Merge pull request espnet#14 from espnet/master

Merge the latest ESPnet
chintu619 · Apr 13, 2022 · 0a1d05b · 0a1d05b
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -151,6 +151,11 @@ we recommend using small model parameters and avoiding dynamic imports, file acc
 more running time, you can annotate your test with `@pytest.mark.execution_timeout(sec)`.
 - For test initialization (parameters, modules, etc), you can use pytest fixtures. Refer to  [pytest fixtures](https://docs.pytest.org/en/latest/fixture.html#using-fixtures-from-classes-modules-or-projects) for more information.
 
+In addition, please follow the [PEP 8 convention](https://peps.python.org/pep-0008/) for the coding style and [Google's convention for docstrings](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods).
+Below are some specific points that should be taken care of in particular:
+- [import ordering](https://peps.python.org/pep-0008/#imports)
+- Avoid writing python2-style code. For example, `super().__init__()` is preferred over `super(CLASS_NAME, self).__init()__`.
+
 
 ### 4.2 Bash scripts
 

diff --git a/egs/README.md b/egs/README.md
@@ -49,7 +49,8 @@ See: https://espnet.github.io/espnet/tutorial.html
 | librispeech             | LibriSpeech ASR corpus                                       | ASR                                        | EN             | http://www.openslr.org/12                                    |                               |
 | libritts                | LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech | TTS                                      | EN             | http://www.openslr.org/60/                                   |                               |
 | ljspeech                | The LJ Speech Dataset                                        | TTS                                        | EN             | https://keithito.com/LJ-Speech-Dataset/                      |                               |
-| lrs                     | The Lip Reading Sentences Dataset                            | ASR/AVSR                                       | EN             | https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html                      |                               |
+| lrs2                     | The Lip Reading Sentences 2 Dataset                            | ASR                                       | ENG             | https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html                      |                               |
+| lrs                     | The Lip Reading Sentences 2 and 3 Dataset                            | AVSR                                       | ENG             | https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html  https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs3.html                     |                               |
