注意
点击 这里 下载完整的示例代码
CTC强制对齐API教程¶
作者: Xiaohui Zhang, Moto Hira
警告
从 2.9 版本开始，TorchAudio 已进入维护阶段。因此，
本教程中描述的 API 在 2.8 版本中已弃用，并在 2.9 版本中被移除。
PyTorch 的音频和视频的解码和编码功能已合并到 TorchCodec 中。
请参阅 https://github.com/pytorch/audio/issues/3902 获取更多信息。
强制对齐是将文本与语音对齐的过程。本教程展示了如何使用 torchaudio.functional.forced_align() 来对齐文本和语音，该功能是随着 Scaling Speech Technology to 1,000+ Languages 项目开发的。
forced_align() 具有自定义的 CPU 和 CUDA 实现，比上面提供的原始 Python 实现更高效、更准确。它还可以处理带有特殊 <star> 标记的缺失文本。
还有一个高级 API torchaudio.pipelines.Wav2Vec2FABundle，它封装了本教程中解释的预处理和后处理，并简化了强制对齐的运行。 多语言数据的强制对齐 教程使用了这个 API 来演示如何对齐非英语文本。
准备¶
import torch
import torchaudio
print(torch.__version__)
print(torchaudio.__version__)
2.10.0.dev20251013+cu126
2.8.0a0+1d65bbe
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
cuda
import IPython
import matplotlib.pyplot as plt
import torchaudio.functional as F
首先，我们准备要使用的语音数据和文本。
SPEECH_FILE = torchaudio.utils._download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
waveform, _ = torchaudio.load(SPEECH_FILE)
TRANSCRIPT = "i had that curiosity beside me at this moment".split()
生成排放（Emissions）¶
forced_align() 接收排放和标记序列，并输出标记的时间戳和得分。
排放表示词元（token）的逐帧概率分布，可以通过将波形传递给声学模型来获得。
词元是文本的数值表示。有许多方法可以对文本进行分词，但在这里，我们只是将字母映射为整数，这与我们即将使用的声学模型在训练时构建标签的方式相同。
我们将使用预训练的 Wav2Vec2 模型 torchaudio.pipelines.MMS_FA 来获取排放并对文本进行分词。
bundle = torchaudio.pipelines.MMS_FA
model = bundle.get_model(with_star=False).to(device)
with torch.inference_mode():
emission, _ = model(waveform.to(device))
Downloading: "https://dl.fbaipublicfiles.com/mms/torchaudio/ctc_alignment_mling_uroman/model.pt" to /root/.cache/torch/hub/checkpoints/model.pt
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12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.5%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.6%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.7%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.8%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
12.9%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.0%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.1%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.2%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.3%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.4%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.5%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.6%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.7%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.8%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
13.9%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.0%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.1%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.2%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.3%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.5%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.6%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.7%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.8%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
14.9%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.0%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.1%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.2%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.3%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.4%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.5%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.6%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.7%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.8%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
15.9%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.0%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.1%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.2%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.3%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.4%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.5%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.6%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.8%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
16.9%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.0%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.1%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.2%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.3%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.4%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.5%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.6%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.7%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.8%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
17.9%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.0%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.1%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.2%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.3%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.4%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.5%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.6%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.7%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.8%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
18.9%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.0%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.1%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.2%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.3%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.4%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.5%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.7%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.8%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
19.9%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.0%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.1%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.2%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.3%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.4%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.5%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.6%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.7%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.8%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
20.9%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.0%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.1%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.2%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.4%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.5%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.6%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.7%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.8%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
21.9%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.0%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.1%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.2%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.3%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.4%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.5%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.6%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.7%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.8%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
22.9%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.0%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.1%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.2%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.3%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.4%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.5%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.6%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.7%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.8%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
