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使用 Wav2Vec2 进行强制对齐¶
作者 Moto Hira
本教程展示了如何使用CTC分割算法将转录与语音对齐，
torchaudio，该算法在
大型语料库的CTC分割用于德语端到端语音识别中进行了描述。
概述¶
对齐过程如下所示。
从音频波形估算帧级标签概率
生成表示时间步上标签对齐概率的网格矩阵。
从网格矩阵中找出最可能的路径。
在本示例中，我们使用 torchaudio 的 Wav2Vec2 模型进行声学特征提取。
准备¶
首先，我们导入必要的包，并获取我们要处理的数据。
# %matplotlib inline
import os
from dataclasses import dataclass
import torch
import torchaudio
import requests
import matplotlib
import matplotlib.pyplot as plt
import IPython
matplotlib.rcParams['figure.figsize'] = [16.0, 4.8]
torch.random.manual_seed(0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(torch.__version__)
print(torchaudio.__version__)
print(device)
SPEECH_URL = 'https://download.pytorch.org/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav'
SPEECH_FILE = '_assets/speech.wav'
if not os.path.exists(SPEECH_FILE):
os.makedirs('_assets', exist_ok=True)
with open(SPEECH_FILE, 'wb') as file:
file.write(requests.get(SPEECH_URL).content)
Out:
1.10.0+cpu
0.10.0+cpu
cpu
生成逐帧标签概率¶
第一步是生成每个音频帧的标签类别概率。我们可以使用为 ASR 训练的 Wav2Vec2 模型。这里我们使用
torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H()。
torchaudio 提供对带有相关标签的预训练模型的便捷访问。
注意
在后续部分，我们将在对数域中计算概率以避免数值不稳定。为此，我们将 emission 与 torch.log_softmax() 进行归一化。
bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H
model = bundle.get_model().to(device)
labels = bundle.get_labels()
with torch.inference_mode():
waveform, _ = torchaudio.load(SPEECH_FILE)
emissions, _ = model(waveform.to(device))
emissions = torch.log_softmax(emissions, dim=-1)
emission = emissions[0].cpu().detach()
可视化¶
print(labels)
plt.imshow(emission.T)
plt.colorbar()
plt.title("Frame-wise class probability")
plt.xlabel("Time")
plt.ylabel("Labels")
plt.show()
Out:
('<s>', '<pad>', '</s>', '<unk>', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
生成对齐概率（网格）¶
从发射矩阵出发，接下来我们生成格状图（trellis），它表示在每一时间帧上出现转录标签的概率。
Trellis 是一个具有时间轴和标签轴的二维矩阵。标签轴代表我们正在对齐的转录文本。在下文中，我们使用 \(t\) 表示时间轴的索引，使用 \(j\) 表示标签轴的索引。\(c_j\) 表示标签索引为 \(j\) 处的标签。
为了生成时间步 \(t+1\) 的概率，我们查看来自时间步 \(t\) 的格网以及时间步 \(t+1\) 的发射概率。有两条路径可以到达带有标签 \(c_{j+1}\) 的时间步 \(t+1\)。第一种情况是，在 \(t\) 时刻标签为 \(c_{j+1}\)，且从 \(t\) 到 \(t+1\) 期间标签未发生变化。另一种情况是，在 \(t\) 时刻标签为 \(c_j\)，并在 \(t+1\) 时刻转换到了下一个标签 \(c_{j+1}\)。
下图说明了这一转变。
由于我们要寻找最可能的状态转移，因此我们选择概率更高的路径作为 \(k_{(t+1, j+1)}\) 的值，即
\(k_{(t+1, j+1)} = max( k_{(t, j)} p(t+1, c_{j+1}), k_{(t, j+1)} p(t+1, repeat) )\)
其中 \(k\) 代表网格矩阵，而 \(p(t, c_j)\) 代表时间步 \(t\) 处标签 \(c_j\) 的概率。 \(repeat\) 代表来自 CTC 公式的空白标记。（有关 CTC 算法的详细信息，请参阅《使用 CTC 进行序列建模》 [distill.pub]）
transcript = 'I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT'
dictionary = {c: i for i, c in enumerate(labels)}
tokens = [dictionary[c] for c in transcript]
print(list(zip(transcript, tokens)))
def get_trellis(emission, tokens, blank_id=0):
num_frame = emission.size(0)
num_tokens = len(tokens)
