Search Results for "colab"

...At its core, it acts as a hub that wires together multiple speech-related capabilities, including TTS, speech-to-text and LLM-based control flows, behind a consistent interface. The system is designed to be deployed in several ways: you can try it online via hosted demos, spin it up in a one-click Colab environment, run it in Docker containers, or set it up locally with its environment preparation scripts. It is model-agnostic and advertises support for a variety of TTS and speech models such as ChatTTS, CosyVoice, Fish-Speech, FireredTTS and others, as well as Whisper-based ASR, giving you a flexible playground for experimenting with different speech stacks. ...

Audiblez is a tool for generating high-quality .m4b audiobooks directly from .epub e-books using the Kokoro-82M neural text-to-speech model. It focuses on making audiobook creation easy and fast: from a single command, the tool splits an e-book into chapters, synthesizes audio for each section, and then merges the results into a structured audiobook with chapter-based WAV files and a final .m4b container. The Kokoro-82M model it uses is compact (82M parameters) yet natural sounding, trained...

...Inference is provided through a Python package that uses vLLM under the hood for high-throughput, low-latency generation, including streaming examples that show how to generate audio chunks in real time. The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency.

ChatTTS_colab is a wrapper project around the ChatTTS model that focuses on “one-click” deployment, especially in Google Colab. It provides an integrated offline bundle and scripts for Windows and macOS so users can run ChatTTS locally without wrestling with complex environment setup. The repository includes Colab notebooks that launch a Gradio-based web UI and expose streaming TTS, making it possible to listen to generated audio as it is produced. A distinctive feature is the “voice gacha” system, which batch-generates many distinct voice timbres and allows users to save the ones they like into a curated voice library. ...

...It also preserves aspects of the acoustic environment, such as background noise or reverb, making the generated audio feel more like it came from the same setting as the prompt. The repository includes Python APIs, sample scripts, ready-to-use voice presets, and demos hosted on Hugging Face Spaces and Google Colab so users can try it.