23.9%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.0%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.1%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.2%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.3%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.4%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.5%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.6%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.7%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.8%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
24.9%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.0%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.1%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.2%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.3%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.4%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.5%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.6%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.7%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.8%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
25.9%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.0%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.1%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.3%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.5%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.7%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.8%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
26.9%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.0%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.1%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.2%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.3%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.4%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.5%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.6%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.7%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.8%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
27.9%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.0%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.1%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.2%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.3%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.4%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.5%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.6%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.7%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.8%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
28.9%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.0%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.1%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.2%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.3%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.4%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.5%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.6%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.7%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.8%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
29.9%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.0%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.1%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.2%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.3%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.4%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.6%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.7%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.8%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
30.9%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.0%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.1%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.2%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.3%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.4%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.5%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.6%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.7%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.8%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
31.9%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.0%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.1%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.2%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.3%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.4%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.5%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.6%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.7%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.8%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
32.9%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.0%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.1%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.2%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.3%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.4%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.5%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.6%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.7%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.8%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
33.9%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.1%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.2%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.3%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.4%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.5%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.6%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.7%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.8%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
34.9%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.0%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.1%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.2%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.3%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.4%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.5%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.6%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.7%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.8%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
35.9%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.1%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.2%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.3%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.4%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.5%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.6%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.7%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.8%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
36.9%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.0%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.1%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.2%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.3%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.4%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.5%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.6%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.7%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.8%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
37.9%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.0%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.1%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.2%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.3%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
38.4%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.5%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.6%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.7%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.8%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
