Скачать или смотреть 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_...

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