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Sound Prediction

Solution for submission 147089

A detailed solution for submission 147089 submitted for challenge Sound Prediction

sean_benhur

image.png

Starter Code for Speech Recognition

Here we go, this is the last challange of Blitz 9.Now in this challange, we are not going to use any text based dataset, but we are going to predict numbers said from a sound. While, we will be learning tons to new things in this final challange, this final challange is more about putting what we learned from the last 4 challanges into practical real-world application such a Speech Recognition.

What we are going to Learn

  • Introduction to sound based datasets.
  • Using Mozilla DeepSpeech to train, evaluate and test our model.

Install packages 🗃

In [ ]:
!pip install aicrowd-cli
!mkdir assets

Collecting aicrowd-cli
  Downloading https://files.pythonhosted.org/packages/1f/57/59b5a00c6e90c9cc028b3da9dff90e242ad2847e735b1a0e81a21c616e27/aicrowd_cli-0.1.7-py3-none-any.whl (49kB)
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Collecting requests-toolbelt<1,>=0.9.1
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Requirement already satisfied: toml<1,>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli) (0.10.2)
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ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
ERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.
Installing collected packages: requests, requests-toolbelt, smmap, gitdb, gitpython, tqdm, colorama, commonmark, rich, aicrowd-cli
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Successfully installed aicrowd-cli-0.1.7 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.7 gitpython-3.1.14 requests-2.25.1 requests-toolbelt-0.9.1 rich-10.3.0 smmap-4.0.0 tqdm-4.61.1

Installing DeepSpeech

Now, all what we are doing in the below 4 cells is to setting up environment for Deepspeech, is a really trick part to do in this whole notebook

In [ ]:
!git clone --branch v0.9.3 https://github.com/mozilla/DeepSpeech

Cloning into 'DeepSpeech'...
remote: Enumerating objects: 23874, done.
remote: Counting objects: 100% (411/411), done.
remote: Compressing objects: 100% (185/185), done.
remote: Total 23874 (delta 231), reused 359 (delta 213), pack-reused 23463
Receiving objects: 100% (23874/23874), 49.48 MiB | 26.20 MiB/s, done.
Resolving deltas: 100% (16362/16362), done.
Note: checking out 'f2e9c85880dff94115ab510cde9ca4af7ee51c19'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b <new-branch-name>

Install DeepSpeech Dependencies

All the steps taken for this section are from Train IITM

In [ ]:
%cd /content/
!sudo apt-get install python3-venv
!sudo apt-get install python3-dev
!pip install --upgrade pip
!sudo apt-get install sox
!sudo apt-get install sox libsox-fmt-mp3
!sudo apt install git
!pip install librosa==0.7.2
!sudo apt-get install pciutils
!lspci | grep -i nvidia

!wget https://github.com/git-lfs/git-lfs/releases/download/v2.11.0/git-lfs-linux-amd64-v2.11.0.tar.gz
!tar xvf /content/git-lfs-linux-amd64-v2.11.0.tar.gz -C /content
!sudo bash /content/install.sh
%cd /content/DeepSpeech
!git-lfs pull

!wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.4/ds_ctcdecoder-0.7.4-cp36-cp36m-manylinux1_x86_64.whl
!pip install /content/DeepSpeech/ds_ctcdecoder-0.7.4-cp36-cp36m-manylinux1_x86_64.whl

!pip3 install folium==0.2.1
!pip3 install --upgrade pip==20.0.2 wheel==0.34.2 setuptools==46.1.3
!pip3 install --upgrade --force-reinstall -e .