# Trellis has extra diemsions for both time axis and tokens.
# The extra dim for tokens represents <SoS> (start-of-sentence)
# The extra dim for time axis is for simplification of the code.
trellis = torch.full((num_frame+1, num_tokens+1), -float('inf'))
trellis[:, 0] = 0
for t in range(num_frame):
trellis[t+1, 1:] = torch.maximum(
# Score for staying at the same token
trellis[t, 1:] + emission[t, blank_id],
# Score for changing to the next token
trellis[t, :-1] + emission[t, tokens],
)
return trellis
trellis = get_trellis(emission, tokens)
Out:
[('I', 10), ('|', 4), ('H', 11), ('A', 7), ('D', 14), ('|', 4), ('T', 6), ('H', 11), ('A', 7), ('T', 6), ('|', 4), ('C', 19), ('U', 16), ('R', 13), ('I', 10), ('O', 8), ('S', 12), ('I', 10), ('T', 6), ('Y', 22), ('|', 4), ('B', 24), ('E', 5), ('S', 12), ('I', 10), ('D', 14), ('E', 5), ('|', 4), ('M', 17), ('E', 5), ('|', 4), ('A', 7), ('T', 6), ('|', 4), ('T', 6), ('H', 11), ('I', 10), ('S', 12), ('|', 4), ('M', 17), ('O', 8), ('M', 17), ('E', 5), ('N', 9), ('T', 6)]
查找最可能的路径（回溯）¶
一旦生成网格，我们将沿着高概率元素对其进行遍历。
我们将从具有最高概率的时间步的最后一个标签索引开始，然后，我们逆时间遍历，根据后转换概率选择停留 (\(c_j \rightarrow c_j\)) 或转换 (\(c_j \rightarrow c_{j+1}\))，基于后转换概率 \(k_{t, j} p(t+1, c_{j+1})\) 或 \(k_{t, j+1} p(t+1, repeat)\)。
当标签到达起始位置时，过渡即完成。
Trellis 矩阵用于路径搜索，但对于每个片段的最终概率，我们采用来自发射矩阵的逐帧概率。
@dataclass
class Point:
token_index: int
time_index: int
score: float
def backtrack(trellis, emission, tokens, blank_id=0):
# Note:
# j and t are indices for trellis, which has extra dimensions
# for time and tokens at the beginning.
# When refering to time frame index `T` in trellis,
# the corresponding index in emission is `T-1`.
# Similarly, when refering to token index `J` in trellis,
# the corresponding index in transcript is `J-1`.
j = trellis.size(1) - 1
t_start = torch.argmax(trellis[:, j]).item()
path = []
for t in range(t_start, 0, -1):
# 1. Figure out if the current position was stay or change
# Note (again):
# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
# Score for token staying the same from time frame J-1 to T.
stayed = trellis[t-1, j] + emission[t-1, blank_id]
# Score for token changing from C-1 at T-1 to J at T.
changed = trellis[t-1, j-1] + emission[t-1, tokens[j-1]]
# 2. Store the path with frame-wise probability.
prob = emission[t-1, tokens[j-1] if changed > stayed else 0].exp().item()