38.9%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.0%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.1%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.2%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.3%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.4%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.5%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.6%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.7%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.8%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
39.9%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.0%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.1%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.2%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.3%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.4%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.5%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.6%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.7%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
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40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
41.0%
41.0%
41.0%
41.0%
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41.0%
41.0%
41.0%
41.0%
41.0%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
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41.1%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
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41.3%
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41.3%
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41.3%
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41.4%
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41.5%
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41.5%
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41.5%
41.6%
41.6%
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41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.6%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.7%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.8%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
41.9%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.0%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
42.1%
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42.1%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.2%
42.3%
42.3%
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42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.4%
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42.4%
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42.4%
42.5%
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42.5%
42.5%
42.5%
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42.5%
42.6%
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42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.6%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.7%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.8%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
42.9%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.0%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.1%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
43.2%
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43.2%
43.3%
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43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.4%
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43.4%
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43.4%
43.5%
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43.5%
43.5%
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43.5%
43.5%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.6%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.7%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.8%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
43.9%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.0%
44.1%
44.1%
44.1%
44.1%
44.1%
44.1%
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44.1%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
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44.3%
44.3%
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44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.4%
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44.4%
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44.5%
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44.6%
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44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.7%
44.7%
44.7%
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44.8%
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44.9%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.0%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.1%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
45.2%
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45.3%
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45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.4%
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45.4%
45.4%
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45.4%
45.4%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.5%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.6%
45.7%
45.7%
45.7%
45.7%
45.7%
45.7%
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45.7%
45.7%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.8%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
45.9%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.0%
46.1%
46.1%
46.1%
46.1%
46.1%
46.1%
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46.1%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
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46.3%
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46.3%
46.3%
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46.4%
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46.4%
46.4%
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46.5%
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46.6%
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46.6%
46.6%
46.6%
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46.6%
46.6%
46.7%
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47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
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47.0%
47.0%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
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47.1%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
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47.2%
47.3%
47.3%
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47.3%
47.3%
47.3%
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47.3%
47.4%
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47.4%
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47.5%
47.5%
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47.5%
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47.5%
47.5%
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47.5%
47.5%
47.6%
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47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.7%
47.7%
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47.8%
47.8%
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47.8%
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47.8%
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47.8%
47.8%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
47.9%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.1%
48.1%
48.1%
48.1%
48.1%
48.1%
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48.1%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
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48.2%
48.3%
48.3%
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48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.4%
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48.4%
48.5%
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48.6%
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48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.7%
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49.0%
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49.0%
49.1%
49.1%
49.1%
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49.2%
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49.3%