/content
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Suggested packages:
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The following NEW packages will be installed:
  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa
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00:04.0 3D controller: NVIDIA Corporation Device 1eb8 (rev a1)
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README.md
CHANGELOG.md
git-lfs
install.sh
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/content/DeepSpeech
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Collecting MarkupSafe>=0.9.2
  Downloading MarkupSafe-2.0.1-cp37-cp37m-manylinux2010_x86_64.whl (31 kB)
Collecting cached-property; python_version < "3.8"
  Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)
Building wheels for collected packages: opuslib, bs4, resampy, audioread, wrapt, gast, termcolor, pyperclip
  Building wheel for opuslib (setup.py) ... done
  Created wheel for opuslib: filename=opuslib-2.0.0-py3-none-any.whl size=11009 sha256=aa5a10d52eebfd8a8fe154330ceea4a1ffe38879847b752bd714946e724c9539
  Stored in directory: /root/.cache/pip/wheels/e5/ba/d4/0e81231a9797fbb262ae3a54fd761fab850db7f32d94a3283a
  Building wheel for bs4 (setup.py) ... done
  Created wheel for bs4: filename=bs4-0.0.1-py3-none-any.whl size=1272 sha256=7a80997f1eb4f3f1c0acb33b91e702ef3e6a38371d98851f261b9a21558d3c3a
  Stored in directory: /root/.cache/pip/wheels/0a/9e/ba/20e5bbc1afef3a491f0b3bb74d508f99403aabe76eda2167ca
  Building wheel for resampy (setup.py) ... done
  Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320720 sha256=8374c1d6f754f8ccbfc805ed76728d42a718ac91e150f9b1b7d52922e8a52f4f
  Stored in directory: /root/.cache/pip/wheels/a0/18/0a/8ad18a597d8333a142c9789338a96a6208f1198d290ece356c
  Building wheel for audioread (setup.py) ... done
  Created wheel for audioread: filename=audioread-2.1.9-py3-none-any.whl size=23142 sha256=50ba58761c2f4ebf84ed18d76bf3207eaebbea80a3ecb640db6ce54bad3bda0b
  Stored in directory: /root/.cache/pip/wheels/ba/7b/eb/213741ccc0678f63e346ab8dff10495995ca3f426af87b8d88
  Building wheel for wrapt (setup.py) ... done
  Created wheel for wrapt: filename=wrapt-1.12.1-cp37-cp37m-linux_x86_64.whl size=68667 sha256=bbc3163dccabb5484cb436460b37452583b2e8bc5ac540444536f211df15fe27
  Stored in directory: /root/.cache/pip/wheels/62/76/4c/aa25851149f3f6d9785f6c869387ad82b3fd37582fa8147ac6
  Building wheel for gast (setup.py) ... done
  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7539 sha256=ddd13b1d9638e09f6b8d7a882aece5eafe3dbc9d28e281a8d83a391a5563e659
  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3
  Building wheel for termcolor (setup.py) ... done
  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4830 sha256=7113069d24fa49af911ad3dcedcffac5875f756b7f215c4b16362391087396cf
  Stored in directory: /root/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
  Building wheel for pyperclip (setup.py) ... done
  Created wheel for pyperclip: filename=pyperclip-1.8.2-py3-none-any.whl size=11107 sha256=e8b230c0783712fa905c94509169208f7dc75ea91da4c8bc425a24c267d912bb
  Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28
Successfully built opuslib bs4 resampy audioread wrapt gast termcolor pyperclip
ERROR: tensorflow 1.15.4 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.20.3 which is incompatible.
ERROR: tensorflow-probability 0.12.1 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.
ERROR: tensorflow-metadata 1.0.0 has requirement absl-py<0.13,>=0.9, but you'll have absl-py 0.13.0 which is incompatible.
ERROR: networkx 2.5.1 has requirement decorator<5,>=4.3, but you'll have decorator 5.0.9 which is incompatible.
ERROR: moviepy 0.2.3.5 has requirement decorator<5.0,>=4.0.2, but you'll have decorator 5.0.9 which is incompatible.
ERROR: kapre 0.3.5 has requirement tensorflow>=2.0.0, but you'll have tensorflow 1.15.4 which is incompatible.
ERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= "3.0", but you'll have pandas 1.2.4 which is incompatible.
ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
ERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.16.0 which is incompatible.
ERROR: flask 1.1.4 has requirement Werkzeug<2.0,>=0.15, but you'll have werkzeug 2.0.1 which is incompatible.
ERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.
Installing collected packages: numpy, six, python-utils, progressbar2, pyxdg, attrdict, absl-py, semver, opuslib, zipp, typing-extensions, importlib-metadata, greenlet, sqlalchemy, scipy, colorlog, cmaes, pbr, wcwidth, pyperclip, colorama, attrs, cmd2, PrettyTable, PyYAML, pyparsing, stevedore, cliff, python-dateutil, python-editor, MarkupSafe, Mako, alembic, packaging, tqdm, optuna, sox, soupsieve, beautifulsoup4, bs4, pytz, pandas, urllib3, certifi, idna, chardet, requests, llvmlite, setuptools, numba, joblib, decorator, resampy, audioread, threadpoolctl, scikit-learn, pycparser, cffi, soundfile, appdirs, pooch, librosa, ds-ctcdecoder, grpcio, markdown, wheel, protobuf, werkzeug, tensorboard, wrapt, astor, tensorflow-estimator, opt-einsum, gast, google-pasta, termcolor, keras-preprocessing, cached-property, h5py, keras-applications, tensorflow, deepspeech-training
  Attempting uninstall: numpy
    Found existing installation: numpy 1.19.5
    Uninstalling numpy-1.19.5:
      Successfully uninstalled numpy-1.19.5
  Attempting uninstall: six
    Found existing installation: six 1.15.0
    Uninstalling six-1.15.0:
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  Attempting uninstall: python-utils
    Found existing installation: python-utils 2.5.6
    Uninstalling python-utils-2.5.6:
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  Attempting uninstall: progressbar2
    Found existing installation: progressbar2 3.38.0
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  Attempting uninstall: absl-py
    Found existing installation: absl-py 0.12.0
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  Attempting uninstall: semver
    Found existing installation: semver 2.13.0
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  Attempting uninstall: zipp
    Found existing installation: zipp 3.4.1
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  Attempting uninstall: typing-extensions
    Found existing installation: typing-extensions 3.7.4.3
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    Found existing installation: importlib-metadata 4.5.0
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  Attempting uninstall: greenlet
    Found existing installation: greenlet 1.1.0
    Uninstalling greenlet-1.1.0:
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  Attempting uninstall: sqlalchemy
    Found existing installation: SQLAlchemy 1.4.18
    Uninstalling SQLAlchemy-1.4.18:
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  Attempting uninstall: scipy
    Found existing installation: scipy 1.4.1
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      Successfully uninstalled scipy-1.4.1
  Attempting uninstall: wcwidth
    Found existing installation: wcwidth 0.2.5
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  Attempting uninstall: colorama
    Found existing installation: colorama 0.4.4
    Uninstalling colorama-0.4.4:
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  Attempting uninstall: attrs
    Found existing installation: attrs 21.2.0
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  Attempting uninstall: PrettyTable
    Found existing installation: prettytable 2.1.0
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  Attempting uninstall: PyYAML
    Found existing installation: PyYAML 3.13
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  Attempting uninstall: pyparsing
    Found existing installation: pyparsing 2.4.7
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  Attempting uninstall: python-dateutil
    Found existing installation: python-dateutil 2.8.1
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  Attempting uninstall: MarkupSafe
    Found existing installation: MarkupSafe 2.0.1
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      Successfully uninstalled MarkupSafe-2.0.1
