"""
Evaluation script to compute the Raw Pitch Accuracy
Procedure:
- Look at all voiced frames in file
- Compute number of pitches in those frames that lie within a 50 cent threshold
RPA = (Total number of pitches within threshold summed across all files)/(Total number of voiced frames summed accross all files)
"""
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from prettytable import PrettyTable
import numpy as np
import glob
import random
import tqdm
import torch
import librosa
import json
from utils import stft, random_filter, feature_xform
import subprocess
import crepe
from models import PitchDNN, PitchDNNIF, PitchDNNXcorr
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def rca(reference,input,voicing,thresh = 25):
idx_voiced = np.where(voicing != 0)[0]
acc = np.where(np.abs(reference - input)[idx_voiced] < thresh)[0]
return acc.shape[0]
def sweep_rca(reference,input,voicing,thresh = 25,ind_arr = np.arange(-10,10)):
l = []
for i in ind_arr:
l.append(rca(reference,np.roll(input,i),voicing,thresh))
l = np.array(l)
return np.max(l)
def rpa(model,device = 'cpu',data_format = 'if'):
list_files = glob.glob('/home/ubuntu/Code/Datasets/SPEECH DATA/combined_mic_16k_raw/*.raw')
dir_f0 = '/home/ubuntu/Code/Datasets/SPEECH DATA/combine_f0_ptdb/'
# random_shuffle = list(np.random.permutation(len(list_files)))
random.shuffle(list_files)
list_files = list_files[:1000]
C_all = 0
C_all_m = 0
C_all_f = 0
list_rca_model_all = []
list_rca_male_all = []
list_rca_female_all = []
thresh = 50
N = 320
H = 160
freq_keep = 30
for idx in tqdm.trange(len(list_files)):
audio_file = list_files[idx]
file_name = os.path.basename(list_files[idx])[:-4]
audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1)
offset = 432
audio = audio[offset:]
rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160))
spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T
phase_diff = spec*np.conj(np.roll(spec,1,axis = -1))
phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8)
idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)])
feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T
feature_if = feature[:,idx_save]
data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+')
data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16)
subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32'])
feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1)
ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1)
feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1)
# feature_xcorr = feature_xform(feature_xcorr)
os.remove('./temp.raw')
os.remove('./temp_xcorr.f32')
if data_format == 'if':
feature = feature_if
elif data_format == 'xcorr':
feature = feature_xcorr
else:
indmin = min(feature_if.shape[0],feature_xcorr.shape[0])
feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1)
pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0"
pitch = np.loadtxt(pitch_file_name)[:,0]
voicing = np.loadtxt(pitch_file_name)[:,1]
indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0])
pitch = pitch[:indmin]
voicing = voicing[:indmin]
rmse = rmse[:indmin]
voicing = voicing*(rmse > 0.05*np.max(rmse))
if "mic_F" in audio_file:
idx_correct = np.where(pitch < 125)
voicing[idx_correct] = 0
cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int')
model_cents = model(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device))
model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy()
num_frames = min(cent.shape[0],model_cents.shape[0])
pitch = pitch[:num_frames]
cent = cent[:num_frames]
voicing = voicing[:num_frames]
model_cents = model_cents[:num_frames]
voicing_all = np.copy(voicing)
# Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model
force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True)
voicing_all[force_out_of_pitch] = 0
C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
list_rca_model_all.append(rca(cent,model_cents,voicing_all,thresh))
if "mic_M" in audio_file:
list_rca_male_all.append(rca(cent,model_cents,voicing_all,thresh))
C_all_m = C_all_m + np.where(voicing_all != 0)[0].shape[0]
else:
list_rca_female_all.append(rca(cent,model_cents,voicing_all,thresh))
C_all_f = C_all_f + np.where(voicing_all != 0)[0].shape[0]
list_rca_model_all = np.array(list_rca_model_all)
list_rca_male_all = np.array(list_rca_male_all)
list_rca_female_all = np.array(list_rca_female_all)
x = PrettyTable()
x.field_names = ["Experiment", "Mean RPA"]
x.add_row(["Both all pitches", np.sum(list_rca_model_all)/C_all])
x.add_row(["Male all pitches", np.sum(list_rca_male_all)/C_all_m])
x.add_row(["Female all pitches", np.sum(list_rca_female_all)/C_all_f])
print(x)
return None
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):
"""
Cycle through SNR evaluation for list of checkpoints
"""
list_files = glob.glob(ptdb_dataset_path + 'combined_mic_16k/*.raw')
dir_f0 = ptdb_dataset_path + 'combined_reference_f0/'
random.shuffle(list_files)
list_files = list_files[:(int)(fraction*len(list_files))]
dict_models = {}
list_snr.append(np.inf)
for f in checkpoint_list:
if (f!='crepe') and (f!='lpcnet'):
checkpoint = torch.load(f, map_location='cpu')
dict_params = checkpoint['config']
if dict_params['data_format'] == 'if':
from models import large_if_ccode as model
pitch_nn = PitchDNNIF(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim'])
elif dict_params['data_format'] == 'xcorr':
from models import large_xcorr as model
pitch_nn = PitchDNNXcorr(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
else:
from models import large_joint as model
pitch_nn = PitchDNN(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim'])
pitch_nn.load_state_dict(checkpoint['state_dict'])
