CTC forced alignment API tutorial

Author: Xiaohui Zhang, Moto Hira

Warning

Starting with version 2.9, we have transitioned TorchAudio into a maintenance phase. As a result:

• The APIs described in this tutorial were deprecated in 2.8 and have been removed in 2.9.

• The decoding and encoding capabilities of PyTorch for both audio and video have been consolidated into TorchCodec.

Please see https://github.com/pytorch/audio/issues/3902 for more information.

The forced alignment is a process to align transcript with speech. This tutorial shows how to align transcripts to speech using torchaudio.functional.forced_align() which was developed along the work of Scaling Speech Technology to 1,000+ Languages.

forced_align() has custom CPU and CUDA implementations which are more performant than the vanilla Python implementation above, and are more accurate. It can also handle missing transcript with special <star> token.

There is also a high-level API, torchaudio.pipelines.Wav2Vec2FABundle, which wraps the pre/post-processing explained in this tutorial and makes it easy to run forced-alignments. Forced alignment for multilingual data uses this API to illustrate how to align non-English transcripts.

Preparation

import torch
import torchaudio

print(torch.__version__)
print(torchaudio.__version__)

2.14.0.dev20260802+cu130
2.11.0a0+2470b9c

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

First we prepare the speech data and the transcript we area going to use.

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()

Generating emissions

forced_align() takes emission and token sequences and outputs timestaps of the tokens and their scores.

Emission reperesents the frame-wise probability distribution over tokens, and it can be obtained by passing waveform to an acoustic model.

Tokens are numerical expression of transcripts. There are many ways to tokenize transcripts, but here, we simply map alphabets into integer, which is how labels were constructed when the acoustice model we are going to use was trained.

We will use a pre-trained Wav2Vec2 model, torchaudio.pipelines.MMS_FA, to obtain emission and tokenize the transcript.