  Attempting uninstall: packaging
    Found existing installation: packaging 20.9
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  Attempting uninstall: tqdm
    Found existing installation: tqdm 4.61.1
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  Attempting uninstall: beautifulsoup4
    Found existing installation: beautifulsoup4 4.6.3
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      Successfully uninstalled beautifulsoup4-4.6.3
  Attempting uninstall: bs4
    Found existing installation: bs4 0.0.1
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  Attempting uninstall: pytz
    Found existing installation: pytz 2018.9
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  Attempting uninstall: pandas
    Found existing installation: pandas 1.1.5
    Uninstalling pandas-1.1.5:
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  Attempting uninstall: urllib3
    Found existing installation: urllib3 1.24.3
    Uninstalling urllib3-1.24.3:
      Successfully uninstalled urllib3-1.24.3
  Attempting uninstall: certifi
    Found existing installation: certifi 2021.5.30
    Uninstalling certifi-2021.5.30:
      Successfully uninstalled certifi-2021.5.30
  Attempting uninstall: idna
    Found existing installation: idna 2.10
    Uninstalling idna-2.10:
      Successfully uninstalled idna-2.10
  Attempting uninstall: chardet
    Found existing installation: chardet 3.0.4
    Uninstalling chardet-3.0.4:
      Successfully uninstalled chardet-3.0.4
  Attempting uninstall: requests
    Found existing installation: requests 2.25.1
    Uninstalling requests-2.25.1:
      Successfully uninstalled requests-2.25.1
  Attempting uninstall: llvmlite
    Found existing installation: llvmlite 0.34.0
    Uninstalling llvmlite-0.34.0:
      Successfully uninstalled llvmlite-0.34.0
  Attempting uninstall: setuptools
    Found existing installation: setuptools 46.1.3
    Uninstalling setuptools-46.1.3:
      Successfully uninstalled setuptools-46.1.3
  Attempting uninstall: numba
    Found existing installation: numba 0.51.2
    Uninstalling numba-0.51.2:
      Successfully uninstalled numba-0.51.2
  Attempting uninstall: joblib
    Found existing installation: joblib 1.0.1
    Uninstalling joblib-1.0.1:
      Successfully uninstalled joblib-1.0.1
  Attempting uninstall: decorator
    Found existing installation: decorator 4.4.2
    Uninstalling decorator-4.4.2:
      Successfully uninstalled decorator-4.4.2
  Attempting uninstall: resampy
    Found existing installation: resampy 0.2.2
    Uninstalling resampy-0.2.2:
      Successfully uninstalled resampy-0.2.2
  Attempting uninstall: audioread
    Found existing installation: audioread 2.1.9
    Uninstalling audioread-2.1.9:
      Successfully uninstalled audioread-2.1.9
  Attempting uninstall: scikit-learn
    Found existing installation: scikit-learn 0.22.2.post1
    Uninstalling scikit-learn-0.22.2.post1:
      Successfully uninstalled scikit-learn-0.22.2.post1
  Attempting uninstall: pycparser
    Found existing installation: pycparser 2.20
    Uninstalling pycparser-2.20:
      Successfully uninstalled pycparser-2.20
  Attempting uninstall: cffi
    Found existing installation: cffi 1.14.5
    Uninstalling cffi-1.14.5:
      Successfully uninstalled cffi-1.14.5
  Attempting uninstall: soundfile
    Found existing installation: SoundFile 0.10.3.post1
    Uninstalling SoundFile-0.10.3.post1:
      Successfully uninstalled SoundFile-0.10.3.post1
  Attempting uninstall: appdirs
    Found existing installation: appdirs 1.4.4
    Uninstalling appdirs-1.4.4:
      Successfully uninstalled appdirs-1.4.4
  Attempting uninstall: pooch
    Found existing installation: pooch 1.4.0
    Uninstalling pooch-1.4.0:
      Successfully uninstalled pooch-1.4.0
  Attempting uninstall: librosa
    Found existing installation: librosa 0.7.2
    Uninstalling librosa-0.7.2:
      Successfully uninstalled librosa-0.7.2
  Attempting uninstall: grpcio
    Found existing installation: grpcio 1.34.1
    Uninstalling grpcio-1.34.1:
      Successfully uninstalled grpcio-1.34.1
  Attempting uninstall: markdown
    Found existing installation: Markdown 3.3.4
    Uninstalling Markdown-3.3.4:
      Successfully uninstalled Markdown-3.3.4
  Attempting uninstall: wheel
    Found existing installation: wheel 0.34.2
    Uninstalling wheel-0.34.2:
      Successfully uninstalled wheel-0.34.2
  Attempting uninstall: protobuf
    Found existing installation: protobuf 3.12.4
    Uninstalling protobuf-3.12.4:
      Successfully uninstalled protobuf-3.12.4
  Attempting uninstall: werkzeug
    Found existing installation: Werkzeug 1.0.1
    Uninstalling Werkzeug-1.0.1:
      Successfully uninstalled Werkzeug-1.0.1
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.5.0
    Uninstalling tensorboard-2.5.0:
      Successfully uninstalled tensorboard-2.5.0
  Attempting uninstall: wrapt
    Found existing installation: wrapt 1.12.1
    Uninstalling wrapt-1.12.1:
      Successfully uninstalled wrapt-1.12.1
  Attempting uninstall: astor
    Found existing installation: astor 0.8.1
    Uninstalling astor-0.8.1:
      Successfully uninstalled astor-0.8.1
  Attempting uninstall: tensorflow-estimator
    Found existing installation: tensorflow-estimator 2.5.0
    Uninstalling tensorflow-estimator-2.5.0:
      Successfully uninstalled tensorflow-estimator-2.5.0
  Attempting uninstall: opt-einsum
    Found existing installation: opt-einsum 3.3.0
    Uninstalling opt-einsum-3.3.0:
      Successfully uninstalled opt-einsum-3.3.0
  Attempting uninstall: gast
    Found existing installation: gast 0.4.0
    Uninstalling gast-0.4.0:
      Successfully uninstalled gast-0.4.0
  Attempting uninstall: google-pasta
    Found existing installation: google-pasta 0.2.0
    Uninstalling google-pasta-0.2.0:
      Successfully uninstalled google-pasta-0.2.0
  Attempting uninstall: termcolor
    Found existing installation: termcolor 1.1.0
    Uninstalling termcolor-1.1.0:
      Successfully uninstalled termcolor-1.1.0
  Attempting uninstall: keras-preprocessing
    Found existing installation: Keras-Preprocessing 1.1.2
    Uninstalling Keras-Preprocessing-1.1.2:
      Successfully uninstalled Keras-Preprocessing-1.1.2
  Attempting uninstall: cached-property
    Found existing installation: cached-property 1.5.2
    Uninstalling cached-property-1.5.2:
      Successfully uninstalled cached-property-1.5.2
  Attempting uninstall: h5py
    Found existing installation: h5py 3.1.0
    Uninstalling h5py-3.1.0:
      Successfully uninstalled h5py-3.1.0
  Attempting uninstall: tensorflow
    Found existing installation: tensorflow 2.5.0
    Uninstalling tensorflow-2.5.0:
      Successfully uninstalled tensorflow-2.5.0
  Running setup.py develop for deepspeech-training
Successfully installed Mako-1.1.4 MarkupSafe-2.0.1 PrettyTable-2.1.0 PyYAML-5.4.1 absl-py-0.13.0 alembic-1.6.5 appdirs-1.4.4 astor-0.8.1 attrdict-2.0.1 attrs-21.2.0 audioread-2.1.9 beautifulsoup4-4.9.3 bs4-0.0.1 cached-property-1.5.2 certifi-2021.5.30 cffi-1.14.5 chardet-4.0.0 cliff-3.8.0 cmaes-0.8.2 cmd2-2.1.0 colorama-0.4.4 colorlog-5.0.1 decorator-5.0.9 deepspeech-training ds-ctcdecoder-0.9.3 gast-0.2.2 google-pasta-0.2.0 greenlet-1.1.0 grpcio-1.38.0 h5py-3.2.1 idna-2.10 importlib-metadata-4.5.0 joblib-1.0.1 keras-applications-1.0.8 keras-preprocessing-1.1.2 librosa-0.8.1 llvmlite-0.31.0 markdown-3.3.4 numba-0.47.0 numpy-1.20.3 opt-einsum-3.3.0 optuna-2.8.0 opuslib-2.0.0 packaging-20.9 pandas-1.2.4 pbr-5.6.0 pooch-1.4.0 progressbar2-3.53.1 protobuf-3.17.3 pycparser-2.20 pyparsing-2.4.7 pyperclip-1.8.2 python-dateutil-2.8.1 python-editor-1.0.4 python-utils-2.5.6 pytz-2021.1 pyxdg-0.27 requests-2.25.1 resampy-0.2.2 scikit-learn-0.24.2 scipy-1.6.3 semver-2.13.0 setuptools-57.0.0 six-1.16.0 soundfile-0.10.3.post1 soupsieve-2.2.1 sox-1.4.1 sqlalchemy-1.4.18 stevedore-3.3.0 tensorboard-1.15.0 tensorflow-1.15.4 tensorflow-estimator-1.15.1 termcolor-1.1.0 threadpoolctl-2.1.0 tqdm-4.61.1 typing-extensions-3.10.0.0 urllib3-1.26.5 wcwidth-0.2.5 werkzeug-2.0.1 wheel-0.36.2 wrapt-1.12.1 zipp-3.4.1

