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Sound Prediction

Solution for submission 148562

A detailed solution for submission 148562 submitted for challenge Sound Prediction

KSHITIJ_076

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 [1]:
!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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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: tqdm, requests, smmap, gitdb, gitpython, requests-toolbelt, colorama, commonmark, rich, 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% (187/187), done.
remote: Total 23874 (delta 232), reused 357 (delta 211), pack-reused 23463
Receiving objects: 100% (23874/23874), 49.48 MiB | 28.40 MiB/s, done.
Resolving deltas: 100% (16366/16366), 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 Device 1eb8 (rev a1)
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README.md
CHANGELOG.md
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/content/DeepSpeech
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     |████████████████████████████████| 53 kB 2.9 MB/s 
Collecting wcwidth>=0.1.7
  Downloading wcwidth-0.2.5-py2.py3-none-any.whl (30 kB)
Collecting pyperclip>=1.6
  Downloading pyperclip-1.8.2.tar.gz (20 kB)
Collecting zipp>=0.5
  Downloading zipp-3.4.1-py3-none-any.whl (5.2 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, audioread, resampy, 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=a58e184299ede793f8e2cee7c2eee914c921a736d7065d848d30af36e4bea702
  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=75358aa65f73245390b0d9a725c3f330cb3e1405c3ce0e3e293e97fcd32b218d
  Stored in directory: /root/.cache/pip/wheels/0a/9e/ba/20e5bbc1afef3a491f0b3bb74d508f99403aabe76eda2167ca
  Building wheel for audioread (setup.py) ... done
  Created wheel for audioread: filename=audioread-2.1.9-py3-none-any.whl size=23142 sha256=939fe246fa299e203694bd01e2e0c35ca2807fe3052e489fd5d780e4bdcb8730
  Stored in directory: /root/.cache/pip/wheels/ba/7b/eb/213741ccc0678f63e346ab8dff10495995ca3f426af87b8d88
  Building wheel for resampy (setup.py) ... done
  Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320720 sha256=dbfc85058d84e26e6070d795aeef7f7af4c33c2cf140e760b66317352ee48b52
  Stored in directory: /root/.cache/pip/wheels/a0/18/0a/8ad18a597d8333a142c9789338a96a6208f1198d290ece356c
  Building wheel for termcolor (setup.py) ... done
  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4830 sha256=5e0d7d6028c346308e3546451c2fadf3f769816f39621171623424f55d790903
  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=68671 sha256=436614612849a03b0e7cd3dcb7451dc1d1cfdf5c465369767cc787682fa357da
  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=2332b3ea2fc9df263f393ac8dff5eb92154aeb2cf45d8b1d33242122364e5ac3
  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=04191e765d26314f1735d182316e2742a5a3187afbc6f04108b64b3e66d6edac
  Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28
Successfully built opuslib bs4 audioread resampy 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, python-editor, MarkupSafe, Mako, greenlet, zipp, typing-extensions, importlib-metadata, sqlalchemy, python-dateutil, alembic, colorlog, pyparsing, packaging, scipy, PyYAML, colorama, attrs, wcwidth, pyperclip, cmd2, pbr, stevedore, PrettyTable, cliff, tqdm, cmaes, optuna, sox, soupsieve, beautifulsoup4, bs4, pytz, pandas, urllib3, certifi, chardet, idna, requests, setuptools, llvmlite, numba, appdirs, pooch, joblib, audioread, pycparser, cffi, soundfile, resampy, decorator, threadpoolctl, scikit-learn, librosa, ds-ctcdecoder, protobuf, tensorflow-estimator, markdown, werkzeug, grpcio, wheel, tensorboard, astor, opt-einsum, google-pasta, termcolor, wrapt, gast, cached-property, h5py, keras-applications, keras-preprocessing, 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: MarkupSafe
    Found existing installation: MarkupSafe 2.0.1
    Uninstalling MarkupSafe-2.0.1:
      Successfully uninstalled MarkupSafe-2.0.1
  Attempting uninstall: greenlet
    Found existing installation: greenlet 1.1.0
    Uninstalling greenlet-1.1.0:
      Successfully uninstalled greenlet-1.1.0
  Attempting uninstall: zipp
    Found existing installation: zipp 3.4.1
    Uninstalling zipp-3.4.1:
      Successfully uninstalled zipp-3.4.1
  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: 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: 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: pyparsing
