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Sound Prediction

Solution for submission 148478

A detailed solution for submission 148478 submitted for challenge Sound Prediction

BerAnton

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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 [1]:
!pip install aicrowd-cli
!mkdir assets
Collecting aicrowd-cli
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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.
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Installing collected packages: requests, colorama, commonmark, rich, requests-toolbelt, smmap, gitdb, gitpython, tqdm, aicrowd-cli
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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 [2]:
!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 | 27.11 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 [3]:
%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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00:04.0 3D controller: NVIDIA Corporation GP100GL [Tesla P100 PCIe 16GB] (rev a1)
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README.md
CHANGELOG.md
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/content/DeepSpeech
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  Downloading attrs-21.2.0-py2.py3-none-any.whl (53 kB)
     |████████████████████████████████| 53 kB 2.5 MB/s 
Collecting pyperclip>=1.6
  Downloading pyperclip-1.8.2.tar.gz (20 kB)
Collecting colorama>=0.3.7
  Using cached colorama-0.4.4-py2.py3-none-any.whl (16 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, termcolor, wrapt, gast, pyperclip
  Building wheel for opuslib (setup.py) ... done
  Created wheel for opuslib: filename=opuslib-2.0.0-py3-none-any.whl size=11009 sha256=bdd462f4112569fdecce1e55dcdc3af08f55684e7c7d63ee41dda0128b0ee779
  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=f20855b1ef3f2a885c76ba498197d37d407efb9cdc3385e1cc14d8e9454c879b
  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=83695980bdb0cb345c258b2b96824bafb5d3488d44313bd03c5d3d86990dbd2d
  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=d8c1b94e9864f1a70b9f207b8c5a4ba81fcf51c0e6ba1eb174b5210647a0a045
  Stored in directory: /root/.cache/pip/wheels/ba/7b/eb/213741ccc0678f63e346ab8dff10495995ca3f426af87b8d88
  Building wheel for termcolor (setup.py) ... done
  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4830 sha256=d70dd29c74038890e09182929c166cddaf15cc805d5859ded0a54b2bb4e9ce2f
  Stored in directory: /root/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
  Building wheel for wrapt (setup.py) ... done
  Created wheel for wrapt: filename=wrapt-1.12.1-cp37-cp37m-linux_x86_64.whl size=68669 sha256=1c1c8a6a30a6a66185b3f1a300ccc83ee0a413c4469911898e14df037816df9a
  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=adb348ce0d5fa1b14581365e4416ce1a680a12a1791113c1435b28ad4706ff84
  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3
  Building wheel for pyperclip (setup.py) ... done
  Created wheel for pyperclip: filename=pyperclip-1.8.2-py3-none-any.whl size=11107 sha256=d4b6c1c753ba4b832eca339ad5bb8e42c130608666ef59426e6bd00bf5f1a11e
  Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28
Successfully built opuslib bs4 resampy audioread termcolor wrapt gast pyperclip
ERROR: tensorflow 1.15.4 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.21.0 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 1.2.5 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, greenlet, typing-extensions, zipp, importlib-metadata, sqlalchemy, cmaes, tqdm, colorlog, python-dateutil, MarkupSafe, Mako, python-editor, alembic, pyparsing, packaging, pbr, stevedore, PyYAML, wcwidth, PrettyTable, attrs, pyperclip, colorama, cmd2, cliff, scipy, optuna, sox, soupsieve, beautifulsoup4, bs4, pytz, pandas, urllib3, certifi, chardet, idna, requests, llvmlite, setuptools, numba, appdirs, pooch, resampy, pycparser, cffi, soundfile, joblib, audioread, decorator, threadpoolctl, scikit-learn, librosa, ds-ctcdecoder, google-pasta, termcolor, opt-einsum, wrapt, astor, protobuf, gast, werkzeug, markdown, wheel, grpcio, tensorboard, cached-property, h5py, keras-applications, keras-preprocessing, tensorflow-estimator, 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:
      Successfully uninstalled six-1.15.0
  Attempting uninstall: python-utils
    Found existing installation: python-utils 2.5.6
    Uninstalling python-utils-2.5.6:
      Successfully uninstalled python-utils-2.5.6
  Attempting uninstall: progressbar2
    Found existing installation: progressbar2 3.38.0
    Uninstalling progressbar2-3.38.0:
      Successfully uninstalled progressbar2-3.38.0
  Attempting uninstall: absl-py
    Found existing installation: absl-py 0.12.0
    Uninstalling absl-py-0.12.0:
      Successfully uninstalled absl-py-0.12.0
  Attempting uninstall: semver
    Found existing installation: semver 2.13.0
    Uninstalling semver-2.13.0:
      Successfully uninstalled semver-2.13.0
  Attempting uninstall: greenlet
    Found existing installation: greenlet 1.1.0
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  Attempting uninstall: typing-extensions
    Found existing installation: typing-extensions 3.7.4.3
    Uninstalling typing-extensions-3.7.4.3:
      Successfully uninstalled typing-extensions-3.7.4.3
  Attempting uninstall: zipp
    Found existing installation: zipp 3.4.1
    Uninstalling zipp-3.4.1:
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  Attempting uninstall: importlib-metadata
    Found existing installation: importlib-metadata 4.5.0
    Uninstalling importlib-metadata-4.5.0:
      Successfully uninstalled importlib-metadata-4.5.0
  Attempting uninstall: sqlalchemy
    Found existing installation: SQLAlchemy 1.4.18
    Uninstalling SQLAlchemy-1.4.18:
      Successfully uninstalled SQLAlchemy-1.4.18
  Attempting uninstall: tqdm
    Found existing installation: tqdm 4.61.1
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      Successfully uninstalled tqdm-4.61.1
  Attempting uninstall: python-dateutil
    Found existing installation: python-dateutil 2.8.1
    Uninstalling python-dateutil-2.8.1:
      Successfully uninstalled python-dateutil-2.8.1
  Attempting uninstall: MarkupSafe
    Found existing installation: MarkupSafe 2.0.1
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      Successfully uninstalled MarkupSafe-2.0.1
  Attempting uninstall: pyparsing
    Found existing installation: pyparsing 2.4.7
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  Attempting uninstall: packaging
    Found existing installation: packaging 20.9
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  Attempting uninstall: PyYAML
    Found existing installation: PyYAML 3.13
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  Attempting uninstall: wcwidth
    Found existing installation: wcwidth 0.2.5
    Uninstalling wcwidth-0.2.5:
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  Attempting uninstall: PrettyTable
    Found existing installation: prettytable 2.1.0
    Uninstalling prettytable-2.1.0:
      Successfully uninstalled prettytable-2.1.0
  Attempting uninstall: attrs
    Found existing installation: attrs 21.2.0
    Uninstalling attrs-21.2.0:
      Successfully uninstalled attrs-21.2.0
  Attempting uninstall: colorama
    Found existing installation: colorama 0.4.4
    Uninstalling colorama-0.4.4:
      Successfully uninstalled colorama-0.4.4
  Attempting uninstall: scipy
    Found existing installation: scipy 1.4.1
    Uninstalling scipy-1.4.1:
      Successfully uninstalled scipy-1.4.1
  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
    Uninstalling bs4-0.0.1:
      Successfully uninstalled bs4-0.0.1
  Attempting uninstall: pytz
    Found existing installation: pytz 2018.9
    Uninstalling pytz-2018.9:
      Successfully uninstalled pytz-2018.9
  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: chardet
    Found existing installation: chardet 3.0.4
    Uninstalling chardet-3.0.4:
      Successfully uninstalled chardet-3.0.4
  Attempting uninstall: idna
    Found existing installation: idna 2.10
    Uninstalling idna-2.10:
      Successfully uninstalled idna-2.10
  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: 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: resampy
    Found existing installation: resampy 0.2.2
    Uninstalling resampy-0.2.2:
      Successfully uninstalled resampy-0.2.2
  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: joblib
    Found existing installation: joblib 1.0.1
    Uninstalling joblib-1.0.1:
      Successfully uninstalled joblib-1.0.1
  Attempting uninstall: audioread
    Found existing installation: audioread 2.1.9
    Uninstalling audioread-2.1.9:
      Successfully uninstalled audioread-2.1.9
  Attempting uninstall: decorator
    Found existing installation: decorator 4.4.2
    Uninstalling decorator-4.4.2:
      Successfully uninstalled decorator-4.4.2
  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: librosa
    Found existing installation: librosa 0.7.2
    Uninstalling librosa-0.7.2:
      Successfully uninstalled librosa-0.7.2
  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: 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: 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: protobuf
    Found existing installation: protobuf 3.12.4
    Uninstalling protobuf-3.12.4:
      Successfully uninstalled protobuf-3.12.4
  Attempting uninstall: gast
    Found existing installation: gast 0.4.0
    Uninstalling gast-0.4.0:
      Successfully uninstalled gast-0.4.0
  Attempting uninstall: werkzeug
    Found existing installation: Werkzeug 1.0.1
    Uninstalling Werkzeug-1.0.1:
      Successfully uninstalled Werkzeug-1.0.1
  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: grpcio
    Found existing installation: grpcio 1.34.1
    Uninstalling grpcio-1.34.1:
      Successfully uninstalled grpcio-1.34.1
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.5.0
    Uninstalling tensorboard-2.5.0:
      Successfully uninstalled tensorboard-2.5.0
  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: 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: 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: 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 cmd2-2.1.1 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-1.38.1 h5py-3.3.0 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-1.21.0 opt-einsum-3.3.0 optuna-2.8.0 opuslib-2.0.0 packaging-20.9 pandas-1.2.5 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-1.7.0 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-1.4.19 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 urllib3-1.26.6 wcwidth-0.2.5 werkzeug-2.0.1 wheel-0.36.2 wrapt-1.12.1 zipp-3.4.1
In [ ]:
# Restarting the Runtime, run only below cells after colab has restarted
import os
os.kill(os.getpid(), 9)