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49.4%
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49.5%
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49.6%
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49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.7%
49.7%
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49.7%
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49.8%
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49.8%
49.8%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
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49.9%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.1%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.2%
50.3%
50.3%
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50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.4%
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50.4%
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50.4%
50.5%
50.5%
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50.5%
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50.5%
50.6%
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50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.6%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
50.7%
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50.7%
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50.8%
50.8%
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50.8%
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50.9%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.0%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.1%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.2%
51.3%
51.3%
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51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.3%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.4%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.5%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.6%
51.7%
51.7%
51.7%
51.7%
51.7%
51.7%
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51.7%
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51.8%
51.8%
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51.8%
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51.8%
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52.0%
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52.0%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
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52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
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52.3%
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52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.4%
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52.4%
52.4%
52.4%
52.4%
52.4%
52.4%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.5%
52.6%
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52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.7%
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52.7%
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53.0%
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53.1%
53.1%
53.1%
53.1%
53.1%
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53.2%
53.2%
53.2%
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53.2%
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53.3%
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53.4%
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53.6%
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53.6%
53.6%
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53.7%
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53.9%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.0%
54.1%
54.1%
54.1%
54.1%
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54.2%
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54.2%
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54.2%
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54.3%
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54.4%
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54.4%
54.4%
54.4%
54.5%
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54.6%
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54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.7%
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54.7%
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54.7%
54.7%
54.7%
54.7%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.8%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
54.9%
55.0%
55.0%
55.0%
55.0%
55.0%
55.0%
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55.0%
55.0%
55.1%
55.1%
55.1%
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55.6%
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55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.7%
55.7%
55.7%
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55.7%
55.7%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.8%
55.9%
55.9%
55.9%
55.9%
55.9%
55.9%
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55.9%
55.9%
55.9%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.0%
56.1%
56.1%
56.1%
56.1%
56.1%
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56.2%
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57.0%
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57.0%
57.1%
57.1%
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57.1%
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57.2%
57.2%
57.2%
57.2%
57.2%
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57.2%
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57.3%
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57.3%
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57.7%
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57.8%
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57.8%
57.8%
57.9%
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57.9%
57.9%
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57.9%
57.9%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.0%
58.1%
58.1%
58.1%
58.1%
58.1%
58.1%
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58.2%
58.2%
58.2%
58.2%
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58.3%
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58.6%
58.6%
58.6%
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58.6%
58.6%
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58.9%
59.0%
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59.0%
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59.0%
59.0%
59.1%
59.1%
59.1%
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59.2%
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59.3%
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59.6%
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59.6%
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59.6%
59.7%
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59.8%
59.9%
59.9%
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59.9%
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59.9%
59.9%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.0%
60.1%
60.1%
60.1%
60.1%
60.1%
60.1%
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60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
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60.2%
60.3%
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60.3%
60.3%
60.3%
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60.3%
60.3%
60.4%
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60.6%
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60.7%
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61.0%
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61.1%
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61.2%
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61.3%
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61.6%
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61.6%
61.6%
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61.6%
61.6%
61.6%
61.7%
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62.0%
62.0%
62.0%
62.0%
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62.0%
