Sound Prediction
[Getting Started Code] Sound Prediction
In this final challenge of Blitz 9, we need to predict the sentences spoken from sound.
Starter Code for Speech Recognition
Here we go, this is the last challange of Blitz 9.Now in this challange, we are not going to use any text based dataset, but we are going to predict numbers said from a sound. While, we will be learning tons to new things in this final challange, this final challange is more about putting what we learned from the last 4 challanges into practical real-world application such a Speech Recognition.
What we are going to Learn¶
- Introduction to sound based datasets.
- Using Mozilla DeepSpeech to train, evaluate and test our model.
Install packages 🗃¶
In [ ]:
!pip install aicrowd-cli
!mkdir assets
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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 [ ]:
!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 231), reused 357 (delta 211), pack-reused 23463 Receiving objects: 100% (23874/23874), 49.48 MiB | 28.28 MiB/s, done. Resolving deltas: 100% (16362/16362), done. Note: checking out 'f2e9c85880dff94115ab510cde9ca4af7ee51c19'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by performing another checkout. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -b with the checkout command again. Example: git checkout -b <new-branch-name>
Install DeepSpeech Dependencies¶
All the steps taken for this section are from Train IITM
In [ ]:
%cd /content/
!sudo apt-get install python3-venv
!sudo apt-get install python3-dev
!pip install --upgrade pip
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!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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README.md
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Building wheel for opuslib (setup.py) ... done
Created wheel for opuslib: filename=opuslib-2.0.0-py3-none-any.whl size=11009 sha256=98e41b2ed2e1f829cbdbb07aab2cb795db1bd199f3a997f87a97e554380c1cc6
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=5a958db2352d5e584645175c6483d99aa41f3ca1db37dfea5db02893b95a6604
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=e802c6fb1f9910c7a38fbdb196cc079d749a476c9a9ab6be7f916a4268e67efd
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=7f018c7c417f2dff8ba42a1a672ac3fbc5e8564397367fd445481369c060c8c4
Stored in directory: /root/.cache/pip/wheels/ba/7b/eb/213741ccc0678f63e346ab8dff10495995ca3f426af87b8d88
Building wheel for wrapt (setup.py) ... done
Created wheel for wrapt: filename=wrapt-1.12.1-cp37-cp37m-linux_x86_64.whl size=68671 sha256=4962bda46cc68853658d6d8306c07e3a46445ec00e2745b2249464391e79d352
Stored in directory: /root/.cache/pip/wheels/62/76/4c/aa25851149f3f6d9785f6c869387ad82b3fd37582fa8147ac6
Building wheel for termcolor (setup.py) ... done
Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4830 sha256=e60d48f902b4191f96ddb8fa6c2527a95ee1acfd5aa7939851d973c0d79d0835
Stored in directory: /root/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
Building wheel for gast (setup.py) ... done
Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7539 sha256=8f23445dd61aa06a1e4d103557cb41fccd9951acdd90622939a749db36cf0074
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=7710795a4f924f981fec720bf407b0b69b6f4ba6cba44f4043c21e42c430c60f
Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28
Successfully built opuslib bs4 resampy audioread wrapt termcolor gast pyperclip
ERROR: tensorflow 1.15.4 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.20.3 which is incompatible.
ERROR: tensorflow-probability 0.12.1 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.
ERROR: tensorflow-metadata 1.0.0 has requirement absl-py<0.13,>=0.9, but you'll have absl-py 0.13.0 which is incompatible.
ERROR: networkx 2.5.1 has requirement decorator<5,>=4.3, but you'll have decorator 5.0.9 which is incompatible.
ERROR: moviepy 0.2.3.5 has requirement decorator<5.0,>=4.0.2, but you'll have decorator 5.0.9 which is incompatible.
ERROR: kapre 0.3.5 has requirement tensorflow>=2.0.0, but you'll have tensorflow 1.15.4 which is incompatible.
ERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= "3.0", but you'll have pandas 1.2.4 which is incompatible.
ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
ERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.16.0 which is incompatible.
ERROR: flask 1.1.4 has requirement Werkzeug<2.0,>=0.15, but you'll have werkzeug 2.0.1 which is incompatible.
ERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.
