hello, all… anyone here has some experience in using tensorflow, keras for audio data speech processing and ASR applications??
Hi @BMandieng ,
Absolutely! TensorFlow and Keras are powerful tools for building speech processing and Automatic Speech Recognition (ASR) applications. Here’s a breakdown of their functionalities:
You’ll typically use TensorFlow to load audio data (WAV, FLAC formats) and perform pre-processing steps like:
Converting raw audio waveforms to spectrograms or Mel-frequency cepstral coefficients (MFCCs) - these representations capture the frequency content of the speech signal.
Normalization or scaling the audio features.
Splitting data into training, validation, and test sets.
Model Building:
Keras comes in handy for building the ASR model architecture. Common components include:
Convolutional Neural Networks (CNNs): Extract features from the spectrograms or MFCCs.
Recurrent Neural Networks (RNNs): Capture the temporal dependencies in speech sequences (e.g., Long Short-Term Memory (LSTM) networks).
Connectionist Temporal Classification (CTC) layer: Often used in ASR for handling variable-length input and output sequences.
Additional Tips
- Data Augmentation: Use techniques like adding noise, shifting time, or changing pitch to augment your training data and make your model more robust.
- Transfer Learning: Leverage pre-trained models such as DeepSpeech, wav2vec, or others to improve your ASR model’s performance and reduce training time.
- Evaluation: Use metrics like Word Error Rate (WER) to evaluate the performance of your ASR system.
Thank You !