    Found existing installation: pyparsing 2.4.7
    Uninstalling pyparsing-2.4.7:
      Successfully uninstalled pyparsing-2.4.7
  Attempting uninstall: packaging
    Found existing installation: packaging 20.9
    Uninstalling packaging-20.9:
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  Attempting uninstall: scipy
    Found existing installation: scipy 1.4.1
    Uninstalling scipy-1.4.1:
      Successfully uninstalled scipy-1.4.1
  Attempting uninstall: PyYAML
    Found existing installation: PyYAML 3.13
    Uninstalling PyYAML-3.13:
      Successfully uninstalled PyYAML-3.13
  Attempting uninstall: colorama
    Found existing installation: colorama 0.4.4
    Uninstalling colorama-0.4.4:
      Successfully uninstalled colorama-0.4.4
  Attempting uninstall: attrs
    Found existing installation: attrs 21.2.0
    Uninstalling attrs-21.2.0:
      Successfully uninstalled attrs-21.2.0
  Attempting uninstall: wcwidth
    Found existing installation: wcwidth 0.2.5
    Uninstalling wcwidth-0.2.5:
      Successfully uninstalled wcwidth-0.2.5
  Attempting uninstall: PrettyTable
    Found existing installation: prettytable 2.1.0
    Uninstalling prettytable-2.1.0:
      Successfully uninstalled prettytable-2.1.0
  Attempting uninstall: tqdm
    Found existing installation: tqdm 4.61.1
    Uninstalling tqdm-4.61.1:
      Successfully uninstalled tqdm-4.61.1
  Attempting uninstall: beautifulsoup4
    Found existing installation: beautifulsoup4 4.6.3
    Uninstalling beautifulsoup4-4.6.3:
      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:
      Successfully uninstalled pandas-1.1.5
  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: setuptools
    Found existing installation: setuptools 46.1.3
    Uninstalling setuptools-46.1.3:
      Successfully uninstalled setuptools-46.1.3
  Attempting uninstall: llvmlite
    Found existing installation: llvmlite 0.34.0
    Uninstalling llvmlite-0.34.0:
      Successfully uninstalled llvmlite-0.34.0
  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: 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: 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: resampy
    Found existing installation: resampy 0.2.2
    Uninstalling resampy-0.2.2:
      Successfully uninstalled resampy-0.2.2
  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: protobuf
    Found existing installation: protobuf 3.12.4
    Uninstalling protobuf-3.12.4:
      Successfully uninstalled protobuf-3.12.4
  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: markdown
    Found existing installation: Markdown 3.3.4
    Uninstalling Markdown-3.3.4:
      Successfully uninstalled Markdown-3.3.4
  Attempting uninstall: werkzeug
    Found existing installation: Werkzeug 1.0.1
    Uninstalling Werkzeug-1.0.1:
      Successfully uninstalled Werkzeug-1.0.1
  Attempting uninstall: grpcio
    Found existing installation: grpcio 1.34.1
    Uninstalling grpcio-1.34.1:
      Successfully uninstalled grpcio-1.34.1
  Attempting uninstall: wheel
    Found existing installation: wheel 0.34.2
    Uninstalling wheel-0.34.2:
      Successfully uninstalled wheel-0.34.2
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.5.0
    Uninstalling tensorboard-2.5.0:
      Successfully uninstalled tensorboard-2.5.0
  Attempting uninstall: astor
    Found existing installation: astor 0.8.1
    Uninstalling astor-0.8.1:
      Successfully uninstalled astor-0.8.1
  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: 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: wrapt
    Found existing installation: wrapt 1.12.1
    Uninstalling wrapt-1.12.1:
      Successfully uninstalled wrapt-1.12.1
  Attempting uninstall: gast
    Found existing installation: gast 0.4.0
    Uninstalling gast-0.4.0:
      Successfully uninstalled gast-0.4.0
  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
    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 Runtine, 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
Sun Jun 27 05:13:19 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 T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   45C    P8    10W /  70W |      0MiB / 15109MiB |      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
--2021-06-27 05:13:23--  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      

