DeepSpeech-examples
optuna-examples
DeepSpeech-examples | optuna-examples | |
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2 | 2 | |
801 | 599 | |
0.9% | 4.0% | |
0.0 | 8.7 | |
9 months ago | 4 days ago | |
Python | Python | |
- | MIT License |
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DeepSpeech-examples
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Whisper – open source speech recognition by OpenAI
Perhaps this could be adapted? https://github.com/mozilla/DeepSpeech-examples/blob/r0.9/mic...
- anyone know of a free online audio to text transcriber?
optuna-examples
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[D]How to optimize an ANN?
Check out the examples for Optuna, a popular hyper parameter tuning package. It has examples for most popular ML frameworks including Xgboost, so you can see how it compares to an ANN framework like Keras or PyTorch.
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Data Scientists are dying out
That's still regular ML because you are in charge of the features. Optuna might make your life easier though: https://github.com/optuna/optuna-examples/blob/main/xgboost/xgboost_simple.py
What are some alternatives?
whisper
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
openai-whisper-realtime - A quick experiment to achieve almost realtime transcription using Whisper.
optuna - A hyperparameter optimization framework
PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
py-webrtcvad - Python interface to the WebRTC Voice Activity Detector
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.