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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12.8%
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%
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14.2%
14.2%
14.3%
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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%
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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%
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19.4%
19.4%
19.4%
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19.4%
19.5%
19.5%
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19.5%
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19.5%
19.5%
19.6%
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19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.6%
19.7%
19.7%
19.7%
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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%
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20.9%
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20.9%
20.9%
21.0%
21.0%
21.0%
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21.0%
21.0%
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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%
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21.2%
21.2%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.3%
21.4%
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21.4%
21.4%
21.4%
21.4%
21.4%
21.5%
21.5%
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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%
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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%
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26.1%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
26.2%
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26.3%
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26.3%
26.4%
26.4%
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26.4%
26.4%
26.4%
26.4%
26.4%
26.4%
26.5%
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26.5%
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26.5%
26.5%
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26.5%
26.5%
26.6%
26.6%
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26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.6%
26.7%
26.7%
26.7%
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26.7%
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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%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
40.9%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.0%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.1%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.2%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.3%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.4%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.5%
41.6%
41.6%
41.6%
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%
42.1%
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%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.3%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.4%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.5%
42.6%
42.6%
42.6%
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%
43.2%
43.2%
43.2%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.3%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.4%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
43.5%
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%
44.1%
44.1%
44.1%
44.1%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.2%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.3%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.4%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.5%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.6%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.7%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.8%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
44.9%
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%
45.2%
45.2%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.3%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
45.4%
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%
45.7%
45.7%
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%
46.1%
46.1%
46.1%
46.1%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.2%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.3%
46.4%
46.4%
46.4%
46.4%
46.4%
46.4%
46.4%
46.4%
46.4%
46.4%
46.5%
46.5%
46.5%
46.5%
46.5%
46.5%
46.5%
46.5%
46.5%
46.5%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.6%
46.7%
46.7%
46.7%
46.7%
46.7%
46.7%
46.7%
46.7%
46.7%
46.7%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.8%
46.9%
46.9%
46.9%
46.9%
46.9%
46.9%
46.9%
46.9%
46.9%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.0%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.1%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.2%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.3%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.4%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.5%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.6%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.7%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
47.8%
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%
48.1%
48.1%
48.1%
48.1%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.2%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.3%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.4%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.5%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.6%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.7%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.8%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
48.9%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.0%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.1%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.2%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.3%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.4%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.5%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.6%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.7%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.8%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
49.9%
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%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.3%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.4%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.5%
50.6%
50.6%
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%
50.7%
50.7%
50.7%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.8%
50.9%
50.9%
50.9%
50.9%
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50.9%
50.9%
50.9%
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%
51.3%
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%
51.7%
51.7%
51.7%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.8%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
51.9%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.0%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.1%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.2%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.3%
52.4%
52.4%
52.4%
52.4%
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%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.6%
52.7%
52.7%
52.7%
52.7%
52.7%
52.7%
52.7%
52.7%
52.7%
52.7%
52.8%
52.8%
52.8%
52.8%
52.8%
52.8%
52.8%
52.8%
52.8%
52.9%
52.9%
52.9%
52.9%
52.9%
52.9%
52.9%
52.9%
52.9%
52.9%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.0%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.1%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.2%
53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.3%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.4%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.5%
53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.6%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.7%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.8%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
53.9%
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%
54.1%
54.1%
54.1%
54.1%
54.1%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.2%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.3%
54.4%
54.4%
54.4%
54.4%
54.4%
54.4%
54.4%
54.4%
54.4%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.5%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.6%
54.7%
54.7%
54.7%
54.7%
54.7%
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%
55.0%
55.0%
55.0%
55.0%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.1%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.2%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.3%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.4%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.5%
55.6%
55.6%
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55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.6%
55.7%
55.7%
55.7%
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%
55.9%
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%
56.1%
56.1%
56.1%
56.1%
56.1%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.2%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.3%
56.4%
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56.4%
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56.5%
56.5%
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56.5%
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56.5%
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56.6%
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56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.6%
56.7%
56.7%
56.7%
56.7%
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56.7%
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56.7%
56.7%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.8%
