)[0].shape[0]
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else:
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# list_rca_female_lp.append(sweep_rca(cent,model_cents,voicing_lp,thresh,[0]))
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list_rca_female_lp.append(rca(cent,model_cents,voicing_lp,thresh))
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C_lp_f = C_lp_f + np.where(voicing_lp != 0)[0].shape[0]
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# High pitch estimation
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voicing_hp = np.copy(voicing)
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force_out_of_pitch = np.where(np.logical_or(pitch < 125,pitch > 500)==True)
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voicing_hp[force_out_of_pitch] = 0
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C_hp = C_hp + np.where(voicing_hp != 0)[0].shape[0]
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# list_rca_model_hp.append(sweep_rca(cent,model_cents,voicing_hp,thresh,[0]))
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list_rca_model_hp.append(rca(cent,model_cents,voicing_hp,thresh))
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if "mic_M" in audio_file:
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# list_rca_male_hp.append(sweep_rca(cent,model_cents,voicing_hp,thresh,[0]))
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list_rca_male_hp.append(rca(cent,model_cents,voicing_hp,thresh))
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C_hp_m = C_hp_m + np.where(voicing_hp != 0)[0].shape[0]
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else:
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# list_rca_female_hp.append(sweep_rca(cent,model_cents,voicing_hp,thresh,[0]))
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list_rca_female_hp.append(rca(cent,model_cents,voicing_hp,thresh))
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C_hp_f = C_hp_f + np.where(voicing_hp != 0)[0].shape[0]
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# list_rca_model.append(acc_model)
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# list_rca_crepe.append(acc_crepe)
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# list_rca_lpcnet.append(acc_lpcnet)
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# list_rca_penn.append(acc_penn)
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"""
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# list_rca_crepe = np.array(list_rca_crepe)
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# list_rca_model_lp = np.array(list_rca_model_lp)
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# list_rca_male_lp = np.array(list_rca_male_lp)
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# list_rca_female_lp = np.array(list_rca_female_lp)
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# list_rca_model_hp = np.array(list_rca_model_hp)
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# list_rca_male_hp = np.array(list_rca_male_hp)
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# list_rca_female_hp = np.array(list_rca_female_hp)
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list_rca_model_all = np.array(list_rca_model_all)
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list_rca_male_all = np.array(list_rca_male_all)
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list_rca_female_all = np.array(list_rca_female_all)
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# list_rca_lpcnet = np.array(list_rca_lpcnet)
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# list_rca_penn = np.array(list_rca_penn)
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x = PrettyTable()
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x.field_names = ["Experiment", "Mean RPA"]
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x.add_row(["Both all pitches", np.sum(list_rca_model_all)/C_all])
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# x.add_row(["Both low pitches", np.sum(list_rca_model_lp)/C_lp])
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# x.add_row(["Both high pitches", np.sum(list_rca_model_hp)/C_hp])
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x.add_row(["Male all pitches", np.sum(list_rca_male_all)/C_all_m])
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# x.add_row(["Male low pitches", np.sum(list_rca_male_lp)/C_lp_m])
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# x.add_row(["Male high pitches", np.sum(list_rca_male_hp)/C_hp_m])
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x.add_row(["Female all pitches", np.sum(list_rca_female_all)/C_all_f])
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# x.add_row(["Female low pitches", np.sum(list_rca_female_lp)/C_lp_f])
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# x.add_row(["Female high pitches", np.sum(list_rca_female_hp)/C_hp_f])
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print(x)
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return None
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def cycle_eval(list_files_pth, noise_type = 'synthetic', noise_dataset = None, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = None,fraction = 0.1,thresh = 50):
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def cycle_eval(checkpoint_list, noise_type = 'synthetic', noise_dataset = None, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = None,fraction = 0.1,thresh = 50):
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"""
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Cycle through SNR evaluation for list of .pth files
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Cycle through SNR evaluation for list of checkpoints
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"""
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# list_files = glob.glob('/home/ubuntu/Code/Datasets/SPEECH DATA/combined_mic_16k_raw/*.raw')
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# dir_f0 = '/home/ubuntu/Code/Datasets/SPEECH DATA/combine_f0_ptdb/'
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# random_shuffle = list(np.random.permutation(len(list_files)))
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list_files = glob.glob(ptdb_dataset_path + 'combined_mic_16k/*.raw')
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dir_f0 = ptdb_dataset_path + 'combined_reference_f0/'
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random.shuffle(list_files)
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list_files = list_files[:(int)(fraction*len(list_files))]
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# list_nfiles = ['DKITCHEN','NFIELD','OHALLWAY','PCAFETER','SPSQUARE','TCAR','DLIVING','NPARK','OMEETING','PRESTO','STRAFFIC','TMETRO','DWASHING','NRIVER','OOFFICE','PSTATION','TBUS']