# Return token index and time index in non-trellis coordinate.
path.append(Point(j-1, t-1, prob))
# 3. Update the token
if changed > stayed:
j -= 1
if j == 0:
break
else:
raise ValueError('Failed to align')
return path[::-1]
path = backtrack(trellis, emission, tokens)
print(path)
Out:
[Point(token_index=0, time_index=30, score=0.9999842643737793), Point(token_index=0, time_index=31, score=0.9846950173377991), Point(token_index=0, time_index=32, score=0.9999707937240601), Point(token_index=0, time_index=33, score=0.1540004163980484), Point(token_index=1, time_index=34, score=0.9999173879623413), Point(token_index=1, time_index=35, score=0.6080269813537598), Point(token_index=2, time_index=36, score=0.9997720122337341), Point(token_index=2, time_index=37, score=0.9997130036354065), Point(token_index=3, time_index=38, score=0.9999357461929321), Point(token_index=3, time_index=39, score=0.9861581325531006), Point(token_index=4, time_index=40, score=0.9238582253456116), Point(token_index=4, time_index=41, score=0.9257349967956543), Point(token_index=4, time_index=42, score=0.015662744641304016), Point(token_index=5, time_index=43, score=0.9998378753662109), Point(token_index=6, time_index=44, score=0.9988442659378052), Point(token_index=6, time_index=45, score=0.10144233703613281), Point(token_index=7, time_index=46, score=0.9999426603317261), Point(token_index=7, time_index=47, score=0.9999946355819702), Point(token_index=8, time_index=48, score=0.9979603290557861), Point(token_index=8, time_index=49, score=0.036036331206560135), Point(token_index=8, time_index=50, score=0.06162545830011368), Point(token_index=9, time_index=51, score=4.3326534068910405e-05), Point(token_index=10, time_index=52, score=0.999980092048645), Point(token_index=10, time_index=53, score=0.9967095851898193), Point(token_index=10, time_index=54, score=0.9999257326126099), Point(token_index=11, time_index=55, score=0.9999982118606567), Point(token_index=11, time_index=56, score=0.9990689158439636), Point(token_index=11, time_index=57, score=0.9999996423721313), Point(token_index=11, time_index=58, score=0.9999996423721313), Point(token_index=11, time_index=59, score=0.8457557559013367), Point(token_index=12, time_index=60, score=0.9999995231628418), Point(token_index=12, time_index=61, score=0.999601423740387), Point(token_index=13, time_index=62, score=0.999998927116394), Point(token_index=13, time_index=63, score=0.0035246757324784994), Point(token_index=13, time_index=64, score=1.0), Point(token_index=13, time_index=65, score=1.0), Point(token_index=14, time_index=66, score=0.9999916553497314), Point(token_index=14, time_index=67, score=0.9971591234207153), Point(token_index=14, time_index=68, score=0.9999990463256836), Point(token_index=14, time_index=69, score=0.9999991655349731), Point(token_index=14, time_index=70, score=0.9999998807907104), Point(token_index=14, time_index=71, score=0.9999998807907104), Point(token_index=14, time_index=72, score=0.9999881982803345), Point(token_index=14, time_index=73, score=0.011426654644310474), Point(token_index=15, time_index=74, score=0.9999978542327881), Point(token_index=15, time_index=75, score=0.9996134042739868), Point(token_index=15, time_index=76, score=0.999998927116394), Point(token_index=15, time_index=77, score=0.9727553129196167), Point(token_index=16, time_index=78, score=0.999998927116394), Point(token_index=16, time_index=79, score=0.9949328303337097), Point(token_index=16, time_index=80, score=0.999998927116394), Point(token_index=16, time_index=81, score=0.9999121427536011), Point(token_index=17, time_index=82, score=0.9999775886535645), Point(token_index=17, time_index=83, score=0.6576985716819763), Point(token_index=17, time_index=84, score