AI Voice Clone with Colab + Qwen3-TTS (Free) скачать в хорошем качестве

AI Voice Clone with Colab + Qwen3-TTS (Free)

Qwen3-TTS is a multilingual text-to-speech model with high-quality zero-shot voice cloning capabilities. It uses a discrete speech-token language-model architecture combined with a flow-matching decoder to generate natural speech from just 3-20 seconds of reference audio. #Qwen3 #TTS #Colab #AI #VoiceCloning 📂 Project Source Code: https://github.com/artcore-c/AI-Voice... How It Works Voice Cloning Process: Audio Analysis: The model analyzes your reference audio to extract acoustic features (pitch, tone, cadence, speaking style) Speaker Encoding: These features are encoded into a speaker embedding that represents your vocal identity Text-to-Speech Generation: Given new text, the model synthesizes speech that matches the learned speaker characteristics Waveform Synthesis: The generated speech tokens are decoded into a high-quality waveform Technical Stack: Model: Qwen3-TTS (0.6B and 1.7B variants) Framework: PyTorch with CUDA support Inference: Runs on Google Colab's free T4 GPU (16GB VRAM) Sample Rate: 24kHz output Languages: Supports Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian 📌Required: Google Colab - provides free access to GPU-accelerated computing, which is essential for running large neural network models like Qwen3-TTS. Voice synthesis on CPU would take significantly longer (10-20x slower). The free T4 GPU tier is sufficient for generating voice clones without requiring local hardware or paid cloud services. Intended Use Cases Consistent narration for storytelling, tutorials, and educational content Editing specific audio sections without full re-recording Creating voiceovers when recording conditions aren't ideal Maintaining voice consistency across multiple recording sessions Generating placeholder audio for video editing workflows Notes: Recording Equipment (Minimum Recommended) Recommended for best results while recording audio samples: USB audio interface (we used an Arturia MiniFuse 2) Condenser or shotgun microphone (we used an Audio-Technica AT875R) Quiet recording environment Acceptable minimum: Smartphone (eg. iPhone 8+) in a quiet room USB microphone with cardioid pattern Desktop/Laptop built-in mic in very quiet environment (quality will be lower) Background noise: More important than mic quality. Record in a quiet space. Disclaimers: Not sponsored. I purchased everything. I list some affiliate links to items shown in the video, alongside non-affiliate links. All affiliate links are noted, and their inclusion helps support this channel. License: All code and documentation: Released under Apache 2.0 License Qwen3-TTS models are released under Apache 2.0, allowing both commercial and non-commercial use. Acknowledgements This project builds upon: Qwen3-TTS - Open-source models and framework https://github.com/QwenLM/Qwen-TTS Google Colab - Free GPU infrastructure https://colab.research.google.com PyTorch - Deep learning framework https://pytorch.org Voice sample: "I'll fly with you, Takion" by deleted_user_1390811 via Freesound.org https://freesound.org/people/deleted_... 👋 Follow My Work: If You liked this video and would like to see more of what I'm doing, then be sure and check out my: Artstation https://www.artstation.com/unicorn-1 Github https://github.com/artcore-c Medium   / gingerbreadcocoa   YouTube    / @3dcharacterart