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dict_models = {}
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list_snr.append(np.inf)
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# thresh = 50
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for f in list_files_pth:
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for f in checkpoint_list:
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if (f!='crepe') and (f!='lpcnet'):
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fname = os.path.basename(f).split('_')[0] + '_' + os.path.basename(f).split('_')[-1][:-4]
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config_path = os.path.dirname(f) + '/' + os.path.basename(f).split('_')[0] + '_' + 'config_' + os.path.basename(f).split('_')[-1][:-4] + '.json'
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with open(config_path) as json_file:
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dict_params = json.load(json_file)
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checkpoint = torch.load(f, map_location='cpu')
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dict_params = checkpoint['config']
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if dict_params['data_format'] == 'if':
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from models import large_if_ccode as model
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim'])
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elif dict_params['data_format'] == 'xcorr':
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from models import large_xcorr as model
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pitch_nn = model(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
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else:
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from models import large_joint as model
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
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pitch_nn.load_state_dict(torch.load(f))
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pitch_nn.load_state_dict(checkpoint['state_dict'])
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N = dict_params['window_size']
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H = dict_params['hop_factor']
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@ -356,15 +265,8 @@ def cycle_eval(list_files_pth, noise_type = 'synthetic', noise_dataset = None, l
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cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int')
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# if os.path.basename(f) == 'crepe':
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# elif (model == 'crepe'):
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# _, model_frequency, _, _ = crepe.predict(np.concatenate([np.zeros(80),audio]), 16000, viterbi=True,center=True,verbose=0)
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# model_cents = 1200*np.log2(model_frequency/(16000/256))
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# else:
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# else:
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model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device))
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model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy()
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# model_cents = np.roll(model_cents,-1*3)
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num_frames = min(cent.shape[0],model_cents.shape[0])
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pitch = pitch[:num_frames]
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@ -378,9 +280,7 @@ def cycle_eval(list_files_pth, noise_type = 'synthetic', noise_dataset = None, l
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voicing_all[force_out_of_pitch] = 0
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C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
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# list_rca_model_all.append(sweep_rca(cent,model_cents,voicing_all,thresh,[0]))
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C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh)
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# list_rca_model_all.append(np.count_nonzero(np.where(np.abs(cent - model_cents))))
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list_mean.append(C_correct/C_all)
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else:
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fname = f
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@ -453,9 +353,7 @@ def cycle_eval(list_files_pth, noise_type = 'synthetic', noise_dataset = None, l
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voicing_all[force_out_of_pitch] = 0
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C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
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# list_rca_model_all.append(sweep_rca(cent,model_cents,voicing_all,thresh,[0]))
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C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh)
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# list_rca_model_all.append(np.count_nonzero(np.where(np.abs(cent - model_cents))))
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list_mean.append(C_correct/C_all)
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dict_models[fname] = {}
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dict_models[fname]['list_SNR'] = list_mean[:-1]
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@ -36,7 +36,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
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parser = argparse.ArgumentParser()
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parser.add_argument('checkpoint', type=str, help='rdovae model checkpoint')
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parser.add_argument('checkpoint', type=str, help='model checkpoint')
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parser.add_argument('output_dir', type=str, help='output folder')
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args = parser.parse_args()
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@ -85,5 +85,6 @@ if __name__ == "__main__":
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os.makedirs(args.output_dir, exist_ok=True)
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model = large_if_ccode()
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model.load_state_dict(torch.load(args.checkpoint,map_location='cpu'))