Installing collected packages: numpy, six, python-utils, progressbar2, pyxdg, attrdict, absl-py, semver, opuslib, tqdm, zipp, typing-extensions, importlib-metadata, greenlet, sqlalchemy, PyYAML, wcwidth, pyperclip, attrs, colorama, cmd2, pbr, pyparsing, PrettyTable, stevedore, cliff, python-dateutil, python-editor, MarkupSafe, Mako, alembic, packaging, scipy, colorlog, cmaes, optuna, sox, soupsieve, beautifulsoup4, bs4, pytz, pandas, chardet, certifi, urllib3, idna, requests, llvmlite, setuptools, numba, resampy, decorator, joblib, audioread, threadpoolctl, scikit-learn, pycparser, cffi, soundfile, appdirs, pooch, librosa, ds-ctcdecoder, tensorflow-estimator, wrapt, astor, termcolor, wheel, protobuf, grpcio, gast, opt-einsum, google-pasta, keras-preprocessing, werkzeug, markdown, tensorboard, cached-property, h5py, keras-applications, tensorflow, deepspeech-training
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Uninstalling numpy-1.19.5:
Successfully uninstalled numpy-1.19.5
Attempting uninstall: six
Found existing installation: six 1.15.0
Uninstalling six-1.15.0:
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: tqdm
Found existing installation: tqdm 4.61.1
Uninstalling tqdm-4.61.1:
Successfully uninstalled tqdm-4.61.1
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: greenlet
Found existing installation: greenlet 1.1.0
Uninstalling greenlet-1.1.0:
Successfully uninstalled greenlet-1.1.0
Attempting uninstall: sqlalchemy
Found existing installation: SQLAlchemy 1.4.18
Uninstalling SQLAlchemy-1.4.18:
Successfully uninstalled SQLAlchemy-1.4.18
Attempting uninstall: PyYAML
Found existing installation: PyYAML 3.13
Uninstalling PyYAML-3.13:
Successfully uninstalled PyYAML-3.13
Attempting uninstall: wcwidth
Found existing installation: wcwidth 0.2.5
Uninstalling wcwidth-0.2.5:
Successfully uninstalled wcwidth-0.2.5
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: pyparsing
Found existing installation: pyparsing 2.4.7
Uninstalling pyparsing-2.4.7:
Successfully uninstalled pyparsing-2.4.7
Attempting uninstall: PrettyTable
Found existing installation: prettytable 2.1.0
Uninstalling prettytable-2.1.0:
Successfully uninstalled prettytable-2.1.0
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
Uninstalling MarkupSafe-2.0.1:
Successfully uninstalled MarkupSafe-2.0.1
Attempting uninstall: packaging
Found existing installation: packaging 20.9
Uninstalling packaging-20.9:
Successfully uninstalled packaging-20.9
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
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: chardet
Found existing installation: chardet 3.0.4
Uninstalling chardet-3.0.4:
Successfully uninstalled chardet-3.0.4
Attempting uninstall: certifi
Found existing installation: certifi 2021.5.30
Uninstalling certifi-2021.5.30:
Successfully uninstalled certifi-2021.5.30
Attempting uninstall: urllib3
Found existing installation: urllib3 1.24.3
Uninstalling urllib3-1.24.3:
Successfully uninstalled urllib3-1.24.3
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: 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: 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: scikit-learn
Found existing installation: scikit-learn 0.22.2.post1
Uninstalling scikit-learn-0.22.2.post1:
Successfully uninstalled scikit-learn-0.22.2.post1
Attempting uninstall: pycparser
Found existing installation: pycparser 2.20
Uninstalling pycparser-2.20:
Successfully uninstalled pycparser-2.20
Attempting uninstall: cffi
Found existing installation: cffi 1.14.5
Uninstalling cffi-1.14.5:
Successfully uninstalled cffi-1.14.5
Attempting uninstall: soundfile
Found existing installation: SoundFile 0.10.3.post1
Uninstalling SoundFile-0.10.3.post1:
Successfully uninstalled SoundFile-0.10.3.post1
Attempting uninstall: appdirs
Found existing installation: appdirs 1.4.4
Uninstalling appdirs-1.4.4:
Successfully uninstalled appdirs-1.4.4
Attempting uninstall: pooch
Found existing installation: pooch 1.4.0
Uninstalling pooch-1.4.0:
Successfully uninstalled pooch-1.4.0
Attempting uninstall: librosa
Found existing installation: librosa 0.7.2