N = dict_params['window_size']
H = dict_params['hop_factor']
freq_keep = dict_params['freq_keep']
list_mean = []
list_std = []
for snr_dB in list_snr:
C_all = 0
C_correct = 0
for idx in tqdm.trange(len(list_files)):
audio_file = list_files[idx]
file_name = os.path.basename(list_files[idx])[:-4]
audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1)
offset = 432
audio = audio[offset:]
rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = N,hop_length = H))
if noise_type != 'synthetic':
list_noisefiles = noise_dataset + '*.wav'
noise_file = random.choice(glob.glob(list_noisefiles))
n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1)
rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing
n = n[rand_range:rand_range + audio.shape[0]]
else:
n = np.random.randn(audio.shape[0])
n = random_filter(n)
snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10))
audio = audio + snr_multiplier*n
spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T
phase_diff = spec*np.conj(np.roll(spec,1,axis = -1))
phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8)
idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)])
feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T
feature_if = feature[:,idx_save]
data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+')
# data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16)
data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16)
subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32'])
feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1)
ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1)
feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1)
os.remove('./temp.raw')
os.remove('./temp_xcorr.f32')
if dict_params['data_format'] == 'if':
feature = feature_if
elif dict_params['data_format'] == 'xcorr':
feature = feature_xcorr
else:
indmin = min(feature_if.shape[0],feature_xcorr.shape[0])
feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1)
pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0"
pitch = np.loadtxt(pitch_file_name)[:,0]
voicing = np.loadtxt(pitch_file_name)[:,1]
indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0])
pitch = pitch[:indmin]
voicing = voicing[:indmin]
rmse = rmse[:indmin]
voicing = voicing*(rmse > 0.05*np.max(rmse))
if "mic_F" in audio_file:
idx_correct = np.where(pitch < 125)
voicing[idx_correct] = 0
cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int')
model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device))
model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy()
num_frames = min(cent.shape[0],model_cents.shape[0])
pitch = pitch[:num_frames]
cent = cent[:num_frames]
voicing = voicing[:num_frames]
model_cents = model_cents[:num_frames]
voicing_all = np.copy(voicing)
# Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model
force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True)
voicing_all[force_out_of_pitch] = 0
C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh)
list_mean.append(C_correct/C_all)
else:
fname = f
list_mean = []
list_std = []
for snr_dB in list_snr:
C_all = 0
C_correct = 0
for idx in tqdm.trange(len(list_files)):
audio_file = list_files[idx]
file_name = os.path.basename(list_files[idx])[:-4]
audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1)
offset = 432
audio = audio[offset:]
rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160))
if noise_type != 'synthetic':
list_noisefiles = noise_dataset + '*.wav'
noise_file = random.choice(glob.glob(list_noisefiles))
n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1)
rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing
n = n[rand_range:rand_range + audio.shape[0]]
else:
n = np.random.randn(audio.shape[0])
n = random_filter(n)
snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10))
audio = audio + snr_multiplier*n
if (f == 'crepe'):
_, model_frequency, _, _ = crepe.predict(np.concatenate([np.zeros(80),audio]), 16000, viterbi=True,center=True,verbose=0)
model_cents = 1200*np.log2(model_frequency/(16000/256) + 1.0e-8)
else:
data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+')
# data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16)
data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16)
subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32', './temp_period.f32'])
feature_xcorr = np.fromfile('./temp_period.f32', dtype='float32')
model_cents = 1200*np.log2((256/feature_xcorr + 1.0e-8) + 1.0e-8)
os.remove('./temp.raw')
os.remove('./temp_xcorr.f32')
os.remove('./temp_period.f32')
pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0"
pitch = np.loadtxt(pitch_file_name)[:,0]
voicing = np.loadtxt(pitch_file_name)[:,1]
indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0])
pitch = pitch[:indmin]
voicing = voicing[:indmin]
rmse = rmse[:indmin]
voicing = voicing*(rmse > 0.05*np.max(rmse))
if "mic_F" in audio_file:
idx_correct = np.where(pitch < 125)
voicing[idx_correct] = 0
cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int')
num_frames = min(cent.shape[0],model_cents.shape[0])
pitch = pitch[:num_frames]
cent = cent[:num_frames]
voicing = voicing[:num_frames]
model_cents = model_cents[:num_frames]
voicing_all = np.copy(voicing)
# Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model
force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True)
voicing_all[force_out_of_pitch] = 0
C_all = C_all + np.where(voicing_all != 0)[0].shape[0]
C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh)
list_mean.append(C_correct/C_all)
dict_models[fname] = {}
dict_models[fname]['list_SNR'] = list_mean[:-1]
dict_models[fname]['inf'] = list_mean[-1]
return dict_models