- Category
- AI Tools
- Rank
- No. 512Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- bytedance
- GitHub
- 6.1k stars
- Date
About
ByteDance's open-source text-to-speech model — high-quality voice synthesis with multilingual support and voice cloning.
What it does
MegaTTS3 turns a reference recording and target text into new speech carrying the reference speaker’s traits. Its pipeline aligns the prompt, converts text to phonemes, predicts timing, generates acoustic latents with a diffusion transformer, then decodes them into 24 kHz audio. It handles Chinese, English, code-switching, and adjustable accent retention.
Why it's ranked here
The architecture offers unusually direct control over intelligibility, speaker similarity, and accent strength, with both terminal and browser workflows. The practical catch is substantial: public checkpoints omit the WaveVAE encoder, so cloning a new voice locally requires a matching latent supplied through the project’s screened request process. That makes it compelling for research and prepared voices, but constrained for independent deployment.
What's good
The system exposes useful controls instead of hiding synthesis behind one preset. Separate weights tune intelligibility and speaker similarity, while duration prediction and punctuation-aware text splitting support longer passages. It accepts CPU inference, optional GPU execution, Docker, a command-line interface, and a local Gradio interface. The aligner, phoneme converter, and waveform codec also have documented uses beyond synthesis.
Tradeoffs
Fresh voice enrollment is not self-contained because the released checkpoints exclude WaveVAE encoder parameters. Local inference therefore needs both a reference recording and its pre-extracted latent. Windows and Docker support remain under testing, while Windows requires dependency substitutions. CPU generation is supported but the documented example takes about 30 seconds for ten inference steps. Fine-grained pronunciation and duration adjustment is promised, not available.
How to use it well
Use it for academic experiments, bilingual narration, accented cross-language speech, or controlled cloning with an approved latent already available. Start locally through the browser interface, then move repeatable batches to the command line and tune intelligibility and similarity weights against clean prompts. Do not choose it when your workflow requires unrestricted, fully local enrollment of arbitrary new speakers or a hosted production API.
Technical notes+
tts/infer_cli.py builds MegaTTS3DiTInfer from duration, diffusion-transformer, aligner, G2P, and WaveVAE checkpoints; preprocessing aligns prompt audio and loads a .npy latent when the encoder is absent. tts/modules/llm_dit/dit.py performs classifier-free guided flow inference with a sway-adjusted schedule and AMO sampling, while tts/frontend_function.py supplies phonemes, tones, alignments, and durations. tts/utils/audio_utils/io.py loudness-normalizes, crossfades chunks, and writes 24 kHz WAV bytes. tts/gradio_api.py runs one spawned worker with concurrency limited to one and refuses non-local binding without username and password. requirements.txt pins PyTorch 2.6.0, torchaudio 2.6.0, and Gradio 5.23.1.
Observed
- License
- Apache-2.0 for the project; the conditional flow matching file retains its MIT license.
- Primary language
- Python with PyTorch.
- Install surface
- Repository clone plus pip requirements; Conda, virtualenv, and Docker workflows are documented.
- Interfaces
- Command-line inference and a Gradio web interface.
- Platform support
- Linux is the stated platform; Windows and Docker are under testing.
- Language support
- Chinese, English, and code-switching.
- Model distribution
- Pretrained checkpoints are distributed through Google Drive and Hugging Face.
- Voice enrollment
- Released checkpoints omit WaveVAE encoder parameters, so local decoder-only inference requires a matching pre-extracted latent.
Read from readme.md, requirements.txt, tts/infer_cli.py, tts/gradio_api.py, tts/frontend_function.py, tts/modules/llm_dit/cfm.py, tts/modules/llm_dit/dit.py, tts/utils/audio_utils/io.py, tts/utils/commons/hparams.py, tts/utils/audio_utils/plot.py, tts/utils/audio_utils/align.py, tts/utils/commons/ckpt_utils.py, tts/modules/llm_dit/transformer.py, tts/utils/text_utils/split_text.py.
What it can do
Convert text to speech
Text string → Audio file with synthesized speech
Synthesize speech in multiple languages
Text in various languages → Audio with language-appropriate pronunciation
Clone voice characteristics
Reference audio sample and target text → Speech audio matching the reference voice
Generate high-quality voice synthesis
Text content → High-fidelity audio with natural-sounding speech
Process multilingual text for speech generation
Mixed-language text document → Audio with appropriate pronunciation for each language
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