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62.1%
62.1%
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62.2%
62.2%
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62.3%
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62.4%
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62.6%
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62.6%
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62.7%
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63.0%
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63.1%
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63.2%
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63.3%
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63.7%
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64.0%
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64.1%
64.1%
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64.2%
64.2%
64.2%
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64.3%
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64.4%
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64.5%
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64.6%
64.6%
64.6%
64.6%
64.7%
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64.9%
65.0%
65.0%
65.0%
65.0%
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65.0%
65.0%
65.0%
65.0%
65.0%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
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65.2%
65.2%
65.2%
65.2%
65.2%
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65.3%
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65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.4%
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65.6%
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65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.7%
65.7%
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65.8%
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65.9%
65.9%
65.9%
65.9%
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65.9%
65.9%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.0%
66.1%
66.1%
66.1%
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66.2%
66.2%
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66.3%
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66.3%
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66.4%
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66.4%
66.5%
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66.6%
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66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.7%
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67.0%
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67.1%
67.1%
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67.2%
67.2%
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67.2%
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67.3%
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67.4%
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67.6%
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67.6%
67.6%
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67.6%
67.7%
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67.8%
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67.9%
67.9%
67.9%
67.9%
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67.9%
67.9%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.0%
68.1%
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68.2%
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68.3%
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68.6%
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68.6%
68.6%
68.6%
68.6%
68.6%
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68.7%
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68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
68.9%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.0%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
69.1%
69.2%
69.2%
69.2%
69.2%
69.2%
69.2%
69.2%
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69.2%
69.3%
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69.3%
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69.3%
69.3%
69.3%
69.3%
69.4%
69.4%
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69.4%
69.5%
69.5%
69.5%
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69.5%
69.5%
69.5%
69.5%
69.5%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.6%
69.7%
69.7%
69.7%
69.7%
69.7%
69.7%
69.7%
69.7%
69.7%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.8%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
69.9%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.0%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.1%
70.2%
70.2%
70.2%
70.2%
70.2%
70.2%
70.2%
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70.2%
70.2%
70.3%
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70.3%
70.3%
70.3%
70.3%
70.3%
70.3%
70.4%
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70.4%
70.4%
70.4%
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70.5%
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70.5%
70.5%
70.6%
70.6%
70.6%
70.6%
70.6%
70.6%
70.6%
70.6%
70.6%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.7%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
70.8%
70.9%
70.9%
70.9%
70.9%
70.9%
70.9%
70.9%
70.9%
70.9%
70.9%
71.0%
71.0%
71.0%
71.0%
71.0%
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71.0%
71.0%
71.0%
71.0%
71.1%
71.1%
71.1%
71.1%
71.1%
71.1%
71.1%
71.1%
71.1%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.2%
71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.3%
71.4%
71.4%
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71.4%
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71.4%
71.4%
71.4%
71.4%
71.5%
71.5%
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71.5%
71.5%
71.5%
71.5%
71.5%
71.5%
71.5%
71.6%
71.6%
71.6%
71.6%
71.6%
71.6%
71.6%
71.6%
71.6%
71.7%
71.7%
71.7%
71.7%
71.7%
71.7%
71.7%
71.7%
71.7%
71.7%
71.8%
71.8%
71.8%
71.8%
71.8%
71.8%
71.8%
71.8%
71.8%
71.8%
71.9%
71.9%
71.9%
71.9%
71.9%
71.9%
71.9%
71.9%
71.9%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.0%
72.1%
72.1%
72.1%
72.1%
72.1%
72.1%
72.1%
72.1%
72.1%
72.1%
72.2%
72.2%
72.2%
72.2%
72.2%
72.2%
72.2%
72.2%
72.2%
72.3%
72.3%
72.3%
72.3%
72.3%
72.3%
72.3%
72.3%
72.3%
72.3%
72.4%
72.4%
72.4%
72.4%
72.4%
72.4%
72.4%
72.4%
72.4%
72.5%
72.5%
72.5%
72.5%
72.5%
72.5%
72.5%
72.5%
72.5%
72.5%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.6%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.7%
72.8%
72.8%
72.8%
72.8%
72.8%
72.8%
72.8%
72.8%
72.8%
72.8%
72.9%
72.9%
72.9%
72.9%
72.9%
72.9%
72.9%
72.9%
72.9%
72.9%
73.0%
73.0%
73.0%
73.0%
73.0%
73.0%
73.0%
73.0%
73.0%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.1%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
73.2%
73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.3%
73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.4%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.5%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.6%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.7%
73.8%
73.8%
73.8%
73.8%
73.8%
73.8%
73.8%
73.8%
73.8%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
73.9%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.0%
74.1%
74.1%
74.1%
74.1%
74.1%
74.1%
74.1%
74.1%
74.1%
74.2%
74.2%
74.2%
74.2%
74.2%
74.2%
74.2%
74.2%
74.2%
74.2%
74.3%
74.3%
74.3%
74.3%
74.3%
74.3%
74.3%
74.3%
74.3%
74.4%
74.4%
74.4%
74.4%
74.4%
74.4%
74.4%
74.4%
74.4%
74.4%
74.5%
74.5%
74.5%
74.5%
74.5%
74.5%
74.5%
74.5%
74.5%
74.5%
74.6%
74.6%
74.6%
74.6%
74.6%
74.6%
74.6%
74.6%
74.6%
74.7%
74.7%
74.7%
74.7%
74.7%
74.7%
74.7%
74.7%
74.7%
74.7%
74.8%
74.8%
74.8%
74.8%
74.8%
74.8%
74.8%
74.8%
74.8%
74.8%
74.9%
74.9%
74.9%
74.9%
74.9%
74.9%
74.9%
74.9%
74.9%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.0%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.1%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.2%
75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.3%