 | m_ailabs                | The M-AILABS Speech Dataset                                  | TTS                                        | ~5 languages   | https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/    |
 | mucs_2021               | MUCS 2021: MUltilingual and Code-Switching ASR Challenges for Low Resource Indian Languages   | ASR/Code Switching          | HI, MR, OR, TA, TE, GU, HI-EN, BN-EN | https://navana-tech.github.io/MUCS2021/data.html                    |                               |
 | mtedx                   | Multilingual TEDx | ASR/Machine Translation/Speech Translation | 13 Language pairs | http://www.openslr.org/100/                         |

diff --git a/egs/lrs/README.md b/egs/lrs/README.md
diff --git a/egs/lrs/avsr1/RESULTS.md b/egs/lrs/avsr1/RESULTS.md
@@ -0,0 +1,294 @@
+## pretrain_Train_pytorch_audio_delta_specaug (Audio-Only)
+
+* Model files (archived to model.tar.gz by <code>$ pack_model.sh</code>)
+  - download link: <code>https://drive.google.com/file/d/1ITgdZoa8vQ7lDwi1jLziYGXOyUtgE2ow/view</code>
+  - training config file: <code>conf/train.yaml</code>
+  - decoding config file: <code>conf/decode.yaml</code>
+  - preprocess config file: <code>conf/specaug.yaml</code>
+  - lm config file: <code>conf/lm.yaml</code> 
+  - cmvn file: <code>data/train/cmvn.ark</code>
+  - e2e file: <code>exp/audio/model.last10.avg.best</code>
+  - e2e json file: <code>exp/audio/model.json</code>
+  - lm file: <code>exp/train_rnnlm_pytorch_lm_unigram500/rnnlm.model.best</code>
+  - lm JSON file: <code>exp/train_rnnlm_pytorch_lm_unigram500/model.json</code>
+  - dict file: <code>data/lang_char/train_unigram500_units.txt</code>
+
+## Environments
+- date: `Mon Feb 21 11:52:07 UTC 2022`
+- python version: `3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]`
+- espnet version: `espnet 0.6.0`
+- chainer version: `chainer 6.0.0`
+- pytorch version: `pytorch 1.0.1.post2`
+
+### CER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|music noise|-12|171|1669|82.0|11.2|6.8|2.2|20.3|38.6|
+||-9|187|1897|87.0|8.3|4.7|0.8|13.8|33.2|
+||-6|176|1821|92.0|5.5|2.5|1.1|9.1|26.7|
+||-3|201|2096|94.4|2.2|3.3|0.2|5.8|20.4|
+||0|158|1611|95.0|3.0|2.0|0.4|5.4|19.0|
+||3|173|1710|94.7|2.7|2.6|0.4|5.7|24.9|
+||6|185|1920|96.2|1.8|2.0|0.5|4.3|17.8|
+||9|157|1533|97.6|1.0|1.4|0.5|2.9|13.4|
+||12|150|1536|96.4|1.6|2.1|0.3|4.0|20.7|
+||clean|138|1390|96.7|1.4|1.9|0.4|3.7|17.4|
+||reverb|177|1755|93.7|3.6|2.7|0.7|7.0|23.2|
+|ambient noise|-12|187|1873|76.4|16.3|7.3|2.3|25.9|51.9|
+||-9 |193|1965|84.2|10.3|5.4|1.8|17.6|40.4|
+||-6 |176|1883|90.2|5.8|4.0|1.3|11.2|26.1|
+||-3 |173|1851|91.2|4.8|4.0|1.0|9.8|32.9|
+|| 0 |148|1470|94.8|3.0|2.2|0.7|5.9|23.6|