2021-06-27 05:13:23 (172 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).
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Configuration file '/etc/apt/sources.list.d/cuda.list'
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      D     : show the differences between the versions
      Z     : start a shell to examine the situation
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drwxr-xr-x  1 root root 4096 Jun 27 05:13 bin/
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drwxr-xr-x 15 root root 4096 Jun 15 13:25 cuda-10.1/
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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/
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-rw-r--r--  1 root root 1636 Jun 17 13:43 LICENSE.txt
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Found existing installation: tensorflow 1.15.4
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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
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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"))
test_df
Out[7]:
SoundID label
0 0 abcdefghijklmnopqrstuvwxyz
1 1 abcdefghijklmnopqrstuvwxyz
2 2 abcdefghijklmnopqrstuvwxyz
3 3 abcdefghijklmnopqrstuvwxyz
4 4 abcdefghijklmnopqrstuvwxyz
... ... ...
4995 4995 abcdefghijklmnopqrstuvwxyz
4996 4996 abcdefghijklmnopqrstuvwxyz
4997 4997 abcdefghijklmnopqrstuvwxyz
4998 4998 abcdefghijklmnopqrstuvwxyz
4999 4999 abcdefghijklmnopqrstuvwxyz

5000 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")

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)
In [14]:
print(train_df)
print(test_df)
       SoundID  ... wav_filesize
0            0  ...        30000
1            1  ...        30000
2            2  ...        30000
3            3  ...        30000
4            4  ...        30000
...        ...  ...          ...
19995    19995  ...        30000
19996    19996  ...        30000
19997    19997  ...        30000
19998    19998  ...        30000
19999    19999  ...        30000

[20000 rows x 5 columns]
      SoundID  ... wav_filesize
0           0  ...        30000
1           1  ...        30000
2           2  ...        30000
3           3  ...        30000
4           4  ...        30000
...       ...  ...          ...
4995     4995  ...        30000
4996     4996  ...        30000
4997     4997  ...        30000
4998     4998  ...        30000
4999     4999  ...        30000

[5000 rows x 5 columns]

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 [29]:
%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 32 --dev_batch_size 32 --test_batch_size 32 \
--automatic_mixed_precision True --epochs 20 \
--learning_rate 0.0001 \
--augment volume[p=0.1,dbfs=-10:-40] \
--augment tempo[p=0.1,factor=1~0.5] \
--augment pitch[p=0.1,pitch=1~0.2] \
--test_output_file ../assets/output.txt