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checkpoint = torch.load(args.checkpoint ,map_location='cpu')
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model.load_state_dict(checkpoint['state_dict'])
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c_export(args, model)
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@ -4,7 +4,7 @@ parser = argparse.ArgumentParser()
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parser.add_argument('features', type=str, help='Features generated from dump_data')
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parser.add_argument('data', type=str, help='Data generated from dump_data (offset by 5ms)')
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parser.add_argument('output', type=str, help='output .f32 feature file with replaced neural pitch')
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parser.add_argument('pth_file', type=str, help='.pth file to use for pitch')
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parser.add_argument('checkpoint', type=str, help='model checkpoint file')
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parser.add_argument('path_lpcnet_extractor', type=str, help='path to LPCNet extractor object file (generated on compilation)')
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parser.add_argument('--device', type=str, help='compute device',default = None,required = False)
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parser.add_argument('--replace_xcorr', type = bool, default = False, help='Replace LPCNet xcorr with updated one')
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@ -26,21 +26,20 @@ if device is not None:
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device = torch.device(args.device)
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# Loading the appropriate model
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config_path = os.path.dirname(args.pth_file) + '/' + os.path.basename(args.pth_file).split('_')[0] + '_' + 'config_' + os.path.basename(args.pth_file).split('_')[-1][:-4] + '.json'
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with open(config_path) as json_file:
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dict_params = json.load(json_file)
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checkpoint = torch.load(args.checkpoint, map_location='cpu')
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dict_params = checkpoint['config']
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if dict_params['data_format'] == 'if':
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from models import large_if_ccode as model
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim'])
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elif dict_params['data_format'] == 'xcorr':
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from models import large_xcorr as model
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pitch_nn = model(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
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else:
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from models import large_joint as model
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']).to(device)
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pitch_nn = model(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
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pitch_nn.load_state_dict(torch.load(args.pth_file))
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pitch_nn.load_state_dict(checkpoint['state_dict'])
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pitch_nn = pitch_nn.to(device)
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N = dict_params['window_size']
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@ -3,6 +3,7 @@ Training the neural pitch estimator
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"""
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import os
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import argparse
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parser = argparse.ArgumentParser()
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@ -22,6 +23,7 @@ parser.add_argument('--output_dim', type=int, help='Output dimension',default =
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parser.add_argument('--learning_rate', type=float, help='Learning Rate',default = 1.0e-3,required = False)
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parser.add_argument('--epochs', type=int, help='Number of training epochs',default = 50,required = False)
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parser.add_argument('--choice_cel', type=str, help='Choice of Cross Entropy Loss (default or robust)',choices=['default','robust'],default = 'default',required = False)
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parser.add_argument('--prefix', type=str, help="prefix for model export, default: model", default='model')
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args = parser.parse_args()
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@ -163,12 +165,9 @@ choice_cel = args.choice_cel,
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context = args.context,
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)
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now = datetime.now()
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dir_pth_save = args.output_folder
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dir_network = dir_pth_save + str(now) + '_net_' + args.data_format + '.pth'
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dir_dictparams = dir_pth_save + str(now) + '_config_' + args.data_format + '.json'
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# Save Weights
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torch.save(pitch_nn.state_dict(), dir_network)
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# Save Config
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with open(dir_dictparams, 'w') as fp:
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json.dump(config, fp)
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model_save_path = os.path.join(args.output, f"{args.prefix}_{args.data_format}.pth")
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checkpoint = {
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'state_dict': pitch_nn.state_dict(),
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'config': config
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}
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torch.save(checkpoint, model_save_path)
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