+|| 3 |176|1718|96.0|2.1|1.9|0.3|4.3|17.0|
+|| 6 |166|1714|93.7|2.9|3.4|0.5|6.8|20.5|
+|| 9 |170|1601|96.9|1.5|1.6|0.3|3.4|18.2|
+||12 |169|1718|95.9|2.5|1.6|0.2|4.3|20.1|
+||clean |138|1390|96.7|1.4|1.9|0.4|3.7|17.4|
+||reverb |177|1755|93.7|3.6|2.7|0.7|7.0|23.2|
+
+### WER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|music noise|-12|171|912|83.4|12.5|4.1|2.4|19.0|38.6|
+||-9 |187|1005|87.6|8.6|3.9|1.9|14.3|33.2|
+||-6 |176|951|90.6|5.9|3.5|0.8|10.2|26.7|
+||-3 |201|1097|94.4|3.3|2.3|0.6|6.2|20.4|
+|| 0 |158|847|94.9|3.2|1.9|0.4|5.4|19.0|
+|| 3 |173|884|94.2|3.8|1.9|0.6|6.3|24.9|
+|| 6 |185|997|96.3|2.7|1.0|0.7|4.4|17.8|
+|| 9 |157|817|96.9|1.7|1.3|0.4|3.4|13.4|
+||12 |150|832|95.2|2.9|1.9|0.5|5.3|20.7|
+||clean |138|739|95.7|2.4|1.9|0.4|4.7|17.4|
+||reverb |177|943|93.6|4.0|2.3|0.4|6.8|23.2|
+|ambient noise|-12|187|995|73.7|18.4|7.9|1.7|28.0|51.9|
+||-9 |193|1060|83.0|11.7|5.3|1.4|18.4|40.4|
+||-6 |176|971|90.2|6.8|3.0|1.4|11.2|26.1|
+||-3 |173|972|90.0|6.9|3.1|1.0|11.0|32.9|
+|| 0 |148|838|94.0|4.1|1.9|0.4|6.3|23.6|
+|| 3 |176|909|95.5|2.9|1.7|0.3|4.8|17.0|
+|| 6 |166|830|94.1|3.3|2.7|1.0|6.9|20.5|
+|| 9 |170|872|95.4|3.1|1.5|0.2|4.8|18.2|
+||12 |169|895|95.0|4.0|1.0|0.2|5.3|20.1|
+||clean |138|739|95.7|2.4|1.9|0.4|4.7|17.4|
+||reverb |177|943|93.6|4.0|2.3|0.4|6.8|23.2|
+
+## Train_pytorch_trainvideo_delta_specaug (Video-Only)
+
+* Model files (archived to model.tar.gz by <code>$ pack_model.sh</code>)
+  - download link: <code>https://drive.google.com/file/d/1ZXXCXSbbFS2PDlrs9kbJL9pE6-5nPPxi/view</code>
+  - training config file: <code>conf/finetunevideo/trainvideo.yaml</code>
+  - decoding config file: <code>conf/decode.yaml</code>
+  - preprocess config file: <code>conf/specaug.yaml</code>
+  - lm config file: <code>conf/lm.yaml</code> 
+  - e2e file: <code>exp/vfintune/model.last10.avg.best</code>
+  - e2e json file: <code>exp/vfintune/model.json</code>
+  - lm file: <code>exp/train_rnnlm_pytorch_lm_unigram500/rnnlm.model.best</code>
+  - lm JSON file: <code>exp/train_rnnlm_pytorch_lm_unigram500/model.json</code>
+  - dict file: <code>data/lang_char/train_unigram500_units.txt</code>
+
+## Environments
+- date: `Mon Feb 21 11:52:07 UTC 2022`
+- python version: `3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]`
+- espnet version: `espnet 0.6.0`
+- chainer version: `chainer 6.0.0`
+- pytorch version: `pytorch 1.0.1.post2`
+
+
+### CER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|clean visual data|171|1669|42.3|42.5|15.2|6.4|64.1|91.8|
+||-9 |187|1897|46.4|38.8|14.8|8.5|62.2|90.9|
+||-6 |176|1821|48.1|37.7|14.2|9.2|61.1|92.0|
+||-3 |201|2096|41.7|46.4|11.9|8.9|67.2|90.0|
+|| 0 |158|1611|43.4|42.6|14.0|7.1|63.7|94.9|
+|| 3 |173|1710|49.2|37.6|13.2|8.9|59.7|91.9|
+|| 6 |185|1920|39.3|45.6|15.2|9.4|70.2|95.1|