=0.9984292387962341), Point(token_index=18, time_index=85, score=0.9999874830245972), Point(token_index=18, time_index=86, score=0.9993745684623718), Point(token_index=18, time_index=87, score=0.9999988079071045), Point(token_index=18, time_index=88, score=0.10424679517745972), Point(token_index=19, time_index=89, score=0.9999969005584717), Point(token_index=19, time_index=90, score=0.3978584110736847), Point(token_index=20, time_index=91, score=0.9999933242797852), Point(token_index=20, time_index=92, score=1.6990968561003683e-06), Point(token_index=20, time_index=93, score=0.9861307740211487), Point(token_index=21, time_index=94, score=0.9999960660934448), Point(token_index=21, time_index=95, score=0.9992727637290955), Point(token_index=21, time_index=96, score=0.9993411898612976), Point(token_index=22, time_index=97, score=0.9999983310699463), Point(token_index=22, time_index=98, score=0.9999971389770508), Point(token_index=22, time_index=99, score=0.9999997615814209), Point(token_index=22, time_index=100, score=0.9999995231628418), Point(token_index=23, time_index=101, score=0.9999732971191406), Point(token_index=23, time_index=102, score=0.9983227849006653), Point(token_index=23, time_index=103, score=0.9999992847442627), Point(token_index=23, time_index=104, score=0.9999997615814209), Point(token_index=23, time_index=105, score=1.0), Point(token_index=23, time_index=106, score=1.0), Point(token_index=23, time_index=107, score=0.9998630285263062), Point(token_index=24, time_index=108, score=0.9999982118606567), Point(token_index=24, time_index=109, score=0.9988579750061035), Point(token_index=25, time_index=110, score=0.9999798536300659), Point(token_index=25, time_index=111, score=0.8572984933853149), Point(token_index=26, time_index=112, score=0.9999847412109375), Point(token_index=26, time_index=113, score=0.9870278835296631), Point(token_index=26, time_index=114, score=1.904349664982874e-05), Point(token_index=27, time_index=115, score=0.9999794960021973), Point(token_index=27, time_index=116, score=0.9998253583908081), Point(token_index=28, time_index=117, score=0.9999991655349731), Point(token_index=28, time_index=118, score=0.9999734163284302), Point(token_index=28, time_index=119, score=0.0009004566818475723), Point(token_index=29, time_index=120, score=0.9993478655815125), Point(token_index=29, time_index=121, score=0.9975456595420837), Point(token_index=29, time_index=122, score=0.00030501981382258236), Point(token_index=30, time_index=123, score=0.9999344348907471), Point(token_index=30, time_index=124, score=6.0791257965320256e-06), Point(token_index=31, time_index=125, score=0.9833147525787354), Point(token_index=32, time_index=126, score=0.9974580407142639), Point(token_index=32, time_index=127, score=0.0008236187277361751), Point(token_index=33, time_index=128, score=0.9965153932571411), Point(token_index=33, time_index=129, score=0.0174646507948637), Point(token_index=34, time_index=130, score=0.9989169836044312), Point(token_index=35, time_index=131, score=0.9999698400497437), Point(token_index=35, time_index=132, score=0.9999842643737793), Point(token_index=36, time_index=133, score=0.9997640252113342), Point(token_index=36, time_index=134, score=0.5096558928489685), Point(token_index=37, time_index=135, score=0.9998302459716797), Point(token_index=37, time_index=136, score=0.08524401485919952), Point(token_index=37, time_index=137, score=0.0040728189051151276), Point(token_index=38, time_index=138, score=0.9999814033508301), Point(token_index=38, time_index=139, score=0.012057782150804996), Point(token_index=38, time_index=140, score=0.9999979734420776), Point(token_index=38, time_index=141, score=0.0005778099875897169), Point(token_index=39, time