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from datasets import load_dataset
from tqdm.auto import tqdm
from speech_collator import SpeechCollator
import json
from torch.utils.data import DataLoader
import torch
from vocex import Vocex
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
vocex = Vocex.from_pretrained("cdminix/vocex")
dataset = load_dataset("libritts-r-aligned.py")
# Load the speaker2idx and phone2idx dictionaries
with open("data/speaker2idx.json", "r") as f:
speaker2idx = json.load(f)
idx2speaker = {v: k for k, v in speaker2idx.items()}
with open("data/phone2idx.json", "r") as f:
phone2idx = json.load(f)
idx2phone = {v: k for k, v in phone2idx.items()}
collator = SpeechCollator(
speaker2idx=speaker2idx,
phone2idx=phone2idx,
)
dataloader = DataLoader(
dataset["dev"],
batch_size=1,
shuffle=False,
collate_fn=collator.collate_fn,
num_workers=4,
)
def resample(x, vpw=5):
return np.interp(np.linspace(0, 1, vpw), np.linspace(0, 1, len(x)), x)
mean_pitchs = []
std_pitchs = []
mean_energys = []
std_energys = []
mean_durations = []
std_durations = []
for item in tqdm(dataloader):
result = vocex.model(item["mel"], inference=True)
pitch = result["measures"]["pitch"]
energy = result["measures"]["energy"]
va = result["measures"]["voice_activity_binary"]
mean_pitch = pitch.mean()
std_pitch = pitch.std()
mean_energy = energy.mean()
std_energy = energy.std()
durations = item["phone_durations"].squeeze().numpy()
durations = np.log(durations + 1)
mean_duration = durations.mean()
std_duration = durations.std()
mean_pitchs.append(mean_pitch)
std_pitchs.append(std_pitch)
mean_energys.append(mean_energy)
std_energys.append(std_energy)
mean_durations.append(mean_duration)
std_durations.append(std_duration)
mean_pitch = np.mean(mean_pitchs)
std_pitch = np.mean(std_pitchs)
mean_energy = np.mean(mean_energys)
std_energy = np.mean(std_energys)
mean_duration = np.mean(mean_durations)
std_duration = np.mean(std_durations)
# save the stats
stats = {
"mean_pitch": float(mean_pitch),
"std_pitch": float(std_pitch),
"mean_energy": float(mean_energy),
"std_energy": float(std_energy),
"mean_duration": float(mean_duration),
"std_duration": float(std_duration),
}
with open("data/stats.json", "w") as f:
json.dump(stats, f)
for item in tqdm(dataloader):
plt.figure(figsize=(20, 10))
plt.subplot(4, 1, 1)
plt.title("Mel spectrogram")
plt.imshow(item["mel"].squeeze().numpy().T, aspect="auto", origin="lower")
result = vocex.model(item["mel"], inference=True)
pitch = result["measures"]["pitch"]
energy = result["measures"]["energy"]
va = result["measures"]["voice_activity_binary"]
mean_pitch = pitch.mean()
std_pitch = pitch.std()
pitch = (pitch - pitch.mean()) / pitch.std()
mean_energy = energy.mean()
std_energy = energy.std()
energy = (energy - energy.mean()) / energy.std()
va = (va - 0.5) * 2
durations = item["phone_durations"].squeeze().numpy()
plt.subplot(4, 1, 2)
sns.lineplot(
x=np.arange(len(pitch[0])),
y=pitch[0],
color="red",
label="Pitch",
)
sns.lineplot(
x=np.arange(len(energy[0])),
y=energy[0],
color="blue",
label="Energy",
)
sns.lineplot(
x=np.arange(len(va[0])),
y=va[0],
color="green",
label="Voice activity",
)
plt.legend()
dur = [d for d in durations if d > 0]
current_idx = 0
vpw = 5 # values per window
new_repr = np.zeros((len(dur), vpw*3 + 1))
for i, d in enumerate(dur):
new_repr[i, 0] = d
# get values in duration window
pitch_win = pitch[0, current_idx:current_idx+d]
energy_win = energy[0, current_idx:current_idx+d]
va_win = va[0, current_idx:current_idx+d]
current_idx += d
# resample to vpw values
pitch_win = resample(pitch_win, vpw)
energy_win = resample(energy_win, vpw)
va_win = resample(va_win, vpw)
new_repr[i, 1:vpw+1] = pitch_win
new_repr[i, vpw+1:2*vpw+1] = energy_win
new_repr[i, 2*vpw+1:3*vpw+1] = va_win
new_repr[:, 0] = np.log(new_repr[:, 0] + 1)
mean_dur = new_repr[:, 0].mean()
std_dur = new_repr[:, 0].std()
new_repr[:, 0] = (new_repr[:, 0] - mean_dur) / std_dur
plt.subplot(4, 1, 3)
# heatmap with log scale
phones = [idx2phone[int(p)] for i, p in enumerate(item["phones"][0]) if item["phone_durations"][0][i] > 0]
for p_i, p in enumerate(phones):
if "[" in p:
# make empty symbol for phones with []
phones[p_i] = ""
sns.heatmap(new_repr.T, cmap="viridis")
# set xticks while making sure they are in the middle of the phone
plt.tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=True)
plt.xticks(np.arange(len(phones))+0.5, np.arange(len(phones)), rotation=0)
plt.yticks([0.5]+list(np.array([1,2,3])*(vpw)-vpw/2+1), ["Duration", "Pitch", "Energy", "Voice activity"], rotation=0)
plt.twiny()
plt.xticks(np.arange(len(phones))+0.5, phones, rotation=0)
plt.xlim(0, len(phones))
# allow some space between this plot and the next one
plt.subplots_adjust(hspace=0.5)
# reconstruct pitch, energy and va from new_repr
r_pitch = np.zeros(len(pitch[0]))
r_energy = np.zeros(len(energy[0]))
r_va = np.zeros(len(va[0]))
current_idx = 0
for i, d in enumerate(dur):
# get values in duration window
pitch_win = new_repr[i, 1:vpw+1]
energy_win = new_repr[i, vpw+1:2*vpw+1]
va_win = new_repr[i, 2*vpw+1:3*vpw+1]
# resample to d values
pitch_win = resample(pitch_win, d)
energy_win = resample(energy_win, d)
va_win = resample(va_win, d)
r_pitch[current_idx:current_idx+d] = pitch_win
r_energy[current_idx:current_idx+d] = energy_win
r_va[current_idx:current_idx+d] = va_win
current_idx += d
plt.subplot(4, 1, 4)
sns.lineplot(
x=np.arange(len(r_pitch)),
y=r_pitch,
color="red",
label="Pitch",
)
sns.lineplot(
x=np.arange(len(r_energy)),
y=r_energy,
color="blue",
label="Energy",
)
sns.lineplot(
x=np.arange(len(r_va)),
y=r_va,
color="green",
label="Voice activity",
)
plt.legend()
plt.savefig("test.png")
print("Mean pitch:", mean_pitch)
print("Std pitch:", std_pitch)
print("Mean energy:", mean_energy)
print("Std energy:", std_energy)
print("Mean duration:", mean_dur)
print("Std duration:", std_dur)
break
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