75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.4%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.5%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.6%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
75.7%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.8%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
75.9%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.0%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
76.1%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.2%
76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.3%
76.4%
76.4%
76.4%
76.4%
76.4%
76.4%
76.4%
76.4%
76.4%
76.4%
76.5%
76.5%
76.5%
76.5%
76.5%
76.5%
76.5%
76.5%
76.5%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.6%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.7%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.8%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
76.9%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.0%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.1%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.2%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.3%
77.4%
77.4%
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77.4%
77.4%
77.4%
77.4%
77.4%
77.4%
77.4%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.5%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.6%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.7%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.8%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
77.9%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.1%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.2%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.3%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.4%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.5%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.6%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.7%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.8%
78.9%
78.9%
78.9%
78.9%
78.9%
78.9%
78.9%
78.9%
78.9%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.0%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.1%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
79.2%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.3%
79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.4%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.5%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.6%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.7%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.8%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
79.9%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.0%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.1%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.2%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.3%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.4%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.6%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.7%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.8%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
80.9%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.0%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.1%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.2%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.3%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.4%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.5%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.6%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.7%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.8%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
81.9%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.0%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.1%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.2%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.3%
82.4%
82.4%
82.4%
82.4%
82.4%
82.4%
82.4%
82.4%
82.4%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.6%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.7%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.8%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
82.9%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.0%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.1%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.2%
83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.3%
83.4%
83.4%
83.4%
83.4%
83.4%
83.4%
83.4%
83.4%
83.4%
83.4%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.5%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.6%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.7%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.8%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
83.9%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.0%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.1%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.2%
84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.3%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.4%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.5%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.6%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
84.7%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.8%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
84.9%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.0%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.1%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.2%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.3%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.4%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.5%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.6%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.7%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.8%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
85.9%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.0%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.1%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.2%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.3%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.4%
86.5%
86.5%
86.5%
86.5%
86.5%
86.5%
86.5%
86.5%
86.5%
86.5%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.6%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.7%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.8%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
86.9%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.0%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.1%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.2%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.3%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.4%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.5%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.6%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.7%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.8%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
87.9%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.0%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.1%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.2%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.3%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.4%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.5%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.7%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.8%
88.9%
88.9%
88.9%
88.9%
88.9%
88.9%
88.9%
88.9%
88.9%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.0%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
89.1%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.2%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.3%
89.4%
89.4%
89.4%
89.4%
89.4%
89.4%
89.4%
89.4%
89.4%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.5%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.6%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.7%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.8%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
89.9%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.0%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.1%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.2%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.3%