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

In [1]:
!nvcc --version
!nvidia-smi
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
Sat Jun 26 09:11:32 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |
| N/A   37C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Set default CUDA version

  • A input will be asking a confirmation for changing CUDA, Press Y
In [2]:
# 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
--2021-06-26 09:11:34--  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      

2021-06-26 09:11:34 (164 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.
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Configuration file '/etc/apt/sources.list.d/cuda.list'
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drwxr-xr-x  1 root root 4096 Jun 26 09:11 bin/
lrwxrwxrwx  1 root root   20 Jun 26 09:16 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 17 13:30 etc/
drwxr-xr-x  2 root root 4096 Sep 21  2020 games/
drwxr-xr-x  2 root root 4096 Jun 17 13:41 _gcs_config_ops.so/
drwxr-xr-x  1 root root 4096 Jun 17 13:49 include/
drwxr-xr-x  1 root root 4096 Jun 17 13:49 lib/
-rw-r--r--  1 root root 1636 Jun 17 13:43 LICENSE.txt
drwxr-xr-x  3 root root 4096 Jun 17 13:40 licensing/
lrwxrwxrwx  1 root root    9 Sep 21  2020 man -> share/man/
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drwxr-xr-x  2 root root 4096 Jun 17 13:51 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
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Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==1.15.2) (1.1.2)
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Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.15.2

Importing Libraries 💻

In [3]:
# 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 [4]:

API Key valid
Saved API Key successfully!
In [5]:
# Downloading the Dataset
!rm -rf data
!mkdir data
test.csv:   0% 0.00/159k [00:00<?, ?B/s]
test.csv: 100% 159k/159k [00:00<00:00, 2.16MB/s]

train.csv: 100% 713k/713k [00:00<00:00, 5.95MB/s]
train.zip:   0% 0.00/643M [00:00<?, ?B/s]
train.zip:   5% 33.6M/643M [00:00<00:07, 76.7MB/s]

val.csv: 100% 69.1k/69.1k [00:00<00:00, 1.48MB/s]
train.zip:  26% 168M/643M [00:01<00:05, 92.8MB/s]

train.zip:  31% 201M/643M [00:02<00:04, 94.0MB/s]
train.zip:  57% 369M/643M [00:03<00:02, 106MB/s]
train.zip:  83% 537M/643M [00:05<00:01, 105MB/s]
train.zip: 100% 643M/643M [00:06<00:00, 101MB/s]


val.zip:  53% 33.6M/63.9M [00:04<00:04, 7.34MB/s]
test.zip:  84% 134M/160M [00:07<00:01, 19.2MB/s]
test.zip: 100% 160M/160M [00:08<00:00, 19.3MB/s]


val.zip: 100% 63.9M/63.9M [00:07<00:00, 8.57MB/s]