56.9%
56.9%
56.9%
56.9%
56.9%
56.9%
56.9%
56.9%
56.9%
56.9%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.0%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.1%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.2%
57.3%
57.3%
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57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.3%
57.4%
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57.4%
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57.4%
57.4%
57.4%
57.4%
57.5%
57.5%
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57.5%
57.5%
57.5%
57.5%
57.5%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.6%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.7%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.8%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
57.9%
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%
58.1%
58.1%
58.1%
58.1%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
58.2%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.3%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.4%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.5%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.6%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.7%
58.8%
58.8%
58.8%
58.8%
58.8%
58.8%
58.8%
58.8%
58.8%
58.8%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
58.9%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.0%
59.1%
59.1%
59.1%
59.1%
59.1%
59.1%
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59.1%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
59.2%
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59.2%
59.3%
59.3%
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59.3%
59.3%
59.3%
59.3%
59.3%
59.3%
59.4%
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59.4%
59.4%
59.4%
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59.4%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.5%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.6%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
59.7%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.8%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
59.9%
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%
60.1%
60.1%
60.1%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.2%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.3%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.4%
60.5%
60.5%
60.5%
60.5%
60.5%
60.5%
60.5%
60.5%
60.5%
60.5%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.6%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.7%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.8%
60.9%
60.9%
60.9%
60.9%
60.9%
60.9%
60.9%
60.9%
60.9%
61.0%
61.0%
61.0%
61.0%
61.0%
61.0%
61.0%
61.0%
61.0%
61.0%
61.1%
61.1%
61.1%
61.1%
61.1%
61.1%
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61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
61.2%
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61.2%
61.3%
61.3%
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61.3%
61.3%
61.3%
61.3%
61.3%
61.3%
61.4%
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61.4%
61.4%
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61.4%
61.5%
61.5%
61.5%
61.5%
61.5%
61.5%
61.5%
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61.5%
61.5%
61.6%
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61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.6%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.7%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.8%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
61.9%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.0%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.1%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.2%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.3%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.4%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.5%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.6%
62.7%
62.7%
62.7%
62.7%
62.7%
62.7%
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62.7%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.8%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
62.9%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.0%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
63.1%
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63.1%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
63.2%
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63.2%
63.2%
63.3%
63.3%
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63.3%
63.3%
63.3%
63.3%
63.3%
63.3%
63.4%
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63.4%
63.4%
63.4%
63.4%
63.5%
63.5%
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63.6%
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63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.6%
63.7%
63.7%
63.7%
63.7%
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63.7%
63.7%
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63.8%
63.8%
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63.8%
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63.8%
63.8%
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63.9%
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63.9%
63.9%
64.0%
64.0%
64.0%
64.0%
64.0%
64.0%
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64.0%
64.0%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.1%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.2%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.3%
64.4%
64.4%
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64.4%
64.4%
64.4%
64.4%
64.4%
64.4%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
64.5%
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64.5%
64.5%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.6%
64.7%
64.7%
64.7%
64.7%
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64.7%
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64.7%
64.7%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.8%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
64.9%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.0%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.1%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.2%
65.3%
65.3%
65.3%
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65.3%
65.3%
65.3%
65.3%
65.3%
65.3%
65.4%
65.4%
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65.4%
65.4%
65.4%
65.4%
65.4%
65.4%
65.5%
65.5%
65.5%
65.5%
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65.5%
65.5%
65.5%
65.5%
65.5%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.6%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.7%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.8%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
65.9%
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%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.1%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.2%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.3%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.4%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.5%
66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.6%
66.7%
66.7%
66.7%
66.7%
66.7%
66.7%
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66.8%
66.8%
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66.8%
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66.8%
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66.9%
67.0%
67.0%
67.0%
67.0%
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67.0%
67.0%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
67.1%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.2%
67.3%
67.3%
67.3%
67.3%
67.3%
67.3%
67.3%
67.3%
67.3%
67.4%
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67.4%
67.4%
67.4%
67.4%
67.4%
67.4%
67.4%
67.4%
67.5%
67.5%
67.5%
67.5%
67.5%
67.5%
67.5%
67.5%
67.5%
67.5%
67.6%
67.6%
67.6%
67.6%
67.6%
67.6%
67.6%
67.6%
67.6%
67.7%
67.7%
67.7%
67.7%
67.7%
67.7%
67.7%
67.7%
67.7%
67.7%
67.8%
67.8%
67.8%
67.8%
67.8%
67.8%
67.8%
67.8%
67.8%
67.8%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
67.9%
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%
68.1%
68.1%
68.1%
68.1%
68.1%
68.1%
68.1%
68.1%
68.2%
68.2%
68.2%
68.2%
68.2%
68.2%
68.2%
68.2%
68.2%
68.2%
68.3%
68.3%
68.3%
68.3%
68.3%
68.3%
68.3%
68.3%
68.3%
68.3%
68.4%
68.4%
68.4%
68.4%
68.4%
68.4%
68.4%
68.4%
68.4%
68.5%
68.5%
68.5%
68.5%
68.5%
68.5%
68.5%
68.5%
68.5%
68.5%
68.6%
68.6%
68.6%
68.6%
68.6%
68.6%
68.6%
68.6%
68.6%
68.6%
68.7%
68.7%
68.7%
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68.8%
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68.8%
68.9%
68.9%
68.9%
68.9%
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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%
69.2%
69.2%
69.3%
69.3%
69.3%
69.3%
69.3%
69.3%
69.3%
69.3%
69.3%
69.3%
69.4%
69.4%
69.4%
69.4%
69.4%
69.4%
69.4%
69.4%
69.4%
69.4%
69.5%
69.5%
69.5%
69.5%
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%
70.2%
70.2%
70.2%
70.3%
70.3%
70.3%
70.3%
70.3%
70.3%
70.3%
70.3%
70.3%
70.4%
70.4%
70.4%
70.4%
70.4%
70.4%
70.4%
70.4%
70.4%
70.4%
70.5%
70.5%
70.5%
70.5%
70.5%
70.5%
70.5%
70.5%
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%
71.0%
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%
71.4%
71.4%
71.4%
71.4%
71.4%
71.4%
71.4%
71.5%
71.5%
71.5%
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%
77.4%
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%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.3%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.4%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.5%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.6%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
97.7%
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def plot_emission(emission):
    fig, ax = plt.subplots()
    ax.imshow(emission.cpu().T)
    ax.set_title("Frame-wise class probabilities")
    ax.set_xlabel("Time")
    ax.set_ylabel("Labels")
    fig.tight_layout()