90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.4%
90.5%
90.5%
90.5%
90.5%
90.5%
90.5%
90.5%
90.5%
90.5%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.6%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.7%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.8%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
90.9%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.0%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.1%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.2%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.3%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.4%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.5%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.6%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.7%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.8%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.0%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.1%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.2%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.3%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.4%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.5%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.7%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.8%
92.9%
92.9%
92.9%
92.9%
92.9%
92.9%
92.9%
92.9%
92.9%
93.0%
93.0%
93.0%
93.0%
93.0%
93.0%
93.0%
93.0%
93.0%
93.0%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.1%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.2%
93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.3%
93.4%
93.4%
93.4%
93.4%
93.4%
93.4%
93.4%
93.4%
93.4%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.5%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
93.7%
93.7%
93.7%
93.7%
93.7%
93.7%
93.7%
93.7%
93.7%
93.8%
93.8%
93.8%
93.8%
93.8%
93.8%
93.8%
93.8%
93.8%
93.8%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
93.9%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.0%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.1%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.2%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.3%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.4%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.5%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.6%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.7%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.8%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
94.9%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.0%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.1%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.2%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.3%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.4%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.5%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.6%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.7%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.8%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
95.9%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.0%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.1%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.2%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.3%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.4%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.5%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.6%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.7%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.8%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
96.9%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.0%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.1%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.2%
97.3%
97.3%
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对文本进行分词¶
我们创建一个字典，将每个标签映射到词元。
LABELS = bundle.get_labels(star=None)
DICTIONARY = bundle.get_dict(star=None)
for k, v in DICTIONARY.items():
print(f"{k}: {v}")
-: 0
a: 1
i: 2
e: 3
n: 4
o: 5
u: 6
t: 7
s: 8
r: 9
m: 10
k: 11
l: 12
d: 13
g: 14
h: 15
y: 16
b: 17
p: 18
w: 19
c: 20
v: 21
j: 22
z: 23
f: 24
': 25
q: 26
x: 27
将文本转换为词元非常简单，就像这样：
tokenized_transcript = [DICTIONARY[c] for word in TRANSCRIPT for c in word]
for t in tokenized_transcript:
print(t, end=" ")
print()
2 15 1 13 7 15 1 7 20 6 9 2 5 8 2 7 16 17 3 8 2 13 3 10 3 1 7 7 15 2 8 10 5 10 3 4 7
计算对齐¶
帧级对齐¶
现在我们调用 TorchAudio 的强制对齐 API 来计算帧级对齐。有关函数签名的详细信息，请参阅 forced_align()。
def align(emission, tokens):
targets = torch.tensor([tokens], dtype=torch.int32, device=device)
alignments, scores = F.forced_align(emission, targets, blank=0)
alignments, scores = alignments[0], scores[0] # remove batch dimension for simplicity
scores = scores.exp() # convert back to probability
return alignments, scores
aligned_tokens, alignment_scores = align(emission, tokenized_transcript)
/pytorch/audio/examples/tutorials/ctc_forced_alignment_api_tutorial.py:145: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
alignments, scores = F.forced_align(emission, targets, blank=0)
现在我们来看看输出。
for i, (ali, score) in enumerate(zip(aligned_tokens, alignment_scores)):
print(f"{i:3d}:\t{ali:2d} [{LABELS[ali]}], {score:.2f}")
0: 0 [-], 1.00
1: 0 [-], 1.00
2: 0 [-], 1.00
3: 0 [-], 1.00
4: 0 [-], 1.00
5: 0 [-], 1.00
6: 0 [-], 1.00
7: 0 [-], 1.00
8: 0 [-], 1.00
9: 0 [-], 1.00
10: 0 [-], 1.00
11: 0 [-], 1.00
12: 0 [-], 1.00
13: 0 [-], 1.00
14: 0 [-], 1.00
15: 0 [-], 1.00
16: 0 [-], 1.00
17: 0 [-], 1.00
18: 0 [-], 1.00
19: 0 [-], 1.00
20: 0 [-], 1.00
21: 0 [-], 1.00
22: 0 [-], 1.00
23: 0 [-], 1.00
24: 0 [-], 1.00
25: 0 [-], 1.00
26: 0 [-], 1.00
27: 0 [-], 1.00
28: 0 [-], 1.00
29: 0 [-], 1.00
30: 0 [-], 1.00
31: 0 [-], 1.00
32: 2 [i], 1.00
33: 0 [-], 1.00
34: 0 [-], 1.00
35: 15 [h], 1.00
36: 15 [h], 0.93
37: 1 [a], 1.00
38: 0 [-], 0.96
39: 0 [-], 1.00
40: 0 [-], 1.00
41: 13 [d], 1.00
42: 0 [-], 1.00
43: 0 [-], 0.97
44: 7 [t], 1.00
45: 15 [h], 1.00
46: 0 [-], 0.98
47: 1 [a], 1.00
48: 0 [-], 1.00
49: 0 [-], 1.00
50: 7 [t], 1.00
51: 0 [-], 1.00
52: 0 [-], 1.00
53: 0 [-], 1.00
54: 20 [c], 1.00
55: 0 [-], 1.00
56: 0 [-], 1.00
57: 0 [-], 1.00
58: 6 [u], 1.00
59: 6 [u], 0.96
60: 0 [-], 1.00
61: 0 [-], 1.00
62: 0 [-], 0.53
63: 9 [r], 1.00
64: 0 [-], 1.00
65: 2 [i], 1.00
66: 0 [-], 1.00
67: 0 [-], 1.00
68: 0 [-], 1.00
69: 0 [-], 1.00
70: 0 [-], 1.00
71: 0 [-], 0.96
72: 5 [o], 1.00
73: 0 [-], 1.00
74: 0 [-], 1.00
75: 0 [-], 1.00
76: 0 [-], 1.00
77: 0 [-], 1.00
78: 0 [-], 1.00
79: 8 [s], 1.00
80: 0 [-], 1.00
81: 0 [-], 1.00
82: 0 [-], 0.99
83: 2 [i], 1.00
84: 0 [-], 1.00
85: 7 [t], 1.00
86: 0 [-], 1.00
87: 0 [-], 1.00
88: 16 [y], 1.00
89: 0 [-], 1.00
90: 0 [-], 1.00
91: 0 [-], 1.00
92: 0 [-], 1.00
93: 17 [b], 1.00
94: 0 [-], 1.00
95: 3 [e], 1.00
96: 0 [-], 1.00
97: 0 [-], 1.00
98: 0 [-], 1.00
99: 0 [-], 1.00
100: 0 [-], 1.00
101: 8 [s], 1.00
102: 0 [-], 1.00
103: 0 [-], 1.00
104: 0 [-], 1.00
105: 0 [-], 1.00
106: 0 [-], 1.00
107: 0 [-], 1.00
108: 0 [-], 1.00
109: 0 [-], 0.65
110: 2 [i], 1.00
111: 0 [-], 1.00
112: 0 [-], 1.00
113: 13 [d], 1.00
114: 3 [e], 0.85
115: 0 [-], 1.00
116: 10 [m], 1.00
117: 0 [-], 1.00
118: 0 [-], 1.00
119: 3 [e], 1.00
120: 0 [-], 1.00
121: 0 [-], 1.00
122: 0 [-], 1.00
123: 0 [-], 1.00
124: 1 [a], 1.00
125: 0 [-], 1.00
126: 0 [-], 1.00
127: 7 [t], 1.00
128: 0 [-], 1.00
129: 7 [t], 1.00
130: 15 [h], 1.00
131: 0 [-], 0.79
132: 2 [i], 1.00
133: 0 [-], 1.00
134: 0 [-], 1.00
135: 0 [-], 1.00
136: 8 [s], 1.00
137: 0 [-], 1.00
138: 0 [-], 1.00
139: 0 [-], 1.00
140: 0 [-], 1.00
141: 10 [m], 1.00
142: 0 [-], 1.00
143: 0 [-], 1.00
144: 5 [o], 1.00
145: 0 [-], 1.00
146: 0 [-], 1.00
147: 0 [-], 1.00
148: 10 [m], 1.00
149: 0 [-], 1.00
150: 0 [-], 1.00
151: 3 [e], 1.00
152: 0 [-], 1.00
153: 4 [n], 1.00
154: 0 [-], 1.00
155: 7 [t], 1.00
156: 0 [-], 1.00
157: 0 [-], 1.00
158: 0 [-], 1.00
159: 0 [-], 1.00
160: 0 [-], 1.00
161: 0 [-], 1.00
162: 0 [-], 1.00
163: 0 [-], 1.00
164: 0 [-], 1.00
165: 0 [-], 1.00
166: 0 [-], 1.00
167: 0 [-], 1.00
168: 0 [-], 1.00
注意
对齐是以排放的帧坐标表示的，这与原始波形不同。