Unzipping Files

In [6]:
# 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 [7]:
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[7]:
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 [8]:
# 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 [9]:
# 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[9]:
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 [10]:
# 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 [11]:
Audio(example['wav_filename'])
Out[11]:
In [12]:
example['transcript']
'cold bosons in optical lattices'
Out[12]:
In [13]:
# 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 [14]:
%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 2048 \
--audio_sample_rate 8000 --train_batch_size 32 --dev_batch_size 32 --test_batch_size 32 \
--automatic_mixed_precision True --epochs 7 \
--test_output_file ../assets/output.txt

%cd ..
/content/DeepSpeech
I0626 09:18:25.644307 139642677270400 utils.py:157] NumExpr defaulting to 2 threads.
I Enabling automatic mixed precision training.
I Could not find best validating checkpoint.
I Could not find most recent checkpoint.
I Initializing all variables.
I STARTING Optimization
Epoch 0 |   Training | Elapsed Time: 0:04:04 | Steps: 625 | Loss: 71.048891     
Epoch 0 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 39.279181 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 39.279181 to: /root/.local/share/deepspeech/checkpoints/best_dev-625
--------------------------------------------------------------------------------
Epoch 1 |   Training | Elapsed Time: 0:03:59 | Steps: 625 | Loss: 30.299869     
Epoch 1 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 23.853241 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 23.853241 to: /root/.local/share/deepspeech/checkpoints/best_dev-1250
--------------------------------------------------------------------------------
Epoch 2 |   Training | Elapsed Time: 0:03:59 | Steps: 625 | Loss: 20.153359     
Epoch 2 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 19.341019 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 19.341019 to: /root/.local/share/deepspeech/checkpoints/best_dev-1875
--------------------------------------------------------------------------------
Epoch 3 |   Training | Elapsed Time: 0:03:59 | Steps: 625 | Loss: 14.993419     
Epoch 3 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 16.583995 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 16.583995 to: /root/.local/share/deepspeech/checkpoints/best_dev-2500
--------------------------------------------------------------------------------
Epoch 4 |   Training | Elapsed Time: 0:03:59 | Steps: 625 | Loss: 11.741581     
Epoch 4 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.556142 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 15.556142 to: /root/.local/share/deepspeech/checkpoints/best_dev-3125
--------------------------------------------------------------------------------
Epoch 5 |   Training | Elapsed Time: 0:03:59 | Steps: 625 | Loss: 9.422455      
Epoch 5 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 14.963050 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 14.963050 to: /root/.local/share/deepspeech/checkpoints/best_dev-3750
--------------------------------------------------------------------------------
Epoch 6 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 7.910507      
Epoch 6 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 14.835984 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 14.835984 to: /root/.local/share/deepspeech/checkpoints/best_dev-4375
--------------------------------------------------------------------------------
Epoch 7 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 6.878414      
Epoch 7 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 14.669199 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 14.669199 to: /root/.local/share/deepspeech/checkpoints/best_dev-5000
--------------------------------------------------------------------------------
Epoch 8 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 6.017951      
Epoch 8 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.413242 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 9 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 5.333237      
Epoch 9 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.405348 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 10 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 4.985028     
Epoch 10 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.387170 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 11 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 4.551236     
Epoch 11 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 15.505840 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 12 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 4.231648     
Epoch 12 | Validation | Elapsed Time: 0:00:08 | Steps: 63 | Loss: 15.722773 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 13 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 4.034591     
Epoch 13 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.582327 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 14 |   Training | Elapsed Time: 0:03:58 | Steps: 625 | Loss: 3.604344     
Epoch 14 | Validation | Elapsed Time: 0:00:07 | Steps: 63 | Loss: 15.857236 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
I FINISHED optimization in 1:02:33.831138
I Loading best validating checkpoint from /root/.local/share/deepspeech/checkpoints/best_dev-5000
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: 0:53:31                                 
Test on ../deepspeech_test.csv - WER: 1.000000, CER: 1.000000, loss: 441.435486
--------------------------------------------------------------------------------
Best WER: 
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 427.630646
 - wav: file:///content/data/test/3352.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "rankquantinnections"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 363.589783
 - wav: file:///content/data/test/4307.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "howtwunutrino"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 291.068573
 - wav: file:///content/data/test/1293.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "iminimunaror"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 281.977875
 - wav: file:///content/data/test/4901.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "prequalmiba"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.807692, loss: 226.536453
 - wav: file:///content/data/test/2805.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "bragolotecondo"
--------------------------------------------------------------------------------
Median WER: 
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.923077, loss: 394.371796
 - wav: file:///content/data/test/3846.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "a stochastic energy bugent"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.346154, loss: 394.275299
 - wav: file:///content/data/test/2408.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "protdocoltupar form frechurized flitter"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.846154, loss: 394.194061
 - wav: file:///content/data/test/3415.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "y eit and diffusion"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.923077, loss: 393.977234
 - wav: file:///content/data/test/3589.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "on the tolitongeometry in"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.923077, loss: 393.743713
 - wav: file:///content/data/test/1514.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "moduli of abelian covers"
--------------------------------------------------------------------------------
Worst WER: 
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.307692, loss: 292.660706
 - wav: file:///content/data/test/1732.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "inturse decies in tentlement of thig turity"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.461538, loss: 532.218323
 - wav: file:///content/data/test/225.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "pessimasm about un knon un knowns in spirs"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.500000, loss: 528.877808
 - wav: file:///content/data/test/3949.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "dicecting a s mall in fine band application"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.115385, loss: 288.116150
 - wav: file:///content/data/test/3446.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "actunal en wih un in inter atoli sys"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.230769, loss: 275.371124
 - wav: file:///content/data/test/2762.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "on the sumecoot fus is ton war growt"
--------------------------------------------------------------------------------
/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 [15]:
# 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 [16]:
# 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])
3352 rankquantinnections
In [17]:
test_df['SoundID'] = SoundID
test_df['label'] = label
test_df
Out[17]:
SoundID label wav_filename transcript wav_filesize
0 3352 rankquantinnections /content/data/test/0.wav abcdefghijklmnopqrstuvwxyz 30000
1 4307 howtwunutrino /content/data/test/1.wav abcdefghijklmnopqrstuvwxyz 30000
2 1293 iminimunaror /content/data/test/2.wav abcdefghijklmnopqrstuvwxyz 30000
3 4901 prequalmiba /content/data/test/3.wav abcdefghijklmnopqrstuvwxyz 30000
4 2805 bragolotecondo /content/data/test/4.wav abcdefghijklmnopqrstuvwxyz 30000
... ... ... ... ... ...
4995 1732 inturse decies in tentlement of thig turity /content/data/test/4995.wav abcdefghijklmnopqrstuvwxyz 30000
4996 225 pessimasm about un knon un knowns in spirs /content/data/test/4996.wav abcdefghijklmnopqrstuvwxyz 30000
4997 3949 dicecting a s mall in fine band application /content/data/test/4997.wav abcdefghijklmnopqrstuvwxyz 30000
4998 3446 actunal en wih un in inter atoli sys /content/data/test/4998.wav abcdefghijklmnopqrstuvwxyz 30000
4999 2762 on the sumecoot fus is ton war growt /content/data/test/4999.wav abcdefghijklmnopqrstuvwxyz 30000

