Showing 9 open source projects for "code quality c"

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    CosyVoice

    CosyVoice

    Multi-lingual large voice generation model, providing inference

    CosyVoice is a multilingual large voice generation model that offers a full-stack solution for training, inference, and deployment of high-quality TTS systems. The model supports multiple languages, including Chinese, English, Japanese, Korean, and a range of Chinese dialects such as Cantonese, Sichuanese, Shanghainese, Tianjinese, and Wuhanese. It is designed for zero-shot voice cloning and cross-lingual or mix-lingual scenarios, so a single reference voice can be used to synthesize speech across languages and in code-switching contexts. ...
    Downloads: 1 This Week
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  • 2
    Fish Speech

    Fish Speech

    SOTA Open Source TTS

    Fish Speech is a state-of-the-art open-source text-to-speech project that has evolved into the OpenAudio series of advanced TTS models. The repository hosts the code and tooling for training, fine-tuning, and serving high-quality TTS, while the current flagship models (OpenAudio-S1 and S1-mini) are distributed via Fish Audio’s playground and Hugging Face. The models are evaluated with Seed TTS metrics and achieve exceptionally low word and character error rates, indicating strong intelligibility and alignment between text and audio. ...
    Downloads: 28 This Week
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  • 3
    FireRedTTS-2

    FireRedTTS-2

    Long-form streaming TTS system for multi-speaker dialogue generation

    FireRedTTS2 is a next-generation open-source text-to-speech (TTS) system focused on long-form, streaming speech synthesis for multi-speaker dialogue, delivering stable natural speech with context-aware prosody and reliable speaker transitions that support real-time and conversational applications. It features a specialized streaming speech tokenizer and a dual-transformer architecture that enables low latency and high-quality synthesis, making it suitable for interactive systems like...
    Downloads: 0 This Week
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  • 4
    Dia

    Dia

    A TTS model capable of generating ultra-realistic dialogue

    Dia is a neural text-to-speech model designed specifically for generating ultra-realistic dialogue in a single pass. Instead of focusing on isolated sentences or flat narration, it is optimized for conversational audio, complete with natural turn-taking, prosody, and pacing. The model can be conditioned on a reference audio sample, allowing you to control emotion, tone, and other stylistic aspects of the speech. It can also produce nonverbal vocalizations like laughter, coughs, clearing the...
    Downloads: 1 This Week
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  • 5
    Piper TTS

    Piper TTS

    A fast, local neural text to speech system

    Piper is a fast, local neural text-to-speech (TTS) system developed by the Rhasspy team. Optimized for devices like the Raspberry Pi 4, Piper enables high-quality speech synthesis without relying on cloud services, making it ideal for privacy-conscious applications. It utilizes ONNX models trained with VITS to deliver natural-sounding voices across various languages and accents. Piper is particularly suited for offline voice assistants and embedded systems.
    Downloads: 434 This Week
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  • 6
    StoryTeller

    StoryTeller

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

    ...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 commit. The final video will be saved as /out/out.mp4, alongside other intermediate images, audio files, and subtitles. For more advanced use cases, you can also directly interface with Story Teller in Python code.
    Downloads: 0 This Week
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  • 7
    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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  • 8
    VITS

    VITS

    Conditional Variational Autoencoder with Adversarial Learning

    ...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 (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. It also includes monotonic alignment search code and g2p preprocessing, which are crucial components for aligning text and speech in an end-to-end setup.
    Downloads: 3 This Week
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  • 9
    TTS

    TTS

    Deep learning for text to speech

    TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model benchmarking. ...
    Downloads: 0 This Week
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