%cd ..
/content/DeepSpeech
I0627 06:35:31.378778 140159636625280 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-3745
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: 0:01:53 | Steps: 625 | Loss: 12.079065     
Epoch 0 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 13.559783 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 13.559783 to: /root/.local/share/deepspeech/checkpoints/best_dev-4370
--------------------------------------------------------------------------------
Epoch 1 |   Training | Elapsed Time: 0:01:53 | Steps: 625 | Loss: 11.233332     
Epoch 1 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.702225 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 2 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 10.461749     
Epoch 2 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 13.371430 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 13.371430 to: /root/.local/share/deepspeech/checkpoints/best_dev-5619
--------------------------------------------------------------------------------
Epoch 3 |   Training | Elapsed Time: 0:01:52 | Steps: 625 | Loss: 9.735525      
Epoch 3 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.404833 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 4 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 9.289271      
Epoch 4 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.151255 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 13.151255 to: /root/.local/share/deepspeech/checkpoints/best_dev-6868
--------------------------------------------------------------------------------
Epoch 5 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 9.051024      
Epoch 5 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.615192 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 6 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 8.719619      
Epoch 6 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.517378 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 7 |   Training | Elapsed Time: 0:01:52 | Steps: 625 | Loss: 8.703639      
Epoch 7 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 13.353948 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 8 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 8.422246      
Epoch 8 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.218811 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 9 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 8.144252      
Epoch 9 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.506369 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 10 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 8.374660     
Epoch 10 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.350564 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 11 |   Training | Elapsed Time: 0:01:49 | Steps: 625 | Loss: 7.792157     
Epoch 11 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 12.568969 | Dataset: ../deepspeech_val.csv
I Saved new best validating model with loss 12.568969 to: /root/.local/share/deepspeech/checkpoints/best_dev-11242
--------------------------------------------------------------------------------
Epoch 12 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 7.914048     
Epoch 12 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.438555 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 13 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 8.061275     
Epoch 13 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 12.764479 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 14 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 7.930612     
Epoch 14 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.118056 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 15 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 8.134998     
Epoch 15 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 12.873320 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 16 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 7.854789     
Epoch 16 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 12.868645 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 17 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 7.850710     
Epoch 17 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 12.808877 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 18 |   Training | Elapsed Time: 0:01:51 | Steps: 625 | Loss: 7.900493     
Epoch 18 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.204391 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
Epoch 19 |   Training | Elapsed Time: 0:01:50 | Steps: 625 | Loss: 8.674717     
Epoch 19 | Validation | Elapsed Time: 0:00:04 | Steps: 63 | Loss: 13.180194 | Dataset: ../deepspeech_val.csv
--------------------------------------------------------------------------------
I FINISHED optimization in 0:39:00.071176
I Loading best validating checkpoint from /root/.local/share/deepspeech/checkpoints/best_dev-11242
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: 0:50:43                                 
Test on ../deepspeech_test.csv - WER: 1.000000, CER: 1.000000, loss: 478.367401
--------------------------------------------------------------------------------
Best WER: 
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.846154, loss: 432.150299
 - wav: file:///content/data/test/1293.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "eminimumar"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 347.806061
 - wav: file:///content/data/test/1719.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "atecatof"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 333.377289
 - wav: file:///content/data/test/4901.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "prefualama"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.884615, loss: 327.096558
 - wav: file:///content/data/test/223.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "fromowwmodel"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.846154, loss: 307.293823
 - wav: file:///content/data/test/3887.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "comparintrestaoistaceon"
--------------------------------------------------------------------------------
Median WER: 
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.846154, loss: 452.670013
 - wav: file:///content/data/test/2407.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "black holes and quantum"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.923077, loss: 452.504517
 - wav: file:///content/data/test/2544.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "separating hie and betipes"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.076923, loss: 452.415741
 - wav: file:///content/data/test/1974.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "coexistence of weak andstrong"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.038462, loss: 452.069916
 - wav: file:///content/data/test/440.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "metastable behavior in markov"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.000000, loss: 451.950348
 - wav: file:///content/data/test/2635.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "orbo fold vertual cohomology"
--------------------------------------------------------------------------------
Worst WER: 
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.076923, loss: 325.620972
 - wav: file:///content/data/test/2818.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "the rectivya bobits in s in the"
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.115385, loss: 325.359161
 - wav: file:///content/data/test/2743.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "towards a achanical em f o scani"
--------------------------------------------------------------------------------
WER: 7.000000, CER: 1.038462, loss: 291.718384
 - wav: file:///content/data/test/909.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "flatnigs of a wink raps in in"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.384615, loss: 506.345276
 - wav: file:///content/data/test/225.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "pesimazm af bout unknoe un nons in spires"
--------------------------------------------------------------------------------
WER: 8.000000, CER: 1.230769, loss: 431.181610
 - wav: file:///content/data/test/1011.wav
 - src: "abcdefghijklmnopqrstuvwxyz"
 - res: "long geta inal control of ghe acos in"
--------------------------------------------------------------------------------
/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 [20]:
# 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 [21]:
# 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])
689 polynomialigenvaluebounds
In [22]:
test_df['SoundID'] = SoundID
test_df['label'] = label
In [23]:
test_df.head()
Out[23]:
SoundID label wav_filename transcript wav_filesize
0 689 polynomialigenvaluebounds /content/data/test/0.wav abcdefghijklmnopqrstuvwxyz 30000
1 3015 higsceehashing /content/data/test/1.wav abcdefghijklmnopqrstuvwxyz 30000
2 3357 nonclowehomology /content/data/test/2.wav abcdefghijklmnopqrstuvwxyz 30000
3 4415 igenergygucd /content/data/test/3.wav abcdefghijklmnopqrstuvwxyz 30000
4 76 altliodistationa /content/data/test/4.wav abcdefghijklmnopqrstuvwxyz 30000
In [24]:
# It is recommended to sort your columns before making the submission
test_df = test_df.sort_values("SoundID")

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

In [25]:
# 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 [26]:

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:
4/1AX4XfWhvvU7o41D2ik1tLupxwritk5tmoeGTQryuMK1zUBtC7kqiUUT2BzU
Mounted at /content/drive
Using notebook: /content/drive/MyDrive/Colab Notebooks/Copy of Speech Recognition for submission...
Scrubbing API keys from the notebook...
Collecting notebook...
submission.zip ━━━━━━━━━━━━━━━━━━━━ 100.0%905.4/903.8 KB1.7 MB/s0:00:00
                                                 ╭─────────────────────────╮                                                  
                                                 │ Successfully submitted! │                                                  
                                                 ╰─────────────────────────╯                                                  
                                                       Important links                                                        
┌──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/sound-prediction/submissions/148549              │
│                  │                                                                                                         │
│  All submissions │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/sound-prediction/submissions?my_submissions=true │
│                  │                                                                                                         │
│      Leaderboard │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/sound-prediction/leaderboards                    │
│                  │                                                                                                         │
│ Discussion forum │ https://discourse.aicrowd.com/c/ai-blitz-9                                                              │
│                  │                                                                                                         │
│   Challenge page │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/sound-prediction                                 │
└──────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────┘

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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