5000 rows × 5 columns

In [19]:
# It is recommended to sort your columns before making the submission
test_df = test_df.sort_values("SoundID")
test_df
Out[19]:
SoundID label wav_filename transcript wav_filesize
1839 0 eror adalysis for probabilities /content/data/test/1839.wav abcdefghijklmnopqrstuvwxyz 30000
4059 1 stae ly lassomplitions of universal /content/data/test/4059.wav abcdefghijklmnopqrstuvwxyz 30000
19 2 ficedpointse of /content/data/test/19.wav abcdefghijklmnopqrstuvwxyz 30000
2555 3 geomotry of wagrangian graphennests /content/data/test/2555.wav abcdefghijklmnopqrstuvwxyz 30000
1943 4 creation and vanishing of /content/data/test/1943.wav abcdefghijklmnopqrstuvwxyz 30000
... ... ... ... ... ...
3761 4995 flame waves with leak singularities /content/data/test/3761.wav abcdefghijklmnopqrstuvwxyz 30000
4422 4996 interactive emcps as a toal /content/data/test/4422.wav abcdefghijklmnopqrstuvwxyz 30000
4961 4997 luceo sinses is ini ol carsmit /content/data/test/4961.wav abcdefghijklmnopqrstuvwxyz 30000
1147 4998 search for heavy /content/data/test/1147.wav abcdefghijklmnopqrstuvwxyz 30000
4221 4999 esm higgs boson searches in /content/data/test/4221.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 [20]:
# 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 [ ]:


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