Showing 57 open source projects for "compiler python linux"

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  • 1
    StoryTeller

    StoryTeller

    Multimodal AI Story Teller, built with Stable Diffusion, GPT, etc.

    A multimodal AI story teller, built with Stable Diffusion, GPT, and neural text-to-speech (TTS). Given a prompt as an opening line of a story, GPT writes the rest of the plot; Stable Diffusion draws an image for each sentence; a TTS model narrates each line, resulting in a fully animated video of a short story, replete with audio and visuals. To develop locally, install dev dependencies and install pre-commit hooks. This will automatically trigger linting and code quality checks before each...
    Downloads: 1 This Week
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  • 2
    VALL-E

    VALL-E

    PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)

    We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems....
    Downloads: 2 This Week
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  • 3
    VITS

    VITS

    Conditional Variational Autoencoder with Adversarial Learning

    VITS is a foundational research implementation of “VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,” a well-known neural TTS architecture. Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation...
    Downloads: 0 This Week
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  • 4
    Transformer TTS

    Transformer TTS

    Implementation of a Transformer based neural network

    TransformerTTS is an implementation of a non-autoregressive Transformer-based neural network for text-to-speech, built with TensorFlow 2. It takes inspiration from architectures like FastSpeech, FastSpeech 2, FastPitch, and Transformer TTS, and extends them with its own aligner and forward models. The system separates alignment learning and acoustic modeling: an autoregressive Transformer is used as an aligner to extract phoneme-to-frame durations, while a non-autoregressive...
    Downloads: 0 This Week
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  • 5
    Resemblyzer

    Resemblyzer

    A python package to analyze and compare voices with deep learning

    Resemblyzer is a Python package for analyzing and comparing voices with deep learning. It works by turning speech audio into a compact voice embedding that represents the speaker’s vocal characteristics. These embeddings can then be used for speaker similarity, clustering, diarization experiments, voice comparison, and audio dataset exploration. The project is useful for researchers and developers who need a practical way to reason about speaker identity without building a voice encoder from...
    Downloads: 1 This Week
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  • 6
    DC-TTS

    DC-TTS

    TensorFlow Implementation of DC-TTS: yet another text-to-speech model

    DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN...
    Downloads: 0 This Week
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  • 7
    Dia-1.6B

    Dia-1.6B

    Dia-1.6B generates lifelike English dialogue and vocal expressions

    Dia-1.6B is a 1.6 billion parameter text-to-speech model by Nari Labs that generates high-fidelity dialogue directly from transcripts. Designed for realistic vocal performance, Dia supports expressive features like emotion, tone control, and non-verbal cues such as laughter, coughing, or sighs. The model accepts speaker conditioning through audio prompts, allowing limited voice cloning and speaker consistency across generations. It is optimized for English and built for real-time performance...
    Downloads: 0 This Week
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