+|| 9 |157|1533|46.2|39.1|14.7|8.5|62.3|89.2|
+||12 |150|1536|49.5|37.6|12.9|7.2|57.7|87.3|
+||clean |138|1390|44.2|42.3|13.5|7.8|63.7|92.8|
+||reverb |177|1755|44.8|41.5|13.6|7.5|62.7|92.1|
+|visual gaussian blur|-12|187|1873|37.3|46.6|16.1|9.0|71.6|93.0|
+||-9 |193|1965|43.0|44.1|13.0|11.0|68.1|93.8|
+||-6 |176|1883|39.9|43.3|16.7|7.5|67.6|93.8|
+||-3 |173|1851|43.7|43.8|12.5|8.2|64.5|91.9|
+|| 0 |148|1470|42.3|45.4|12.3|8.2|65.9|93.9|
+|| 3 |176|1718|44.8|41.5|13.7|7.9|63.1|89.2|
+|| 6 |166|1714|38.5|45.4|16.0|10.7|72.2|94.6|
+|| 9 |170|1601|45.1|42.8|12.1|11.7|66.6|91.2|
+||12 |169|1718|42.0|40.1|17.9|8.2|66.2|92.3|
+||clean |138|1390|40.4|45.5|14.2|8.7|68.3|93.5|
+||reverb |177|1755|40.2|45.6|14.2|8.5|68.3|92.7|
+|visual salt and pepper noise|-12|187|1873|36.2|48.1|15.8|9.9|73.7|92.0|
+||-9 |193|1965|41.7|44.6|13.7|10.6|68.9|92.7|
+||-6 |176|1883|36.5|47.2|16.4|8.6|72.1|93.2|
+||-3 |173|1851|42.1|45.4|12.5|10.8|68.6|92.5|
+|| 0 |148|1470|42.3|45.1|12.6|9.5|67.2|91.9|
+|| 3 |176|1718|40.0|45.1|15.0|7.6|67.6|92.0|
+|| 6 |166|1714|38.1|45.2|16.7|10.1|72.0|94.0|
+|| 9 |170|1601|40.2|45.9|13.9|12.0|71.8|92.9|
+||12 |169|1718|37.5|46.8|15.7|8.7|71.2|94.1|
+||clean |138|1390|39.9|46.0|14.0|9.1|69.1|92.8|
+||reverb |177|1755|39.9|46.2|13.9|9.1|69.2|92.7|
+
+### WER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|clean visual data|-12|171|912|39.4|42.7|18.0|4.3|64.9|89.5|
+||-9 |187|1005|43.7|40.6|15.7|5.4|61.7|86.1|
+||-6 |176|951|43.3|42.6|14.1|4.1|60.8|88.6|
+||-3 |201|1097|41.3|44.2|14.5|5.3|64.0|85.6|
+|| 0 |158|847|44.3|37.8|17.9|6.1|61.9|85.4|
+|| 3 |173|884|44.2|39.7|16.1|5.3|61.1|84.4|
+|| 6 |185|997|38.2|44.8|17.0|3.9|65.7|84.9|
+|| 9 |157|817|47.9|37.1|15.1|5.5|57.6|80.3|
+||12 |150|832|42.9|37.6|19.5|5.3|62.4|84.0|
+||clean |138|739|45.9|39.1|15.0|5.3|59.4|85.5|
+||reverb |177|943|43.4|40.5|16.1|5.3|61.9|85.9|
+|visual Gaussian blur|-12|187|995|35.9|45.4|18.7|5.3|69.4|86.6|
+||-9 |193|1060|35.0|44.2|20.8|5.0|70.0|92.2|
+||-6 |176|971|38.2|43.2|18.6|4.6|66.4|87.5|
+||-3 |173|972|37.9|45.5|16.7|4.8|67.0|86.1|
+|| 0 |148|838|38.1|40.7|21.2|4.2|66.1|89.2|
+|| 3 |176|909|36.0|48.5|15.5|5.9|70.0|88.6|
+|| 6 |166|830|36.7|46.6|16.6|6.1|69.4|89.8|
+|| 9 |170|872|39.0|45.5|15.5|4.7|65.7|87.6|
+||12 |169|895|35.2|46.8|18.0|4.6|69.4|89.9|
+||clean |138|739|40.7|42.2|17.1|5.0|64.3|88.4|
+||reverb |177|943|38.0|44.3|17.7|5.0|67.0|89.3|
+|visual salt and pepper noise|-12|187|995|32.5|48.9|18.6|4.6|72.2|83.4|
+||-9 |193|1060|32.3|51.5|16.2|6.1|73.9|92.2|
+||-6 |176|971|36.5|47.3|16.3|7.2|70.8|86.4|
+||-3 |173|972|35.5|47.2|17.3|4.6|69.1|88.4|
+|| 0 |148|838|36.9|41.5|21.6|3.7|66.8|88.5|
+|| 3 |176|909|33.0|51.9|15.1|5.4|72.4|88.6|
+|| 6 |166|830|35.3|49.9|14.8|8.8|73.5|88.0|
+|| 9 |170|872|41.2|43.3|15.5|5.6|64.4|84.7|