Uninstalling librosa-0.7.2:
Successfully uninstalled librosa-0.7.2
Attempting uninstall: 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: 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: termcolor
Found existing installation: termcolor 1.1.0
Uninstalling termcolor-1.1.0:
Successfully uninstalled termcolor-1.1.0
Attempting uninstall: wheel
Found existing installation: wheel 0.34.2
Uninstalling wheel-0.34.2:
Successfully uninstalled wheel-0.34.2
Attempting uninstall: protobuf
Found existing installation: protobuf 3.12.4
Uninstalling protobuf-3.12.4:
Successfully uninstalled protobuf-3.12.4
Attempting uninstall: grpcio
Found existing installation: grpcio 1.34.1
Uninstalling grpcio-1.34.1:
Successfully uninstalled grpcio-1.34.1
Attempting uninstall: gast
Found existing installation: gast 0.4.0
Uninstalling gast-0.4.0:
Successfully uninstalled gast-0.4.0
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: 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: 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: 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: 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: 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-1.38.0 h5py-3.2.1 idna-2.10 importlib-metadata-4.5.0 joblib-1.0.1 keras-applications-1.0.8 keras-preprocessing-1.1.2 librosa-0.8.1 llvmlite-0.31.0 markdown-3.3.4 numba-0.47.0 numpy-1.20.3 opt-einsum-3.3.0 optuna-2.8.0 opuslib-2.0.0 packaging-20.9 pandas-1.2.4 pbr-5.6.0 pooch-1.4.0 progressbar2-3.53.1 protobuf-3.17.3 pycparser-2.20 pyparsing-2.4.7 pyperclip-1.8.2 python-dateutil-2.8.1 python-editor-1.0.4 python-utils-2.5.6 pytz-2021.1 pyxdg-0.27 requests-2.25.1 resampy-0.2.2 scikit-learn-0.24.2 scipy-1.6.3 semver-2.13.0 setuptools-57.0.0 six-1.16.0 soundfile-0.10.3.post1 soupsieve-2.2.1 sox-1.4.1 sqlalchemy-1.4.18 stevedore-3.3.0 tensorboard-1.15.0 tensorflow-1.15.4 tensorflow-estimator-1.15.1 termcolor-1.1.0 threadpoolctl-2.1.0 tqdm-4.61.1 typing-extensions-3.10.0.0 urllib3-1.26.5 wcwidth-0.2.5 werkzeug-2.0.1 wheel-0.36.2 wrapt-1.12.1 zipp-3.4.1
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 [ ]:
!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 19 16:36:17 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 50C 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 [ ]:
# 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
--2021-06-19 16:36:20-- 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
2021-06-19 16:36:20 (124 MB/s) - ‘cuda-repo-ubuntu1804_10.0.130-1_amd64.deb’ saved [2940/2940]
Reading package lists... Done
Building dependency tree
Reading state information... Done
libxi-dev is already the newest version (2:1.7.9-1).
libxi-dev set to manually installed.
libxmu-dev is already the newest version (2:1.1.2-2).
libxmu-dev set to manually installed.
freeglut3 is already the newest version (2.8.1-3).
freeglut3 set to manually installed.
freeglut3-dev is already the newest version (2.8.1-3).
freeglut3-dev set to manually installed.
0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.
Reading package lists... Done
Building dependency tree
Reading state information... Done
build-essential is already the newest version (12.4ubuntu1).
dkms is already the newest version (2.3-3ubuntu9.7).
dkms set to manually installed.
0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.
Selecting previously unselected package cuda-repo-ubuntu1804.
(Reading database ... 160942 files and directories currently installed.)
Preparing to unpack cuda-repo-ubuntu1804_10.0.130-1_amd64.deb ...
Unpacking cuda-repo-ubuntu1804 (10.0.130-1) ...
Setting up cuda-repo-ubuntu1804 (10.0.130-1) ...
Configuration file '/etc/apt/sources.list.d/cuda.list'
==> File on system created by you or by a script.
==> File also in package provided by package maintainer.
What would you like to do about it ? Your options are:
Y or I : install the package maintainer's version
N or O : keep your currently-installed version
D : show the differences between the versions
Z : start a shell to examine the situation
The default action is to keep your current version.