_index=142, score=0.9999068975448608), Point(token_index=39, time_index=143, score=0.9999960660934448), Point(token_index=39, time_index=144, score=0.9999980926513672), Point(token_index=40, time_index=145, score=0.9999915361404419), Point(token_index=40, time_index=146, score=0.9971170425415039), Point(token_index=40, time_index=147, score=0.9981803894042969), Point(token_index=41, time_index=148, score=0.9999310970306396), Point(token_index=41, time_index=149, score=0.9879505634307861), Point(token_index=41, time_index=150, score=0.9997628331184387), Point(token_index=42, time_index=151, score=0.9999535083770752), Point(token_index=43, time_index=152, score=0.9999716281890869), Point(token_index=44, time_index=153, score=0.6811745762825012)]
可视化¶
def plot_trellis_with_path(trellis, path):
# To plot trellis with path, we take advantage of 'nan' value
trellis_with_path = trellis.clone()
for i, p in enumerate(path):
trellis_with_path[p.time_index, p.token_index] = float('nan')
plt.imshow(trellis_with_path[1:, 1:].T, origin='lower')
plot_trellis_with_path(trellis, path)
plt.title("The path found by backtracking")
plt.show()
看起来不错。现在这条路径包含相同标签的重复项，因此让我们将它们合并，使其更接近原始转录稿。
在合并多个路径点时，我们直接对合并后的片段取平均概率。
# Merge the labels
@dataclass
class Segment:
label: str
start: int
end: int
score: float
def __repr__(self):
return f"{self.label}\t({self.score:4.2f}): [{self.start:5d}, {self.end:5d})"
@property
def length(self):
return self.end - self.start
def merge_repeats(path):
i1, i2 = 0, 0
segments = []
while i1 < len(path):
while i2 < len(path) and path[i1].token_index == path[i2].token_index:
i2 += 1
score = sum(path[k].score for k in range(i1, i2)) / (i2 - i1)
segments.append(Segment(transcript[path[i1].token_index], path[i1].time_index, path[i2-1].time_index + 1, score))
i1 = i2
return segments
segments = merge_repeats(path)
for seg in segments:
print(seg)
Out:
I (0.78): [ 30, 34)
| (0.80): [ 34, 36)
H (1.00): [ 36, 38)
A (0.99): [ 38, 40)
D (0.62): [ 40, 43)
| (1.00): [ 43, 44)
T (0.55): [ 44, 46)
H (1.00): [ 46, 48)
A (0.37): [ 48, 51)
T (0.00): [ 51, 52)
| (1.00): [ 52, 55)
C (0.97): [ 55, 60)
U (1.00): [ 60, 62)
R (0.75): [ 62, 66)
I (0.88): [ 66, 74)
O (0.99): [ 74, 78)
S (1.00): [ 78, 82)
I (0.89): [ 82, 85)
T (0.78): [ 85, 89)
Y (0.70): [ 89, 91)
| (0.66): [ 91, 94)
B (1.00): [ 94, 97)
E (1.00): [ 97, 101)
S (1.00): [ 101, 108)
I (1.00): [ 108, 110)
D (0.93): [ 110, 112)
E (0.66): [ 112, 115)
| (1.00): [ 115, 117)
M (0.67): [ 117, 120)
E (0.67): [ 120, 123)
| (0.50): [ 123, 125)
A (0.98): [ 125, 126)
T (0.50): [ 126, 128)
| (0.51): [ 128, 130)
T (1.00): [ 130, 131)
H (1.00): [ 131, 133)
I (0.75): [ 133, 135)
S (0.36): [ 135, 138)
| (0.50): [ 138, 142)
M (1.00): [ 142, 145)
O (1.00): [ 145, 148)
M (1.00): [ 148, 151)
E (1.00): [ 151, 152)
N (1.00): [ 152, 153)
T (0.68): [ 153, 154)
可视化¶
def plot_trellis_with_segments(trellis, segments, transcript):
# To plot trellis with path, we take advantage of 'nan' value
trellis_with_path = trellis.clone()
for i, seg in enumerate(segments):
if seg.label != '|':
trellis_with_path[seg.start+1:seg.end+1, i+1] = float('nan')
fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))
ax1.set_title("Path, label and probability for each label")
ax1.imshow(trellis_with_path.T, origin='lower')
ax1.set_xticks([])
for i, seg in enumerate(segments):
if seg.label != '|':
ax1.annotate(seg.label, (seg.start + .7, i + 0.3), weight='bold')
ax1.annotate(f'{seg.score:.2f}', (seg.start - .3, i + 4.3))
ax2.set_title("Label probability with and without repetation")
xs, hs, ws = [], [], []
for seg in segments:
if seg.label != '|':
xs.append((seg.end + seg.start) / 2 + .4)
hs.append(seg.score)
ws.append(seg.end - seg.start)
ax2.annotate(seg.label, (seg.start + .8, -0.07), weight='bold')
