pannous/tensorflow-speech-recognition
Tensorflow Speech Recognition
This library provides a deep learning framework for training neural networks to perform speech recognition and audio classification. It utilizes sequence-to-sequence architectures to map variable-length audio inputs into text or numerical outputs, enabling the development of custom speech-to-text transcription models.
The project distinguishes itself through integrated audio processing capabilities that transform raw waveforms into spectrograms and high-dimensional numerical vectors. These tools allow for the extraction of unique vocal characteristics to identify speakers, as well as the classification of specific audio sources and spoken digits.
To support model development, the library includes utilities for audio augmentation and signal reconstruction. By programmatically modifying audio samples to simulate diverse acoustic environments and verifying the integrity of learned features through latent-space reconstruction, the system improves the robustness of its underlying neural networks.
Features
- Speech Transcription - Converts raw audio input into written text sequences using deep learning architectures designed for speech transcription.
- Automatic Speech Recognition - Provides a framework for building custom models to convert spoken human language into written text.
- Speech Recognition Libraries - Provides a deep learning framework for training neural networks to transcribe spoken audio and classify voice patterns.
- Sequence-to-Sequence Models - Implements encoder-decoder architectures to map variable-length audio sequences into text or numerical outputs.
- Training Data Augmentation - Provides programmatic audio augmentation techniques to simulate diverse acoustic environments for training robust neural networks.
- Audio Speaker Embeddings - Maps unique vocal characteristics into high-dimensional numerical vectors to isolate speaker identity from speech content.
- Speaker Identification Frameworks - Extracts unique vocal characteristics from audio recordings to distinguish between different individuals or verify speaker identity.
- Feature Extraction - Extracts voice characteristics like pitch and speed into numerical vectors to isolate a speaker's unique identity.
- Audio Classification Models - Classifies audio sources by processing features through deep learning models to distinguish speakers or spoken content.
- Spoken Digit Recognition - Maps spectrogram data to specific numerical labels to accurately recognize individual digits spoken by a human voice.
- Spectrogram Processing - Converts raw audio waveforms into spectrograms to enable neural networks to process sound as spatial data.
- Audio Feature Extraction - Provides utilities for processing raw audio into spectrograms and numerical vectors for speech recognition tasks.