*** cuda.list (Y/I/N/O/D/Z) [default=N] ? Y
Installing new version of config file /etc/apt/sources.list.d/cuda.list ...
Executing: /tmp/apt-key-gpghome.sikyQGhrOx/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
gpg: requesting key from 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub'
gpg: key F60F4B3D7FA2AF80: "cudatools <cudatools@nvidia.com>" not changed
gpg: Total number processed: 1
gpg: unchanged: 1
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cuda-10-0 is already the newest version (10.0.130-1).
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total 80
drwxr-xr-x 1 root root 4096 Jun 19 16:35 bin/
lrwxrwxrwx 1 root root 20 Jun 19 16:36 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/
drwxr-xr-x 2 root root 4096 Sep 21 2020 sbin/
-rw-r--r-- 1 root root 7291 Jun 17 13:43 setup.cfg
drwxr-xr-x 1 root root 4096 Jun 17 13:40 share/
drwxr-xr-x 2 root root 4096 Sep 21 2020 src/
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
Downloading tensorflow_gpu-1.15.2-cp37-cp37m-manylinux2010_x86_64.whl (410.9 MB)
|████████████████████████████████| 410.9 MB 33 kB/s
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==1.15.2) (0.13.0)
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Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==1.15.2) (1.20.3)
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Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.15.2
Importing Libraries 💻¶
In [ ]:
# Importing Libraries
import pandas as pd
import re
from ast import literal_eval
import os
import librosa
# To make things more beautiful!
from rich.console import Console
from rich.table import Table
from rich import pretty
pretty.install()
from IPython.display import Audio
DATA_FOLDER = "data"
Training phase ⚙️¶
Downloading Dataset¶
Same as previous challenges, we need to download the dataset using AIcrowd CLI
In [ ]:
API_KEY = '6e1bbe51193ab0f2fd043a5060ca768c' # Please get your your API Key from [https://www.aicrowd.com/participants/me]
!aicrowd login --api-key $API_KEY
API Key valid Saved API Key successfully!
In [ ]:
# Downloading the Dataset
!rm -rf data
!mkdir data
!aicrowd dataset download --challenge sound-prediction -j 3 -o data
test.zip: 0% 0.00/160M [00:00<?, ?B/s] train.csv: 0% 0.00/713k [00:00<?, ?B/s] test.csv: 0% 0.00/159k [00:00<?, ?B/s] train.csv: 100% 713k/713k [00:00<00:00, 2.05MB/s] test.csv: 100% 159k/159k [00:00<00:00, 782kB/s] val.csv: 0% 0.00/69.1k [00:00<?, ?B/s] val.csv: 100% 69.1k/69.1k [00:00<00:00, 595kB/s] train.zip: 0% 0.00/643M [00:00<?, ?B/s] test.zip: 42% 67.1M/160M [00:04<00:05, 16.6MB/s] train.zip: 5% 33.6M/643M [00:02<00:52, 11.6MB/s] test.zip: 63% 101M/160M [00:05<00:03, 17.4MB/s] test.zip: 84% 134M/160M [00:07<00:01, 17.2MB/s] val.zip: 100% 63.9M/63.9M [00:06<00:00, 9.97MB/s] test.zip: 100% 160M/160M [00:09<00:00, 17.4MB/s] train.zip: 16% 101M/643M [00:08<00:47, 11.4MB/s] train.zip: 21% 134M/643M [00:11<00:44, 11.4MB/s] train.zip: 26% 168M/643M [00:14<00:40, 11.7MB/s] train.zip: 31% 201M/643M [00:16<00:36, 12.1MB/s] train.zip: 37% 235M/643M [00:20<00:35, 11.5MB/s] train.zip: 42% 268M/643M [00:23<00:32, 11.7MB/s] train.zip: 47% 302M/643M [00:25<00:28, 11.9MB/s] train.zip: 52% 336M/643M [00:28<00:25, 12.0MB/s] train.zip: 57% 369M/643M [00:30<00:21, 12.5MB/s] train.zip: 63% 403M/643M [00:33<00:20, 12.0MB/s] train.zip: 68% 436M/643M [00:37<00:17, 11.6MB/s] train.zip: 73% 470M/643M [00:40<00:15, 10.9MB/s] train.zip: 78% 503M/643M [00:44<00:13, 10.4MB/s] train.zip: 83% 537M/643M [00:47<00:09, 10.6MB/s] train.zip: 89% 570M/643M [00:50<00:06, 10.9MB/s] train.zip: 94% 604M/643M [00:52<00:03, 11.0MB/s] train.zip: 99% 638M/643M [00:56<00:00, 10.6MB/s] train.zip: 100% 643M/643M [00:56<00:00, 11.4MB/s]