ax2.bar(xs, hs, width=ws, color='gray', alpha=0.5, edgecolor='black')
xs, hs = [], []
for p in path:
label = transcript[p.token_index]
if label != '|':
xs.append(p.time_index + 1)
hs.append(p.score)
ax2.bar(xs, hs, width=0.5, alpha=0.5)
ax2.axhline(0, color='black')
ax2.set_xlim(ax1.get_xlim())
ax2.set_ylim(-0.1, 1.1)
plot_trellis_with_segments(trellis, segments, transcript)
plt.tight_layout()
plt.show()
看起来不错。现在让我们合并单词。Wav2Vec2 模型使用 '|'
作为词边界，因此我们在每次出现
'|'之前合并片段。
最后，我们将原始音频分割为分段音频，并聆听这些片段以验证分割是否正确。
# Merge words
def merge_words(segments, separator='|'):
words = []
i1, i2 = 0, 0
while i1 < len(segments):
if i2 >= len(segments) or segments[i2].label == separator:
if i1 != i2:
segs = segments[i1:i2]
word = ''.join([seg.label for seg in segs])
score = sum(seg.score * seg.length for seg in segs) / sum(seg.length for seg in segs)
words.append(Segment(word, segments[i1].start, segments[i2-1].end, score))
i1 = i2 + 1
i2 = i1
else:
i2 += 1
return words
word_segments = merge_words(segments)
for word in word_segments:
print(word)
Out:
I (0.78): [ 30, 34)
HAD (0.84): [ 36, 43)
THAT (0.52): [ 44, 52)
CURIOSITY (0.89): [ 55, 91)
BESIDE (0.94): [ 94, 115)
ME (0.67): [ 117, 123)
AT (0.66): [ 125, 128)
THIS (0.70): [ 130, 138)
MOMENT (0.97): [ 142, 154)
可视化¶
def plot_alignments(trellis, segments, word_segments, waveform):
trellis_with_path = trellis.clone()
for i, seg in enumerate(segments):
if seg.label != '|':
trellis_with_path[seg.start+1:seg.end+1, i+1] = float('nan')
fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))
ax1.imshow(trellis_with_path[1:, 1:].T, origin='lower')
ax1.set_xticks([])
ax1.set_yticks([])
for word in word_segments:
ax1.axvline(word.start - 0.5)
ax1.axvline(word.end - 0.5)
for i, seg in enumerate(segments):
if seg.label != '|':
ax1.annotate(seg.label, (seg.start, i + 0.3))
ax1.annotate(f'{seg.score:.2f}', (seg.start , i + 4), fontsize=8)
# The original waveform
ratio = waveform.size(0) / (trellis.size(0) - 1)
ax2.plot(waveform)
for word in word_segments:
x0 = ratio * word.start
x1 = ratio * word.end
ax2.axvspan(x0, x1, alpha=0.1, color='red')
ax2.annotate(f'{word.score:.2f}', (x0, 0.8))
for seg in segments:
if seg.label != '|':
ax2.annotate(seg.label, (seg.start * ratio, 0.9))
xticks = ax2.get_xticks()
plt.xticks(xticks, xticks / bundle.sample_rate)
ax2.set_xlabel('time [second]')
ax2.set_yticks([])
ax2.set_ylim(-1.0, 1.0)
ax2.set_xlim(0, waveform.size(-1))
plot_alignments(trellis, segments, word_segments, waveform[0],)
plt.show()
# A trick to embed the resulting audio to the generated file.
# `IPython.display.Audio` has to be the last call in a cell,
# and there should be only one call par cell.
def display_segment(i):
ratio = waveform.size(1) / (trellis.size(0) - 1)
word = word_segments[i]
x0 = int(ratio * word.start)
x1 = int(ratio * word.end)
filename = f"_assets/{i}_{word.label}.wav"
torchaudio.save(filename, waveform[:, x0:x1], bundle.sample_rate)
print(f"{word.label} ({word.score:.2f}): {x0 / bundle.sample_rate:.3f} - {x1 / bundle.sample_rate:.3f} sec")
return IPython.display.Audio(filename)
# Generate the audio for each segment
print(transcript)
IPython.display.Audio(SPEECH_FILE)
Out:
I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT
display_segment(0)
Out:
I (0.78): 0.604 - 0.684 sec
display_segment(1)
Out:
HAD (0.84): 0.724 - 0.865 sec
display_segment(2)
Out:
THAT (0.52): 0.885 - 1.046 sec
display_segment(3)
Out:
CURIOSITY (0.89): 1.107 - 1.831 sec
display_segment(4)
Out:
BESIDE (0.94): 1.891 - 2.314 sec
display_segment(5)
Out:
ME (0.67): 2.354 - 2.474 sec
display_segment(6)
Out:
AT (0.66): 2.515 - 2.575 sec
display_segment(7)
Out:
THIS (0.70): 2.615 - 2.776 sec
display_segment(8)
Out:
MOMENT (0.97): 2.857 - 3.098 sec