plot_emission(emission[0])

Tokenize the transcript

We create a dictionary, which maps each label into token.

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

converting transcript to tokens is as simple as

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

Computing alignments

Frame-level alignments

Now we call TorchAudio’s forced alignment API to compute the frame-level alignment. For the detail of function signature, please refer to 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)

Now let’s look at the output.

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 [-], 0.64
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

Note

The alignment is expressed in the frame cordinate of the emission, which is different from the original waveform.

It contains blank tokens and repeated tokens. The following is the interpretation of the non-blank tokens.

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

Note

When same token occured after blank tokens, it is not treated as a repeat, but as a new occurrence.

a a a b -> a b
a - - b -> a b
a a - b -> a b
a - a b -> a a b
  ^^^       ^^^

Token-level alignments

Next step is to resolve the repetation, so that each alignment does not depend on previous alignments. torchaudio.functional.merge_tokens() computes the TokenSpan object, which represents which token from the transcript is present at what time span.

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

Word-level alignments

Now let’s group the token-level alignments into word-level alignments.

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])

Audio previews

# 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

Visualization

Now let’s look at the alignment result and segment the original speech into words.

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)

Inconsistent treatment of blank token

When splitting the token-level alignments into words, you will notice that some blank tokens are treated differently, and this makes the interpretation of the result somehwat ambigious.

This is easy to see when we plot the scores. The following figure shows word regions and non-word regions, with the frame-level scores of non-blank tokens.

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)

In this plot, the blank tokens are those highlighted area without vertical bar. You can see that there are blank tokens which are interpreted as part of a word (highlighted red), while the others (highlighted blue) are not.

One reason for this is because the model was trained without a label for the word boundary. The blank tokens are treated not just as repeatation but also as silence between words.

But then, a question arises. Should frames immediately after or near the end of a word be silent or repeat?

In the above example, if you go back to the previous plot of spectrogram and word regions, you see that after “y” in “curiosity”, there is still some activities in multiple frequency buckets.

Would it be more accurate if that frame was included in the word?

Unfortunately, CTC does not provide a comprehensive solution to this. Models trained with CTC are known to exhibit “peaky” response, that is, they tend to spike for an aoccurance of a label, but the spike does not last for the duration of the label. (Note: Pre-trained Wav2Vec2 models tend to spike at the beginning of label occurances, but this not always the case.)

[Zeyer et al., 2021] has in-depth alanysis on the peaky behavior of CTC. We encourage those who are interested understanding more to refer to the paper. The following is a quote from the paper, which is the exact issue we are facing here.

Peaky behavior can be problematic in certain cases, e.g. when an application requires to not use the blank label, e.g. to get meaningful time accurate alignments of phonemes to a transcription.

Advanced: Handling transcripts with <star> token

Now let’s look at when the transcript is partially missing, how can we improve alignment quality using the <star> token, which is capable of modeling any token.

Here we use the same English example as used above. But we remove the beginning text “i had that curiosity beside me at” from the transcript. Aligning audio with such transcript results in wrong alignments of the existing word “this”. However, this issue can be mitigated by using the <star> token to model the missing text.

First, we extend the dictionary to include the <star> token.

DICTIONARY["*"] = len(DICTIONARY)

Next, we extend the emission tensor with the extra dimension corresponding to the <star> token.

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])

The following function combines all the processes, and compute word segments from emission in one-go.

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

Full Transcript

word_spans = compute_alignments(emission, TRANSCRIPT, DICTIONARY)
plot_alignments(waveform, word_spans, emission, TRANSCRIPT)

Partial Transcript with <star> token

Now we replace the first part of the transcript with the <star> token.

transcript = "* this moment".split()
word_spans = compute_alignments(emission, transcript, DICTIONARY)
plot_alignments(waveform, word_spans, emission, transcript)

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

Partial Transcript without <star> token

As a comparison, the following aligns the partial transcript without using <star> token. It demonstrates the effect of <star> token for dealing with deletion errors.

transcript = "this moment".split()
word_spans = compute_alignments(emission, transcript, DICTIONARY)
plot_alignments(waveform, word_spans, emission, transcript)

Conclusion

In this tutorial, we looked at how to use torchaudio’s forced alignment API to align and segment speech files, and demonstrated one advanced usage: How introducing a <star> token could improve alignment accuracy when transcription errors exist.

Acknowledgement

Thanks to Vineel Pratap and Zhaoheng Ni for developing and open-sourcing the forced aligner API.