Unzipping Files¶
In [ ]:
# Unzipping the zip files into the respective set folders
!unzip /content/data/train.zip -d /content/data/train >/dev/null
!unzip /content/data/val.zip -d /content/data/val >/dev/null
!unzip /content/data/test.zip -d /content/data/test >/dev/null
Reading the Dataset¶
In [ ]:
train_df = pd.read_csv(os.path.join(DATA_FOLDER, "train.csv"))
val_df = pd.read_csv(os.path.join(DATA_FOLDER, "val.csv"))
test_df = pd.read_csv(os.path.join(DATA_FOLDER, "test.csv"))
train_df
Out[ ]:
| SoundID | label | |
|---|---|---|
| 0 | 0 | efficient spatialtemporal context modeling for |
| 1 | 1 | on the space |
| 2 | 2 | baryogenesis through mixing |
| 3 | 3 | noncommutative gravity in three dimensions |
| 4 | 4 | effective thermal diffusivity in |
| ... | ... | ... |
| 19995 | 19995 | dixmier trace for |
| 19996 | 19996 | removahedral congruences versus permutree cong... |
| 19997 | 19997 | viscous control of minimum |
| 19998 | 19998 | new boundary harnack inequalities with |
| 19999 | 19999 | a dynamic systems |
20000 rows × 2 columns
Preprocessing the Dataset¶
In this section, we are going to add some necessary columns whcich DeepSpeech will need while model training
In [ ]:
# Preprocessing Dataset Function
def preprocess_data(df, set_name):
# Adding the Wav filepath
df['wav_filename'] = df['SoundID'].apply(lambda x : os.path.join("/content", "data", set_name+"/" +str(x) + ".wav"))
df['transcript'] = df['label']
# Addding the wav file size ( in bytes ), due to mos of the files are around 30,000 bytes, there is not much need put that
# But you can do it you want :)
df['wav_filesize'] = 30000
return df
In [ ]:
# Preprocessing all three sets
train_df = preprocess_data(train_df, "train")
val_df = preprocess_data(val_df, "val")
test_df = preprocess_data(test_df, "test")
val_df
Out[ ]:
| SoundID | label | wav_filename | transcript | wav_filesize | |
|---|---|---|---|---|---|
| 0 | 0 | injectivity in higher order | /content/data/val/0.wav | injectivity in higher order | 30000 |
| 1 | 1 | minimal constraints in the parity | /content/data/val/1.wav | minimal constraints in the parity | 30000 |
| 2 | 2 | learning to refer | /content/data/val/2.wav | learning to refer | 30000 |
| 3 | 3 | on the expressive power | /content/data/val/3.wav | on the expressive power | 30000 |
| 4 | 4 | small parts in the bernoulli | /content/data/val/4.wav | small parts in the bernoulli | 30000 |
| ... | ... | ... | ... | ... | ... |
| 1995 | 1995 | responses of small quantum systems | /content/data/val/1995.wav | responses of small quantum systems | 30000 |
| 1996 | 1996 | thermal rectification in quantum | /content/data/val/1996.wav | thermal rectification in quantum | 30000 |
| 1997 | 1997 | decomposition and unitarity in quantum | /content/data/val/1997.wav | decomposition and unitarity in quantum | 30000 |
| 1998 | 1998 | on gravitational collapse in | /content/data/val/1998.wav | on gravitational collapse in | 30000 |
| 1999 | 1999 | cogrowth and spectral gap | /content/data/val/1999.wav | cogrowth and spectral gap | 30000 |
2000 rows × 5 columns
Sound¶
Listening to some sounds with with respctive labels
In [ ]:
# Getting a sample from the dataset
example = train_df.iloc[10, :]
# Reading the sound using the path
sound, sample_rate = librosa.load(example['wav_filename'])
("Sound : ", sound), ("Label : ", sample_rate)
( ('Sound : ', array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)), ('Label : ', 22050) )
The sound is a 1D list with each value is the amplitude of the sound. And the sample_rate is show many of the sound array elements are going through the speaker in each second.
