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Showing 7 open source projects for "python samples"
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ElevenLabs Python
The official Python SDK for the ElevenLabs API
elevenlabs-python is the official Python SDK for the ElevenLabs API, giving developers a convenient way to access ElevenLabs’ high-quality, lifelike voices. The library wraps the HTTP API into a typed Python client, so you can perform text-to-speech, streaming, voice cloning, voice management, and agents-related operations with simple method calls. It exposes ElevenLabs’ main models such as Eleven Multilingual v2, Eleven Flash v2.5, and Eleven Turbo v2.5, each targeting different trade-offs... -
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VoxCPM2
Tokenizer-Free TTS for Multilingual Speech Generation
VoxCPM2 is an advanced open-source text-to-speech system that redefines speech synthesis by eliminating traditional tokenization and instead generating continuous speech representations through a diffusion-based autoregressive architecture. Built on top of the MiniCPM model family, it enables highly natural, expressive, and context-aware speech generation that adapts tone, emotion, and pacing directly from input text. The system is trained on massive multilingual datasets, enabling support... -
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VibeVoice ComfyUI
ComfyUI integration for Microsoft's VibeVoice text-to-speech model
VibeVoice ComfyUI is a comprehensive wrapper that integrates Microsoft’s VibeVoice text-to-speech models directly into ComfyUI workflows. It exposes VibeVoice as a set of custom nodes so you can build single-speaker and multi-speaker voice generation pipelines visually, combining TTS with other audio or generative components. The integration supports high-quality single-speaker synthesis as well as scripted multi-speaker conversations, with optional voice cloning from audio samples for each... -
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OuteTTS
Interface for OuteTTS models
OuteTTS is an interface library for running OuteTTS text-to-speech models across a range of backends, making it easier to deploy the same model on different hardware and runtimes. It provides a high-level Interface API that wraps model configuration, speaker handling, and audio generation so you can focus on integrating speech into your application rather than wiring up low-level engines. The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face... -
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StreamSpeech
StreamSpeech is a seamless model for offline speech recognition
StreamSpeech is an “all-in-one” speech model designed to perform offline and simultaneous speech recognition, speech translation, and speech synthesis within a single unified architecture. Developed as part of an ACL 2024 paper, it targets streaming and low-latency scenarios where intermediate results and final translations or synthetic speech must be produced continuously as audio is being received. The model supports eight tasks: offline ASR, speech-to-text translation, speech-to-speech... -
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VideoChat
Real-time voice interactive digital human
VideoChat is a real-time voice-interactive “digital human” system that combines automatic speech recognition, large language models, text-to-speech, and talking-head generation into a single conversational pipeline. It supports both pure end-to-end voice solutions based on multimodal large language models (GLM-4-Voice feeding directly into talking-head generation) and a more traditional cascaded pipeline using ASR → LLM → TTS → talking head. It is built as a Gradio Python demo, exposing a... -
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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...
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