The colab notebook will be restarted by running the cell. Continue by running the below cells after the colab has restarted

In [ ]:
!nvcc --version
!nvidia-smi

# Restarting the Runtine, run only below cells after colab has restarted
import os
os.kill(os.getpid(), 9)

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:09_PDT_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.TC445_37.28845127_0

Set default CUDA version

  • A input will be asking a confirmation for changing CUDA, Press Y
In [ ]:
# Default CUDA version in Colab is 10.1, need to change to 10.0

! echo $PATH

import os
os.environ['PATH'] += ":/usr/local/cuda-10.0/bin"
os.environ['CUDADIR'] = "/usr/local/cuda-10.0"
os.environ['LD_LIBRARY_PATH'] = "/usr/lib64-nvidia:/usr/local/cuda-10.0/lib64"

!echo $PATH
!echo $LD_LIBRARY_PATH
!source ~/.bashrc

!env | grep -i cuda

%cd /content/
!wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
!sudo apt-get install freeglut3 freeglut3-dev libxi-dev libxmu-dev
!sudo apt-get install build-essential dkms
!sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
!sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub

!sudo apt-get update
!sudo apt-get install cuda-10-0

!sudo rm /usr/local/cuda
!sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
%ls -l /usr/local/

!pip3 uninstall tensorflow -y
!pip3 install 'tensorflow-gpu==1.15.2'