+||12 |169|895|34.2|47.8|18.0|7.3|73.1|91.1|
+||clean |138|739|37.5|47.8|14.7|7.3|69.8|86.2|
+||reverb |177|943|35.9|47.9|16.1|6.7|70.7|87.0|
+
+## Train_pytorch_trainavs_delta_specaug (Audio-Visual)
+
+* Model files (archived to model.tar.gz by <code>$ pack_model.sh</code>)
+  - download link: <code>https://drive.google.com/file/d/1ZXXCXSbbFS2PDlrs9kbJL9pE6-5nPPxi/view</code>
+  - training config file: <code>conf/finetuneav/trainavs.yaml</code>
+  - decoding config file: <code>conf/decode.yaml</code>
+  - preprocess config file: <code>conf/specaug.yaml</code>
+  - lm config file: <code>conf/lm.yaml</code> 
+  - cmvn file: <code>data/train/cmvn.ark</code>
+  - e2e file: <code>exp/avfintune/model.last10.avg.best</code>
+  - e2e json file: <code>exp/avfintune/model.json</code>
+  - lm file: <code>exp/train_rnnlm_pytorch_lm_unigram500/rnnlm.model.best</code>
+  - lm JSON file: <code>exp/train_rnnlm_pytorch_lm_unigram500/model.json</code>
+  - dict file: <code>data/lang_char/train_unigram500_units.txt</code>
+
+## Environments
+- date: `Mon Feb 21 11:52:07 UTC 2022`
+- python version: `3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]`
+- espnet version: `espnet 0.6.0`
+- chainer version: `chainer 6.0.0`
+- pytorch version: `pytorch 1.0.1.post2`
+
+
+### CER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|music noise with clean visual data |-12|171|1669|90.7|5.4|3.9|0.7|9.9|26.3|
+||-9 |187|1897|93.7|3.5|2.7|0.4|6.7|25.1|
+||-6 |176|1821|95.1|2.9|2.0|0.4|5.4|18.8|
+||-3 |201|2096|96.2|1.6|2.2|0.3|4.2|15.9|
+|| 0 |158|1611|96.4|1.9|1.7|0.2|3.8|13.9|
+|| 3 |173|1710|96.7|1.7|1.6|0.2|3.6|17.9|
+|| 6 |185|1920|96.1|1.6|2.2|0.5|4.3|18.9|
+|| 9 |157|1533|96.9|1.4|1.7|0.5|3.6|14.0|
+||12 |150|1536|96.5|1.4|2.1|0.5|4.0|21.3|
+||clean |138|1390|97.9|0.9|1.2|0.2|2.3|13.8|
+||reverb |177|1755|96.8|1.5|1.8|0.2|3.5|16.4|
+|ambient noise with clean visual data |-12|187|1873|89.6|5.8|4.6|1.2|11.5|31.0|
+||-9 |193|1965|91.2|5.0|3.8|0.9|9.6|29.0|
+||-6 |176|1883|94.3|1.9|3.8|0.3|6.0|21.0|
+||-3 |173|1851|94.8|2.7|2.5|0.9|6.1|22.0|
+|| 0 |148|1470|96.3|1.6|2.0|0.1|3.8|16.9|
+|| 3 |176|1718|97.7|1.5|0.8|0.1|2.4|12.5|
+|| 6 |166|1714|96.6|1.6|1.8|0.2|3.6|16.3|
+|| 9 |170|1601|97.0|1.6|1.4|0.3|3.3|17.1|
+||12 |169|1718|95.4|2.6|2.0|0.1|4.7|20.7|
+||clean |138|1390|97.9|0.9|1.2|0.2|2.3|13.8|
+||reverb |177|1755|96.8|1.5|1.8|0.2|3.5|16.4|
+|ambient noise with visual Gaussian blur|-12|187|1873|86.9|7.3|5.8|1.1|14.2|35.8|
+||-9 |193|1965|91.1|5.4|3.5|1.0|9.9|30.1|
+||-6 |176|1883|93.3|2.7|4.0|0.3|7.0|24.4|
+||-3 |173|1851|95.1|2.5|2.4|0.8|5.7|21.4|
+|| 0 |148|1470|96.3|1.6|2.1|0.1|3.8|17.6|
+|| 3 |176|1718|97.3|1.6|1.2|0.2|2.9|13.6|
+|| 6 |166|1714|96.2|1.8|2.0|0.2|4.0|18.1|
+|| 9 |170|1601|97.0|1.4|1.6|0.2|3.2|16.5|
+||12 |169|1718|94.9|2.8|2.3|0.3|5.4|23.1|
