babysor/mockingbird

★ 36,902PythonAudience · researcherComplexity · 4/5MaintainedSetup · hard

Mindmap

mindmap
  root((MockingBird))
    What it does
      Voice cloning
      Text to speech
      Real-time synthesis
    How it works
      Encoder extracts voice
      Synthesizer generates mel
      Vocoder makes audio
    Tech stack
      Python
      PyTorch
      GPU recommended
    Use cases
      Chinese voice synthesis
      Study voice pipelines
      Local experimentation
    Audience
      Researchers
      ML hobbyists
      Voice enthusiasts

mindmap root((MockingBird)) What it does Voice cloning Text to speech Real-time synthesis How it works Encoder extracts voice Synthesizer generates mel Vocoder makes audio Tech stack Python PyTorch GPU recommended Use cases Chinese voice synthesis Study voice pipelines Local experimentation Audience Researchers ML hobbyists Voice enthusiasts

Things people build with this

USE CASE 1

Clone a Chinese Mandarin speaker's voice from a few seconds of audio and generate new speech in that voice.

USE CASE 2

Study the architecture of a complete voice synthesis pipeline with encoder, synthesizer, and vocoder stages.

USE CASE 3

Experiment with real-time voice cloning locally without relying on cloud services.

Tech stack

PythonPyTorchGPU (CUDA)

Getting it running

Difficulty · hard Time to first run · 1day+

Requires CUDA-capable GPU, PyTorch compilation, pre-trained model downloads, and Chinese language dependencies.

License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

MockingBird is a Python-based AI voice cloning tool that can clone a person's voice from a short audio sample and then generate new speech in that cloned voice from any text you provide, in real time. The problem it solves is that training a voice synthesis model from scratch for a specific person's voice requires large amounts of data and time; MockingBird reduces that to just a few seconds of audio input. The system is built on a three-stage architecture common in modern text-to-speech research. First, an encoder model converts a short voice sample into a numerical representation of that speaker's unique vocal characteristics. Second, a synthesizer model (which the project specifically trained on Chinese Mandarin datasets including aidatatang_200zh, magicdata, and aishell3) takes text and the speaker representation and produces mel spectrograms, a visual representation of sound frequencies over time. Third, a vocoder model converts those spectrograms into actual audio waveforms. The pre-trained encoder and vocoder can be reused directly; only the synthesizer needs to be swapped for a Chinese-compatible version. A graphical toolbox and a web server interface are both available for running inference. The README notes the repository is no longer actively maintained, and the author has moved this work to a commercial service at noiz.ai. You would use this repository if you want to experiment with real-time Chinese Mandarin voice cloning locally, or if you want to study the architecture of a complete voice synthesis pipeline. The tech stack is Python, using PyTorch as the deep learning framework. A GPU is recommended for reasonable performance, though CPU operation is possible. Windows, Linux, and macOS (including Apple Silicon via Rosetta) are supported.

Copy-paste prompts

Prompt 1

How do I set up MockingBird to clone a Chinese speaker's voice and generate speech from text?

Prompt 2

Walk me through the three-stage architecture: encoder, synthesizer, and vocoder. How does each stage work?

Prompt 3

I have a short audio sample of someone speaking Mandarin. How do I use MockingBird to clone their voice and synthesize new sentences?

Prompt 4

What are the differences between the pre-trained encoder/vocoder and the synthesizer in MockingBird, and why does only the synthesizer need to be swapped for Chinese?

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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.