Note : Lower Your PC Volume :)
In [ ]:
Audio(example['wav_filename'])
Out[ ]:
In [ ]:
example['transcript']
'cold bosons in optical lattices'
Out[ ]:
In [ ]:
# Saving the preprocessing dataset
train_df.to_csv("deepspeech_train.csv", index=False)
val_df.to_csv("deepspeech_val.csv", index=False)
test_df.to_csv("deepspeech_test.csv", index=False)
Training the model + Validation + Testing¶
Now, using Deep Speech command line, we are going to put the path of dataset with various other parameters to train & validation every epoch, but test after all epochs are done!
In [ ]:
%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 3 \
--test_output_file ../assets/output.txt
%cd ..
/content/DeepSpeech I0619 16:40:09.254473 140646901794688 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: 0:01:48 | Steps: 625 | Loss: 65.035092 Epoch 0 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 35.207106 | Dataset: ../deepspeech_val.csv I Saved new best validating model with loss 35.207106 to: /root/.local/share/deepspeech/checkpoints/best_dev-620 -------------------------------------------------------------------------------- Epoch 1 | Training | Elapsed Time: 0:01:41 | Steps: 625 | Loss: 28.189920 Epoch 1 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 22.660299 | Dataset: ../deepspeech_val.csv I Saved new best validating model with loss 22.660299 to: /root/.local/share/deepspeech/checkpoints/best_dev-1245 -------------------------------------------------------------------------------- Epoch 2 | Training | Elapsed Time: 0:01:41 | Steps: 625 | Loss: 19.229289 Epoch 2 | Validation | Elapsed Time: 0:00:05 | Steps: 63 | Loss: 18.356063 | Dataset: ../deepspeech_val.csv I Saved new best validating model with loss 18.356063 to: /root/.local/share/deepspeech/checkpoints/best_dev-1870 -------------------------------------------------------------------------------- I FINISHED optimization in 0:05:30.269379 I Loading best validating checkpoint from /root/.local/share/deepspeech/checkpoints/best_dev-1870 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: 0:50:28 Test on ../deepspeech_test.csv - WER: 1.000000, CER: 1.000000, loss: 289.006653 -------------------------------------------------------------------------------- Best WER: -------------------------------------------------------------------------------- WER: 1.000000, CER: 0.884615, loss: 259.989227 - wav: file:///content/data/test/3793.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "froteflack" -------------------------------------------------------------------------------- WER: 1.000000, CER: 0.884615, loss: 259.470276 - wav: file:///content/data/test/3504.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "lowstelarambliquities" -------------------------------------------------------------------------------- WER: 1.000000, CER: 0.884615, loss: 234.458511 - wav: file:///content/data/test/1293.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "iminimadari" -------------------------------------------------------------------------------- WER: 1.000000, CER: 0.884615, loss: 229.874008 - wav: file:///content/data/test/3249.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "arebeon" -------------------------------------------------------------------------------- WER: 1.000000, CER: 0.884615, loss: 228.998367 - wav: file:///content/data/test/4241.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "optoracar" -------------------------------------------------------------------------------- Median WER: -------------------------------------------------------------------------------- WER: 4.000000, CER: 0.923077, loss: 257.242950 - wav: file:///content/data/test/3052.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "efficienty of free energy" -------------------------------------------------------------------------------- WER: 4.000000, CER: 1.000000, loss: 257.052032 - wav: file:///content/data/test/4864.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "symmetryes of he sochassive" -------------------------------------------------------------------------------- WER: 4.000000, CER: 0.884615, loss: 257.002289 - wav: file:///content/data/test/2427.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "large entdi davior of" -------------------------------------------------------------------------------- WER: 4.000000, CER: 1.000000, loss: 256.949615 - wav: file:///content/data/test/841.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "chening berticies of ferdex" -------------------------------------------------------------------------------- WER: 4.000000, CER: 0.961538, loss: 256.901733 - wav: file:///content/data/test/4506.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "on the computational power" -------------------------------------------------------------------------------- Worst WER: -------------------------------------------------------------------------------- WER: 8.000000, CER: 1.192308, loss: 246.295990 - wav: file:///content/data/test/4116.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "con des model in nolinear le real on" -------------------------------------------------------------------------------- WER: 8.000000, CER: 1.653846, loss: 243.259964 - wav: file:///content/data/test/2254.