+||clean |138|1390|97.8|0.9|1.3|0.2|2.4|14.5|
+||reverb |177|1755|96.5|1.5|2.1|0.2|3.7|16.9|
+|ambient noise with visual salt and pepper noise|-12|187|1873|87.6|7.0|5.4|1.3|13.8|35.8|
+||-9 |193|1965|91.0|5.8|3.2|1.3|10.3|30.6|
+||-6 |176|1883|93.6|2.0|4.4|0.4|6.9|24.4|
+||-3 |173|1851|95.6|2.9|1.6|0.8|5.2|20.2|
+|| 0 |148|1470|95.9|1.9|2.2|0.1|4.2|18.2|
+|| 3 |176|1718|98.0|1.0|1.0|0.3|2.3|13.1|
+|| 6 |166|1714|96.4|1.8|1.8|0.2|3.7|17.5|
+|| 9 |170|1601|97.0|1.4|1.6|0.4|3.4|16.5|
+||12 |169|1718|96.2|2.2|1.6|0.2|4.1|18.9|
+||clean |138|1390|98.1|0.9|1.1|0.2|2.2|13.0|
+||reverb |177|1755|96.6|1.5|1.9|0.2|3.6|16.9|
+
+### WER
+
+|dataset|SNR in dB|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|---|
+|music noise with clean visual data |-12|171|912|91.2|6.0|2.7|1.5|10.3|26.3|
+||-9 |187|1005|93.2|4.5|2.3|0.4|7.2|25.1|
+||-6 |176|951|94.1|3.7|2.2|0.3|6.2|18.8|
+||-3 |201|1097|95.2|2.7|2.1|0.4|5.2|15.9|
+|| 0 |158|847|96.7|2.2|1.1|0.4|3.7|13.9|
+|| 3 |173|884|95.6|2.6|1.8|0.3|4.8|17.9|
+|| 6 |185|997|95.5|2.3|2.2|0.7|5.2|18.9|
+|| 9 |157|817|96.2|2.1|1.7|0.7|4.5|14.0|
+||12 |150|832|95.1|2.4|2.5|0.2|5.2|21.3|
+||clean |138|739|97.2|1.5|1.4|0.4|3.2|13.8|
+||reverb |177|943|96.0|1.8|2.2|0.3|4.3|16.4|
+|ambient noise with clean visual data |-12|187|995|90.4|6.9|2.7|1.1|10.8|31.0|
+||-9 |193|1060|91.3|5.6|3.1|1.4|10.1|29.0|
+||-6 |176|971|94.4|2.9|2.7|0.3|5.9|21.0|
+||-3 |173|972|93.7|3.7|2.6|0.1|6.4|22.0|
+|| 0 |148|838|95.7|2.0|2.3|0.1|4.4|16.9|
+|| 3 |176|909|97.0|1.5|1.4|0.3|3.3|12.5|
+|| 6 |166|830|96.0|1.9|2.0|0.6|4.6|16.3|
+|| 9 |170|872|95.6|3.4|0.9|0.2|4.6|17.1|
+||12 |169|895|94.0|3.7|2.3|0.4|6.5|20.7|
+||clean |138|739|97.2|1.5|1.4|0.4|3.2|13.8|
+||reverb |177|943|96.0|1.8|2.2|0.3|4.3|16.4|
+|ambient noise with visual Gaussian blur|-12|187|995|87.0|9.1|3.8|1.0|14.0|35.8|
+||-9 |193|1060|90.6|6.2|3.2|1.1|10.6|30.1|
+||-6 |176|971|93.2|3.6|3.2|0.3|7.1|24.4|
+||-3 |173|972|94.0|3.6|2.4|0.1|6.1|21.4|
+|| 0 |148|838|95.6|2.3|2.1|0.2|4.7|17.6|
+|| 3 |176|909|96.3|1.7|2.1|0.3|4.1|13.6|
+|| 6 |166|830|95.4|2.3|2.3|0.6|5.2|18.1|
+|| 9 |170|872|95.6|3.1|1.3|0.2|4.6|16.5|
+||12 |169|895|93.2|4.4|2.5|0.4|7.3|23.1|
+||clean |138|739|97.0|1.5|1.5|0.4|3.4|14.5|
+||reverb |177|943|95.7|1.7|2.7|0.3|4.7|16.9|
+|ambient noise with visual salt and pepper noise|-12|187|995|87.1|8.8|4.0|0.9|13.8|35.8|
+||-9 |193|1060|90.5|6.3|3.2|1.1|10.7|30.6|
+||-6 |176|971|93.3|3.2|3.5|0.3|7.0|24.4|
+||-3 |173|972|94.7|3.8|1.5|0.2|5.6|20.2|
+|| 0 |148|838|95.3|2.4|2.3|0.2|4.9|18.2|
+|| 3 |176|909|96.8|1.4|1.8|0.3|3.5|13.1|
+|| 6 |166|830|95.9|2.2|1.9|0.7|4.8|17.5|
+|| 9 |170|872|95.6|3.1|1.3|0.2|4.6|16.5|
+||12 |169|895|94.7|3.5|1.8|0.3|5.6|18.9|
+||clean |138|739|97.4|1.5|1.1|0.4|3.0|13.0|
+||average |177|943|95.8|1.9|2.3|0.4|4.7|16.9|