它包含空白词元（blank tokens）和重复词元。以下是对非空白词元的解释。
31: 0 [-], 1.00
32: 2 [i], 1.00 "i" starts and ends
33: 0 [-], 1.00
34: 0 [-], 1.00
35: 15 [h], 1.00 "h" starts
36: 15 [h], 0.93 "h" ends
37: 1 [a], 1.00 "a" starts and ends
38: 0 [-], 0.96
39: 0 [-], 1.00
40: 0 [-], 1.00
41: 13 [d], 1.00 "d" starts and ends
42: 0 [-], 1.00
注意
当空白词元后出现相同的词元时，它不被视为重复，而是被视为新的一次出现。
a a a b -> a b
a - - b -> a b
a a - b -> a b
a - a b -> a a b
^^^ ^^^
词元级对齐¶
下一步是解决重复问题，这样每个对齐就不会依赖于之前的对齐。torchaudio.functional.merge_tokens() 计算 TokenSpan 对象，该对象表示来自文本的哪个词元出现在什么时间段内。
token_spans = F.merge_tokens(aligned_tokens, alignment_scores)
print("Token\tTime\tScore")
for s in token_spans:
print(f"{LABELS[s.token]}\t[{s.start:3d}, {s.end:3d})\t{s.score:.2f}")
Token Time Score
i [ 32, 33) 1.00
h [ 35, 37) 0.96
a [ 37, 38) 1.00
d [ 41, 42) 1.00
t [ 44, 45) 1.00
h [ 45, 46) 1.00
a [ 47, 48) 1.00
t [ 50, 51) 1.00
c [ 54, 55) 1.00
u [ 58, 60) 0.98
r [ 63, 64) 1.00
i [ 65, 66) 1.00
o [ 72, 73) 1.00
s [ 79, 80) 1.00
i [ 83, 84) 1.00
t [ 85, 86) 1.00
y [ 88, 89) 1.00
b [ 93, 94) 1.00
e [ 95, 96) 1.00
s [101, 102) 1.00
i [110, 111) 1.00
d [113, 114) 1.00
e [114, 115) 0.85
m [116, 117) 1.00
e [119, 120) 1.00
a [124, 125) 1.00
t [127, 128) 1.00
t [129, 130) 1.00
h [130, 131) 1.00
i [132, 133) 1.00
s [136, 137) 1.00
m [141, 142) 1.00
o [144, 145) 1.00
m [148, 149) 1.00
e [151, 152) 1.00
n [153, 154) 1.00
t [155, 156) 1.00
词级对齐¶
现在，让我们将词元级对齐分组为词级对齐。
def unflatten(list_, lengths):
assert len(list_) == sum(lengths)
i = 0
ret = []
for l in lengths:
ret.append(list_[i : i + l])
i += l
return ret
word_spans = unflatten(token_spans, [len(word) for word in TRANSCRIPT])
音频预览¶
# Compute average score weighted by the span length
def _score(spans):
return sum(s.score * len(s) for s in spans) / sum(len(s) for s in spans)
def preview_word(waveform, spans, num_frames, transcript, sample_rate=bundle.sample_rate):
ratio = waveform.size(1) / num_frames
x0 = int(ratio * spans[0].start)
x1 = int(ratio * spans[-1].end)
print(f"{transcript} ({_score(spans):.2f}): {x0 / sample_rate:.3f} - {x1 / sample_rate:.3f} sec")
segment = waveform[:, x0:x1]
return IPython.display.Audio(segment.numpy(), rate=sample_rate)
num_frames = emission.size(1)
# Generate the audio for each segment
print(TRANSCRIPT)
IPython.display.Audio(SPEECH_FILE)
['i', 'had', 'that', 'curiosity', 'beside', 'me', 'at', 'this', 'moment']
preview_word(waveform, word_spans[0], num_frames, TRANSCRIPT[0])
i (1.00): 0.644 - 0.664 sec
preview_word(waveform, word_spans[1], num_frames, TRANSCRIPT[1])
had (0.98): 0.704 - 0.845 sec
preview_word(waveform, word_spans[2], num_frames, TRANSCRIPT[2])
that (1.00): 0.885 - 1.026 sec
preview_word(waveform, word_spans[3], num_frames, TRANSCRIPT[3])
curiosity (1.00): 1.086 - 1.790 sec
preview_word(waveform, word_spans[4], num_frames, TRANSCRIPT[4])
beside (0.97): 1.871 - 2.314 sec
preview_word(waveform, word_spans[5], num_frames, TRANSCRIPT[5])
me (1.00): 2.334 - 2.414 sec
preview_word(waveform, word_spans[6], num_frames, TRANSCRIPT[6])
at (1.00): 2.495 - 2.575 sec
preview_word(waveform, word_spans[7], num_frames, TRANSCRIPT[7])
this (1.00): 2.595 - 2.756 sec
preview_word(waveform, word_spans[8], num_frames, TRANSCRIPT[8])
moment (1.00): 2.837 - 3.138 sec
可视化¶
现在，让我们看看对齐结果，并将原始语音分割成单词。
def plot_alignments(waveform, token_spans, emission, transcript, sample_rate=bundle.sample_rate):
ratio = waveform.size(1) / emission.size(1) / sample_rate
fig, axes = plt.subplots(2, 1)
axes[0].imshow(emission[0].detach().cpu().T, aspect="auto")
axes[0].set_title("Emission")
axes[0].set_xticks([])
axes[1].specgram(waveform[0], Fs=sample_rate)
for t_spans, chars in zip(token_spans, transcript):
t0, t1 = t_spans[0].start + 0.1, t_spans[-1].end - 0.1
axes[0].axvspan(t0 - 0.5, t1 - 0.5, facecolor="None", hatch="/", edgecolor="white")
axes[1].axvspan(ratio * t0, ratio * t1, facecolor="None", hatch="/", edgecolor="white")
axes[1].annotate(f"{_score(t_spans):.2f}", (ratio * t0, sample_rate * 0.51), annotation_clip=False)
for span, char in zip(t_spans, chars):
t0 = span.start * ratio
axes[1].annotate(char, (t0, sample_rate * 0.55), annotation_clip=False)
axes[1].set_xlabel("time [second]")
axes[1].set_xlim([0, None])
fig.tight_layout()
plot_alignments(waveform, word_spans, emission, TRANSCRIPT)
blank 词元的处理不一致¶
在将词元级对齐分割成单词时，您会注意到一些空白词元被不同地处理，这使得结果的解释有些模糊。
当我们绘制得分时，很容易看出这一点。下图显示了单词区域和非单词区域，以及非空白词元的帧级得分。
def plot_scores(word_spans, scores):
fig, ax = plt.subplots()
span_xs, span_hs = [], []
ax.axvspan(word_spans[0][0].start - 0.05, word_spans[-1][-1].end + 0.05, facecolor="paleturquoise", edgecolor="none", zorder=-1)
for t_span in word_spans:
for span in t_span:
for t in range(span.start, span.end):
span_xs.append(t + 0.5)
span_hs.append(scores[t].item())
ax.annotate(LABELS[span.token], (span.start, -0.07))
ax.axvspan(t_span[0].start - 0.05, t_span[-1].end + 0.05, facecolor="mistyrose", edgecolor="none", zorder=-1)
ax.bar(span_xs, span_hs, color="lightsalmon", edgecolor="coral")
ax.set_title("Frame-level scores and word segments")
ax.set_ylim(-0.1, None)
ax.grid(True, axis="y")
ax.axhline(0, color="black")
fig.tight_layout()
plot_scores(word_spans, alignment_scores)
在此图中，空白词元是没有垂直条的突出显示区域。您可以看到，有些空白词元被解释为单词的一部分（标为红色），而另一些（标为蓝色）则不是。
原因之一是模型训练时没有词边界的标签。空白词元不仅被视为重复，也被视为单词之间的静默。
但这时，一个问题出现了。应该将紧邻单词结束或接近单词结尾的帧视为静默还是重复？
在上面的例子中，如果您回到之前的声谱图和单词区域图，您会发现在“curiosity”的“y”之后，多个频率频带仍然存在一些活动。
如果将该帧包含在单词中，是否会更准确？
不幸的是，CTC 并没有对此提供全面的解决方案。已知使用 CTC 训练的模型会表现出“峰值”响应，即它们倾向于在标签出现时产生峰值，但该峰值不会持续整个标签的持续时间。（注意：预训练的 Wav2Vec2 模型倾向于在标签出现开始时产生峰值，但这并非总是如此。）
[Zeyer et al., 2021] 对 CTC 的峰值行为进行了深入分析。我们鼓励感兴趣的读者参考该论文。以下是论文中的一段引述，正是我们在此面临的问题。
峰值行为在某些情况下可能存在问题， 例如，当应用程序需要不使用空白标签时， 例如，为了获得有意义的时间精确的音素 与转录的对齐。
高级：处理带有 <star> 标记的文本¶
现在，我们来看看当文本部分缺失时，如何使用能够建模任何词元的 <star> 标记来提高对齐质量。
这里我们使用上面相同的英语示例。但是，我们从文本中删除了开头的 “i had that curiosity beside me at”。使用这样的文本对齐音频会导致现有单词“this”的对齐错误。然而，通过使用 <star> 标记来建模缺失的文本，可以减轻这个问题。
首先，我们扩展字典以包含 <star> 标记。
DICTIONARY["*"] = len(DICTIONARY)
接下来，我们扩展排放张量，添加与 <star> 标记对应的额外维度。
star_dim = torch.zeros((1, emission.size(1), 1), device=emission.device, dtype=emission.dtype)
emission = torch.cat((emission, star_dim), 2)
assert len(DICTIONARY) == emission.shape[2]
plot_emission(emission[0])
以下函数将所有过程组合在一起，并一次性计算排放中的单词片段。
def compute_alignments(emission, transcript, dictionary):
tokens = [dictionary[char] for word in transcript for char in word]
alignment, scores = align(emission, tokens)
token_spans = F.merge_tokens(alignment, scores)
word_spans = unflatten(token_spans, [len(word) for word in transcript])
return word_spans
完整文本¶
word_spans = compute_alignments(emission, TRANSCRIPT, DICTIONARY)
plot_alignments(waveform, word_spans, emission, TRANSCRIPT)
/pytorch/audio/examples/tutorials/ctc_forced_alignment_api_tutorial.py:145: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
alignments, scores = F.forced_align(emission, targets, blank=0)
带有 <star> 标记的部分文本¶
现在，我们用 <star> 标记替换文本的第一部分。
transcript = "* this moment".split()
word_spans = compute_alignments(emission, transcript, DICTIONARY)
plot_alignments(waveform, word_spans, emission, transcript)
/pytorch/audio/examples/tutorials/ctc_forced_alignment_api_tutorial.py:145: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
alignments, scores = F.forced_align(emission, targets, blank=0)
preview_word(waveform, word_spans[0], num_frames, transcript[0])
* (1.00): 0.000 - 2.595 sec
preview_word(waveform, word_spans[1], num_frames, transcript[1])
this (1.00): 2.595 - 2.756 sec
preview_word(waveform, word_spans[2], num_frames, transcript[2])
moment (1.00): 2.837 - 3.138 sec
不带 <star> 标记的部分文本¶
作为比较，以下是对不使用 <star> 标记的部分文本的对齐。它演示了 <star> 标记在处理删除错误方面的效果。
transcript = "this moment".split()
word_spans = compute_alignments(emission, transcript, DICTIONARY)
plot_alignments(waveform, word_spans, emission, transcript)
/pytorch/audio/examples/tutorials/ctc_forced_alignment_api_tutorial.py:145: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
alignments, scores = F.forced_align(emission, targets, blank=0)
结论¶
在本教程中，我们探讨了如何使用 torchaudio 的强制对齐 API 来对齐和分割语音文件，并演示了一个高级用法：当存在文本错误时，引入 <star> 标记如何提高对齐精度。
致谢¶
感谢 Vineel Pratap 和 Zhaoheng Ni 开发并开源了强制对齐 API。
脚本总运行时间： ( 0 分 7.058 秒)