/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/bin
/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/bin:/usr/local/cuda-10.0/bin
/usr/lib64-nvidia:/usr/local/cuda-10.0/lib64
LD_LIBRARY_PATH=/usr/lib64-nvidia:/usr/local/cuda-10.0/lib64
CUDADIR=/usr/local/cuda-10.0
LIBRARY_PATH=/usr/local/cuda/lib64/stubs
CUDA_VERSION=11.0.3
NVIDIA_REQUIRE_CUDA=cuda>=11.0 brand=tesla,driver>=418,driver<419 brand=tesla,driver>=440,driver<441 brand=tesla,driver>=450,driver<451
PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/bin:/usr/local/cuda-10.0/bin
/content
--2021-06-17 10:23:31--  https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 152.195.19.142
Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|152.195.19.142|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2940 (2.9K) [application/x-deb]
Saving to: ‘cuda-repo-ubuntu1804_10.0.130-1_amd64.deb’

cuda-repo-ubuntu180 100%[===================>]   2.87K  --.-KB/s    in 0s      

2021-06-17 10:23:31 (165 MB/s) - ‘cuda-repo-ubuntu1804_10.0.130-1_amd64.deb’ saved [2940/2940]

Reading package lists... Done
Building dependency tree       
Reading state information... Done
libxi-dev is already the newest version (2:1.7.9-1).
libxi-dev set to manually installed.
libxmu-dev is already the newest version (2:1.1.2-2).
libxmu-dev set to manually installed.
freeglut3 is already the newest version (2.8.1-3).
freeglut3 set to manually installed.
freeglut3-dev is already the newest version (2.8.1-3).
freeglut3-dev set to manually installed.
0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.
Reading package lists... Done
Building dependency tree       
Reading state information... Done
build-essential is already the newest version (12.4ubuntu1).
dkms is already the newest version (2.3-3ubuntu9.7).
dkms set to manually installed.
0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.
Selecting previously unselected package cuda-repo-ubuntu1804.
(Reading database ... 160942 files and directories currently installed.)
Preparing to unpack cuda-repo-ubuntu1804_10.0.130-1_amd64.deb ...
Unpacking cuda-repo-ubuntu1804 (10.0.130-1) ...
Setting up cuda-repo-ubuntu1804 (10.0.130-1) ...

Configuration file '/etc/apt/sources.list.d/cuda.list'
 ==> File on system created by you or by a script.
 ==> File also in package provided by package maintainer.
   What would you like to do about it ?  Your options are:
    Y or I  : install the package maintainer's version
    N or O  : keep your currently-installed version
      D     : show the differences between the versions
      Z     : start a shell to examine the situation
 The default action is to keep your current version.
*** cuda.list (Y/I/N/O/D/Z) [default=N] ? y
Installing new version of config file /etc/apt/sources.list.d/cuda.list ...
Executing: /tmp/apt-key-gpghome.bApO6MmchE/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
gpg: requesting key from 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub'
gpg: key F60F4B3D7FA2AF80: "cudatools <cudatools@nvidia.com>" not changed
gpg: Total number processed: 1
gpg:              unchanged: 1
Ign:1 http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease
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Reading package lists... Done
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Building dependency tree       
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cuda-10-0 is already the newest version (10.0.130-1).
0 upgraded, 0 newly installed, 0 to remove and 46 not upgraded.
total 80
drwxr-xr-x  1 root root 4096 Jun 17 10:22 bin/
lrwxrwxrwx  1 root root   20 Jun 17 10:23 cuda -> /usr/local/cuda-10.0/
drwxr-xr-x 16 root root 4096 Jun 15 13:23 cuda-10.0/
drwxr-xr-x 15 root root 4096 Jun 15 13:25 cuda-10.1/
drwxr-xr-x  1 root root 4096 Jun 15 13:28 cuda-11.0/
drwxr-xr-x  1 root root 4096 Jun 15 13:39 etc/
drwxr-xr-x  2 root root 4096 Sep 21  2020 games/
drwxr-xr-x  2 root root 4096 Jun 15 13:51 _gcs_config_ops.so/
drwxr-xr-x  1 root root 4096 Jun 15 14:00 include/
drwxr-xr-x  1 root root 4096 Jun 15 14:00 lib/
-rw-r--r--  1 root root 1636 Jun 15 13:54 LICENSE.txt
drwxr-xr-x  3 root root 4096 Jun 15 13:51 licensing/
lrwxrwxrwx  1 root root    9 Sep 21  2020 man -> share/man/
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drwxr-xr-x  2 root root 4096 Sep 21  2020 src/
drwxr-xr-x  2 root root 4096 Jun 15 14:02 xgboost/
Found existing installation: tensorflow 1.15.4
Uninstalling tensorflow-1.15.4:
  Successfully uninstalled tensorflow-1.15.4
Collecting tensorflow-gpu==1.15.2
  Downloading tensorflow_gpu-1.15.2-cp37-cp37m-manylinux2010_x86_64.whl (410.9 MB)
     |████████████████████████████████| 410.9 MB 32 kB/s 
Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==1.15.2) (1.15.1)
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Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.15.2

Importing Libraries 💻

In [ ]:
# Importing Libraries
import pandas as pd
import re
from ast import literal_eval
import os
import librosa


# To make things more beautiful! 
from rich.console import Console
from rich.table import Table
from rich import pretty
pretty.install()
from IPython.display import Audio

DATA_FOLDER = "data"

Training phase ⚙️

Downloading Dataset

Same as previous challenges, we need to download the dataset using AIcrowd CLI

In [ ]:


API Key valid
Saved API Key successfully!
In [ ]:
# Downloading the Dataset
!rm -rf data
!mkdir data

train.csv:   0% 0.00/713k [00:00<?, ?B/s]
train.csv: 100% 713k/713k [00:00<00:00, 5.74MB/s]
test.csv: 100% 159k/159k [00:00<00:00, 2.25MB/s]

train.zip:   0% 0.00/643M [00:00<?, ?B/s]

val.csv: 100% 69.1k/69.1k [00:00<00:00, 1.67MB/s]

test.zip:  42% 67.1M/160M [00:02<00:04, 22.9MB/s]

val.zip:   0% 0.00/63.9M [00:00<?, ?B/s]
train.zip:   5% 33.6M/643M [00:04<01:22, 7.38MB/s]
test.zip:  84% 134M/160M [00:06<00:01, 20.1MB/s]
test.zip: 100% 160M/160M [00:07<00:00, 21.6MB/s]


train.zip:  10% 67.1M/643M [00:08<01:08, 8.36MB/s]

val.zip: 100% 63.9M/63.9M [00:08<00:00, 7.24MB/s]
train.zip: 100% 643M/643M [01:08<00:00, 9.35MB/s]

Unzipping Files

In [ ]:
# Unzipping the zip files into the respective set folders
!unzip /content/data/train.zip  -d /content/data/train >/dev/null
!unzip /content/data/val.zip -d /content/data/val >/dev/null
!unzip /content/data/test.zip -d /content/data/test >/dev/null

Reading the Dataset

In [ ]:
train_df = pd.read_csv(os.path.join(DATA_FOLDER, "train.csv"))
val_df = pd.read_csv(os.path.join(DATA_FOLDER, "val.csv"))
test_df = pd.read_csv(os.path.join(DATA_FOLDER, "test.csv"))

train_df
Out[ ]:
SoundID label
0 0 efficient spatialtemporal context modeling for
1 1 on the space
2 2 baryogenesis through mixing
3 3 noncommutative gravity in three dimensions
4 4 effective thermal diffusivity in
... ... ...
19995 19995 dixmier trace for
19996 19996 removahedral congruences versus permutree cong...
19997 19997 viscous control of minimum
19998 19998 new boundary harnack inequalities with
19999 19999 a dynamic systems

20000 rows × 2 columns

Preprocessing the Dataset

In this section, we are going to add some necessary columns whcich DeepSpeech will need while model training

In [ ]:
# Preprocessing Dataset Function
def preprocess_data(df, set_name):

  # Adding the Wav filepath 
  df['wav_filename'] = df['SoundID'].apply(lambda x : os.path.join("/content", "data", set_name+"/" +str(x) + ".wav"))
  
  df['transcript'] = df['label']
  
  # Addding the wav file size ( in bytes ), due to mos of the files are around 30,000 bytes, there is not much need put that 
  # But you can do it you want :)
  df['wav_filesize'] = 30000

  return df
In [ ]:
# Preprocessing all three sets
train_df = preprocess_data(train_df, "train")
val_df = preprocess_data(val_df, "val")
test_df = preprocess_data(test_df, "test")
val_df
Out[ ]:
SoundID label wav_filename transcript wav_filesize
0 0 injectivity in higher order /content/data/val/0.wav injectivity in higher order 30000
1 1 minimal constraints in the parity /content/data/val/1.wav minimal constraints in the parity 30000
2 2 learning to refer /content/data/val/2.wav learning to refer 30000
3 3 on the expressive power /content/data/val/3.wav on the expressive power 30000
4 4 small parts in the bernoulli /content/data/val/4.wav small parts in the bernoulli 30000
... ... ... ... ... ...
1995 1995 responses of small quantum systems /content/data/val/1995.wav responses of small quantum systems 30000
1996 1996 thermal rectification in quantum /content/data/val/1996.wav thermal rectification in quantum 30000
1997 1997 decomposition and unitarity in quantum /content/data/val/1997.wav decomposition and unitarity in quantum 30000
1998 1998 on gravitational collapse in /content/data/val/1998.wav on gravitational collapse in 30000
1999 1999 cogrowth and spectral gap /content/data/val/1999.wav cogrowth and spectral gap 30000

2000 rows × 5 columns

Sound

Listening to some sounds with with respctive labels

In [ ]:
# Getting a sample from the dataset
example = train_df.iloc[10, :]

# Reading the sound using the path
sound, sample_rate = librosa.load(example['wav_filename'])

("Sound : ", sound), ("Label : ", sample_rate)

(
    ('Sound : ', array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)),
    ('Label : ', 22050)
)

The sound is a 1D list with each value is the amplitude of the sound. And the sample_rate is show many of the sound array elements are going through the speaker in each second.

Note : Lower Your PC Volume :)

In [ ]:
Audio(example['wav_filename'])
Out[ ]:
In [ ]:
example['transcript']

'cold bosons in optical lattices'
Out[ ]:
In [ ]:
# Saving the preprocessing dataset
train_df.to_csv("deepspeech_train.csv", index=False)
val_df.to_csv("deepspeech_val.csv", index=False)
test_df.to_csv("deepspeech_test.csv", index=False)

Training the model + Validation + Testing

Now, using Deep Speech command line, we are going to put the path of dataset with various other parameters to train & validation every epoch, but test after all epochs are done!

In [18]:
%cd DeepSpeech  

# We are going to use validation data instead of training because training will take a lot more time
# Putting the data files
# Setting up Model parameters
# Setting up the batch size and audo sample rate
# Using mixed precision so that the model will train faster
# Saving the test predictions

!python DeepSpeech.py --train_files ../deepspeech_train.csv --dev_files ../deepspeech_val.csv --test_files ../deepspeech_test.csv \
 --n_hidden 1048 \
--audio_sample_rate 8000 --train_batch_size 64 --dev_batch_size 32 --test_batch_size 32 \
--automatic_mixed_precision True --epochs 4 \
--test_output_file ../assets/output.txt

%cd ..

