awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
pannous avatar

pannous/tensorflow-speech-recognition

0
View on GitHub↗

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.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

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.
2,172 stars·633 forks·Python·31 views

Star history

Star history chart for pannous/tensorflow-speech-recognitionStar history chart for pannous/tensorflow-speech-recognition

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does pannous/tensorflow-speech-recognition do?

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.

What are the main features of pannous/tensorflow-speech-recognition?

The main features of pannous/tensorflow-speech-recognition are: Speech Transcription, Automatic Speech Recognition, Speech Recognition Libraries, Sequence-to-Sequence Models, Training Data Augmentation, Audio Speaker Embeddings, Speaker Identification Frameworks, Feature Extraction.

Which projects share features with pannous/tensorflow-speech-recognition?

Projects with overlapping indexed features include: soniqo/speech-swift — This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing,… paddlepaddle/paddlespeech — PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on… facebookresearch/fairseq — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic… alphacep/vosk-api — Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides… facebookresearch/omnilingual-asr — Omnilingual-ASR is a multilingual automatic speech recognition framework and toolkit designed to transcribe audio… modelscope/funasr — FunASR is an automatic speech recognition toolkit and multilingual speech-to-text engine designed to convert spoken…

Projects sharing features with Tensorflow Speech Recognition

These projects share indexed features with Tensorflow Speech Recognition. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • soniqo/speech-swiftsoniqo avatar

    soniqo/speech-swift

    896View on GitHub↗

    This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing, specifically engineered for Apple Silicon. It provides a framework for building real-time, full-duplex voice agents that operate entirely offline, leveraging native hardware acceleration to maintain performance and privacy. By utilizing optimized machine learning models, the library enables local execution of complex audio tasks without reliance on external cloud services. The library distinguishes itself through its specialized focus on local, high-performance voice interaction. It incl

    Swiftapple-siliconasrcoreml
    View on GitHub↗896
  • paddlepaddle/paddlespeechPaddlePaddle avatar

    PaddlePaddle/PaddleSpeech

    12,626View on GitHub↗

    PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on the PaddlePaddle deep learning framework. It provides a collection of frameworks and tools for converting spoken audio into written text, synthesizing natural audio from text, and performing direct speech translation. The toolkit includes specialized capabilities for keyword spotting to detect trigger words and speaker verification systems that extract unique voiceprints to identify and distinguish between individuals. It also features end-to-end translation tools that map audi

    Pythonasrcode-switchconformer
    View on GitHub↗12,626
  • facebookresearch/fairseqfacebookresearch avatar

    facebookresearch/fairseq

    32,228View on GitHub↗

    Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

    Python
    View on GitHub↗32,228
  • alphacep/vosk-apialphacep avatar

    alphacep/vosk-api

    14,853View on GitHub↗

    Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides a cross-platform speech toolkit with language bindings for integrating voice recognition into server environments, Android, iOS, and Raspberry Pi. The project includes a speaker identification tool to distinguish between different voices and an acoustic model trainer for building custom neural network models. These training tools enable speech feature extraction and model accuracy evaluation to improve recognition for specialized domains. The system supports real-time audio

    Jupyter Notebookandroidasrdeep-learning
    View on GitHub↗14,853
Compare all 30 related projects→

Curated searches featuring Tensorflow Speech Recognition

Hand-picked collections where Tensorflow Speech Recognition appears.
  • Open Source Speech Recognition Engines
  • Speech Synthesis and Recognition Models