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "micromagnetic in theviytions if tara arg ier ring" -------------------------------------------------------------------------------- WER: 8.000000, CER: 1.269231, loss: 234.407944 - wav: file:///content/data/test/1684.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "stres les in duced fi active sfor re" -------------------------------------------------------------------------------- WER: 9.000000, CER: 1.346154, loss: 360.383484 - wav: file:///content/data/test/225.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "pesimasm about un non un nons in s pirs" -------------------------------------------------------------------------------- WER: 9.000000, CER: 1.346154, loss: 242.745834 - wav: file:///content/data/test/928.wav - src: "abcdefghijklmnopqrstuvwxyz" - res: "ab strace diic nos es for time in cerrent" -------------------------------------------------------------------------------- /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 [ ]:
# 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 [ ]:
# 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])
3793 froteflack
In [ ]:
test_df['SoundID'] = SoundID
test_df['label'] = label
test_df
Out[ ]:
| SoundID | label | wav_filename | transcript | wav_filesize | |
|---|---|---|---|---|---|
| 0 | 3793 | froteflack | /content/data/test/0.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 1 | 3504 | lowstelarambliquities | /content/data/test/1.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 2 | 1293 | iminimadari | /content/data/test/2.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 3 | 3249 | arebeon | /content/data/test/3.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4 | 4241 | optoracar | /content/data/test/4.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| ... | ... | ... | ... | ... | ... |
| 4995 | 4116 | con des model in nolinear le real on | /content/data/test/4995.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4996 | 2254 | micromagnetic in theviytions if tara arg ier ring | /content/data/test/4996.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4997 | 1684 | stres les in duced fi active sfor re | /content/data/test/4997.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4998 | 225 | pesimasm about un non un nons in s pirs | /content/data/test/4998.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4999 | 928 | ab strace diic nos es for time in cerrent | /content/data/test/4999.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
5000 rows × 5 columns
In [ ]:
# It is recommended to sort your columns before making the submission
test_df = test_df.sort_values("SoundID")
test_df
Out[ ]:
| SoundID | label | wav_filename | transcript | wav_filesize | |
|---|---|---|---|---|---|
| 692 | 0 | erranalysis for probabilities | /content/data/test/692.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4213 | 1 | paely fla competions of universal | /content/data/test/4213.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 644 | 2 | phixed pints of | /content/data/test/644.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 2465 | 3 | geometry of regrangien rahmening | /content/data/test/2465.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 1596 | 4 | creation and danshion of | /content/data/test/1596.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| ... | ... | ... | ... | ... | ... |
| 4711 | 4995 | flame wave s with weak singularities | /content/data/test/4711.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4865 | 4996 | interactive msc us as a tual | /content/data/test/4865.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 2973 | 4997 | liceipintite in ol tarsmet | /content/data/test/2973.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 762 | 4998 | search for navy | /content/data/test/762.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
| 4786 | 4999 | es em higx poson serches in | /content/data/test/4786.wav | abcdefghijklmnopqrstuvwxyz | 30000 |
5000 rows × 5 columns
Note : Please make sure that there should be filename submission.csv in assets folder before submitting it
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# 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 [21]:
!aicrowd notebook submit -c sound-prediction -a assets --no-verify
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: 4/1AY0e-g4zxv5y54X5kYZWMwfcyzcKOrtaA06NviKuz0U1kp_9LPKKq0MSc9Q Mounted at /content/drive Using notebook: /content/drive/MyDrive/Colab Notebooks/Speech Recognition for submission... Scrubbing API keys from the notebook... Collecting notebook... submission.zip ━━━━━━━━━━━━━━━━━━━━ 100.0% • 908.9/907.3 KB • 1.6 MB/s • 0:00:00 ╭─────────────────────────╮ │ Successfully submitted! │ ╰─────────────────────────╯ Important links ┌──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ This submission │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/sound-prediction/submissions/147406 │ │ │ │ │ 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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Challenge name must be “sound-prediction” !!!
!aicrowd dataset download –challenge soundprediction -j 3 -o data !aicrowd notebook submit -c soundprediction -a assets –no-verify