/content/DeepSpeech
I0617 11:24:58.173683 140584634529664 utils.py:157] NumExpr defaulting to 2 threads.
I Enabling automatic mixed precision training.
I Loading best validating checkpoint from /root/.local/share/deepspeech/checkpoints/best_dev-1243
I Loading variable from checkpoint: cond_1/beta1_power
I Loading variable from checkpoint: cond_1/beta2_power
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/bias
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/bias/Adam
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/bias/Adam_1
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/kernel
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/kernel/Adam
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/kernel/Adam_1
I Loading variable from checkpoint: current_loss_scale
I Loading variable from checkpoint: global_step
I Loading variable from checkpoint: good_steps
I Loading variable from checkpoint: layer_1/bias
I Loading variable from checkpoint: layer_1/bias/Adam
I Loading variable from checkpoint: layer_1/bias/Adam_1
I Loading variable from checkpoint: layer_1/weights
I Loading variable from checkpoint: layer_1/weights/Adam
I Loading variable from checkpoint: layer_1/weights/Adam_1
I Loading variable from checkpoint: layer_2/bias
I Loading variable from checkpoint: layer_2/bias/Adam
I Loading variable from checkpoint: layer_2/bias/Adam_1
I Loading variable from checkpoint: layer_2/weights
I Loading variable from checkpoint: layer_2/weights/Adam
I Loading variable from checkpoint: layer_2/weights/Adam_1
I Loading variable from checkpoint: layer_3/bias
I Loading variable from checkpoint: layer_3/bias/Adam
I Loading variable from checkpoint: layer_3/bias/Adam_1
I Loading variable from checkpoint: layer_3/weights
I Loading variable from checkpoint: layer_3/weights/Adam
I Loading variable from checkpoint: layer_3/weights/Adam_1
I Loading variable from checkpoint: layer_5/bias
I Loading variable from checkpoint: layer_5/bias/Adam
I Loading variable from checkpoint: layer_5/bias/Adam_1
I Loading variable from checkpoint: layer_5/weights
I Loading variable from checkpoint: layer_5/weights/Adam
I Loading variable from checkpoint: layer_5/weights/Adam_1
I Loading variable from checkpoint: layer_6/bias
I Loading variable from checkpoint: layer_6/bias/Adam
I Loading variable from checkpoint: layer_6/bias/Adam_1
I Loading variable from checkpoint: layer_6/weights
I Loading variable from checkpoint: layer_6/weights/Adam
I Loading variable from checkpoint: layer_6/weights/Adam_1
I Loading variable from checkpoint: learning_rate
I STARTING Optimization
Epoch 0 |   Training | Elapsed Time: 0:01:16 | Steps: 312 | Loss: 14.318905     
Epoch 0 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 16.755404 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 16.755404 to: /root/.local/share/deepspeech/checkpoints/best_dev-1555
--------------------------------------------------------------------------------
Epoch 1 |   Training | Elapsed Time: 0:01:14 | Steps: 312 | Loss: 11.427003     
Epoch 1 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 17.155324 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 2 |   Training | Elapsed Time: 0:01:13 | Steps: 312 | Loss: 9.298018      
Epoch 2 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 16.358285 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 16.358285 to: /root/.local/share/deepspeech/checkpoints/best_dev-2179
--------------------------------------------------------------------------------
Epoch 3 |   Training | Elapsed Time: 0:01:13 | Steps: 312 | Loss: 7.738449      
Epoch 3 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 15.976543 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 15.976543 to: /root/.local/share/deepspeech/checkpoints/best_dev-2491
--------------------------------------------------------------------------------
I FINISHED optimization in 0:05:21.935305
I Loading best validating checkpoint from /root/.local/share/deepspeech/checkpoints/best_dev-2491
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/bias
I Loading variable from checkpoint: cudnn_lstm/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell/kernel
I Loading variable from checkpoint: global_step
I Loading variable from checkpoint: layer_1/bias
I Loading variable from checkpoint: layer_1/weights
I Loading variable from checkpoint: layer_2/bias
I Loading variable from checkpoint: layer_2/weights
I Loading variable from checkpoint: layer_3/bias
I Loading variable from checkpoint: layer_3/weights
I Loading variable from checkpoint: layer_5/bias
I Loading variable from checkpoint: layer_5/weights
I Loading variable from checkpoint: layer_6/bias
I Loading variable from checkpoint: layer_6/weights
Testing model on ../deepspeech_test.csv
Test epoch | Steps: 157 | Elapsed Time: 0:49:31                                 
Test on ../deepspeech_test.csv - WER: 1.000000, CER: 1.000000, loss: 394.068298
--------------------------------------------------------------------------------
Best WER: 
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 328.225250
 - wav: file:///content/data/test/890.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "montecrelosimulations"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.769231, loss: 322.645355
 - wav: file:///content/data/test/4061.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "acategoridalapproach"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.846154, loss: 316.388702
 - wav: file:///content/data/test/3083.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "studyolinew"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 313.971680
 - wav: file:///content/data/test/532.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "flashadcdataprocessing"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.923077, loss: 295.368317
 - wav: file:///content/data/test/3488.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "ortetermanantispancions"
--------------------------------------------------------------------------------
Median WER: 
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.038462, loss: 389.109161
 - wav: file:///content/data/test/1457.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "control shalenges for resilian"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.000000, loss: 388.974426
 - wav: file:///content/data/test/473.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "high energy cosmic vaunutrinos"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.000000, loss: 388.883636
 - wav: file:///content/data/test/1022.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "gpformation time and direct"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.038462, loss: 388.800873
 - wav: file:///content/data/test/1296.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "coupling of hetomic quandraqol"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.000000, loss: 388.730713
 - wav: file:///content/data/test/1556.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "metedto quarks in supersymmetric"
--------------------------------------------------------------------------------
Worst WER: 
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.461538, loss: 286.582855
 - wav: file:///content/data/test/4521.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "an manaoedyc ol cerameworg dr underx manning"
--------------------------------------------------------------------------------
WER: 7.000000, CER: 0.923077, loss: 270.195465
 - wav: file:///content/data/test/4738.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "ra le olecu a a bes for"
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.423077, loss: 265.195343
 - wav: file:///content/data/test/4514.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "asi totorspoectral analygis is am ico tes"
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.153846, loss: 236.701065
 - wav: file:///content/data/test/4617.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "pomoc of cofone wean of eer ejials"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.346154, loss: 495.157196
 - wav: file:///content/data/test/225.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "pesimasm af bout on none on nons inspirs"
--------------------------------------------------------------------------------
/content

Getting the Predictions

In the previous command, we saved the testing results as outputs.txt in assets folder. Let's read the file and convert the outputs into the .csv format.

In [19]:
# Reading the output.txt file
data = open(os.path.join("assets", "output.txt"))
output = data.read()

# Convert the text into python list
output = literal_eval(output)
In [20]:
# Getting the sound and respective label for submission
SoundID = [int(sample['wav_filename'].split("/")[-1].split(".")[0])  for sample in output]
label = [sample['res']  for sample in output]
print(SoundID[0], label[0])

890 montecrelosimulations
In [21]:
test_df['SoundID'] = SoundID
test_df['label'] = label
test_df
Out[21]:
SoundID label wav_filename transcript wav_filesize
0 890 montecrelosimulations /content/data/test/0.wav abcdefghijklmnopqrstuvwxyz 30000
1 4061 acategoridalapproach /content/data/test/1.wav abcdefghijklmnopqrstuvwxyz 30000
2 3083 studyolinew /content/data/test/2.wav abcdefghijklmnopqrstuvwxyz 30000
3 532 flashadcdataprocessing /content/data/test/3.wav abcdefghijklmnopqrstuvwxyz 30000
4 3488 ortetermanantispancions /content/data/test/4.wav abcdefghijklmnopqrstuvwxyz 30000
... ... ... ... ... ...
4995 4521 an manaoedyc ol cerameworg dr underx manning /content/data/test/4995.wav abcdefghijklmnopqrstuvwxyz 30000
4996 4738 ra le olecu a a bes for /content/data/test/4996.wav abcdefghijklmnopqrstuvwxyz 30000
4997 4514 asi totorspoectral analygis is am ico tes /content/data/test/4997.wav abcdefghijklmnopqrstuvwxyz 30000
4998 4617 pomoc of cofone wean of eer ejials /content/data/test/4998.wav abcdefghijklmnopqrstuvwxyz 30000
4999 225 pesimasm af bout on none on nons inspirs /content/data/test/4999.wav abcdefghijklmnopqrstuvwxyz 30000

5000 rows × 5 columns

In [22]:
# It is recommended to sort your columns before making the submission
test_df = test_df.sort_values("SoundID")
test_df
Out[22]:
SoundID label wav_filename transcript wav_filesize
2184 0 eror ndalysis foro probabilities /content/data/test/2184.wav abcdefghijklmnopqrstuvwxyz 30000
4502 1 spably fla completions of universeal /content/data/test/4502.wav abcdefghijklmnopqrstuvwxyz 30000
1014 2 fixek points of /content/data/test/1014.wav abcdefghijklmnopqrstuvwxyz 30000
4418 3 gheometry of regranguan grahen ni /content/data/test/4418.wav abcdefghijklmnopqrstuvwxyz 30000
2328 4 greation and banishing of /content/data/test/2328.wav abcdefghijklmnopqrstuvwxyz 30000
... ... ... ... ... ...
2574 4995 flang weaves withweak singularities /content/data/test/2574.wav abcdefghijklmnopqrstuvwxyz 30000
4609 4996 neteractive emsc qus as atual /content/data/test/4609.wav abcdefghijklmnopqrstuvwxyz 30000
3260 4997 noces intotis ineavolf tricemic /content/data/test/3260.wav abcdefghijklmnopqrstuvwxyz 30000
1106 4998 search for havy /content/data/test/1106.wav abcdefghijklmnopqrstuvwxyz 30000
4488 4999 esem higs boson searces in /content/data/test/4488.wav abcdefghijklmnopqrstuvwxyz 30000

5000 rows × 5 columns

Note : Please make sure that there should be filename submission.csv in assets folder before submitting it

In [23]:
# Saving the sample submission in assets directory
test_df.to_csv(os.path.join("assets", "submission.csv"), index=False)

Submit to AIcrowd 🚀

Note : Please save the notebook before submitting it (Ctrl + S)

In [ ]:


Mounting Google Drive 💾
Your Google Drive will be mounted to access the colab notebook
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code

Enter your authorization code:

Congratulations 🎉 you did it, but there still a lot of improvement that can be made, Changing Hyperparameters seems the first option to start with, have fun!

And btw -

Don't be shy to ask question related to any errors you are getting or doubts in any part of this notebook in discussion forum or in AIcrowd Discord sever, AIcrew will be happy to help you :)

Also, wanna give us your valuable feedback for next blitz or wanna work with us creating blitz challanges ? Let us know!

In [ ]:

Content

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