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GPT-SoVITS is a few-shot voice cloning and text-to-speech (TTS) system that can train high-quality voice models from as little as 1 minute of audio data. It combines GPT-based autoregressive acoustic modeling (in GPT_SoVITS/AR/) with a SoVITS neural vocoder (BigVGAN in GPT_SoVITS/BigVGAN/) to enable zero-shot TTS from 5-second samples and fine-tuned TTS from minimal training data, supporting cross-lingual synthesis across English, Japanese, Korean, Cantonese, and Chinese. Monorepo structure with two main model families: GPT_SoVITS/AR/ contains the autoregressive acoustic model (text-to-speech backbone) with separate Lightning training modules (t2s_lightning_module.py, ONNX variants), and GPT_SoVITS/BigVGAN/ contains the neural vocoder for waveform generation. Training pipeline lives in GPT_SoVITS/AR/data/ (dataset, bucketing sampler), while GPT_SoVITS/AR/text_processing/ handles phonemization and symbol mappings; WebUI and inference code are at the repo root.

Voice cloning researchers, content creators, and voice synthesis enthusiasts who need to create synthetic speech in specific speaker voices with minimal reference audio—particularly those building multilingual TTS systems or exploring few-shot learning in speech synthesis without access to large voice datasets.

Actively developed and production-ready; the project has grown to significant popularity (evidenced by Trendshift ranking), includes Docker support, Colab notebooks, and a WebUI, with regular CI/CD workflows (.github/workflows/ shows active Docker and Windows package builds). However, the codebase is complex with many CUDA optimizations and ONNX export paths, suggesting ongoing optimization work rather than a stable, minimal feature set.

Moderate-to-high complexity risk: the project heavily relies on CUDA-specific optimizations (GPT_SoVITS/AR/models/t2s_model_cudagraph.py, GPT_SoVITS/BigVGAN/alias_free_activation/cuda/), creating potential GPU compatibility issues and maintenance burden for non-NVIDIA platforms. Dependency on PyTorch Lightning, librosa, and multiple voice processing libraries (pesq, auraloss, nnAudio) means breaking updates in those libs could require rework; also, the main branch may contain experimental features given the multiple ONNX variants alongside standard PyTorch models.

Active development on performance optimization (multiple CUDA graph and ONNX export implementations suggest recent perf work), Docker containerization (workflows for multi-platform Docker builds), and Windows package distribution (.github/build_windows_packages.ps1). The presence of both standard and ONNX model variants indicates ongoing work to support broader hardware targets and reduce inference latency.

git clone https://github.com/RVC-Boss/GPT-SoVITS.git
cd GPT-SoVITS
pip install torch numpy librosa scipy tensorboard soundfile matplotlib pesq auraloss tqdm nnAudio ninja huggingface_hub
python app.py  # or run via Colab-WebUI.ipynb for cloud training

Daily commands:

# WebUI (primary entry point)
python app.py

# Or use pre-built Colab notebooks
# Colab-WebUI.ipynb for training
# Colab-Inference.ipynb for inference only

# Docker
docker build -t gpt-sovits .
docker run --gpus all -p 7860:7860 gpt-sovits

• GPT_SoVITS/AR/models/t2s_model.py — Core autoregressive text-to-speech model implementation; essential for understanding the primary TTS inference and training pipeline
• GPT_SoVITS/TTS_infer_pack/TTS.py — High-level TTS inference interface that orchestrates the full voice cloning and synthesis workflow; entry point for end-users
• GPT_SoVITS/BigVGAN/bigvgan.py — Neural vocoder that converts mel-spectrograms to waveform; critical for final audio quality in speech synthesis
• GPT_SoVITS/AR/data/dataset.py — Dataset loading and preprocessing logic for training; determines input data handling for few-shot learning
• GPT_SoVITS/AR/modules/transformer.py — Transformer-based sequence model with attention mechanisms; backbone architecture for sequence-to-sequence TTS
• GPT_SoVITS/AR/text_processing/phonemizer.py — Text-to-phoneme conversion pipeline; critical for converting raw text to linguistic features for synthesis
• GPT_SoVITS/AR/models/t2s_lightning_module.py — PyTorch Lightning training wrapper; defines loss functions, optimization, and training loop logic

Add a New Language for Phonemization

1. Define new language phoneme symbols in the symbol table (GPT_SoVITS/AR/text_processing/symbols.py)
2. Implement language-specific phonemization rules in the phonemizer module (GPT_SoVITS/AR/text_processing/phonemizer.py)
3. Update text preprocessing to detect and route the new language (GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py)
4. Test the new language with existing inference pipeline (GPT_SoVITS/TTS_infer_pack/TTS.py)

Fine-tune the Model with Custom Voice Data

1. Prepare dataset in the expected format and configure dataset path (GPT_SoVITS/AR/data/dataset.py)
2. Create a PyTorch Lightning DataModule for your custom data (GPT_SoVITS/AR/data/data_module.py)
3. Configure training hyperparameters (learning rate, batch size, loss weights) (GPT_SoVITS/AR/models/t2s_lightning_module.py)
4. Launch training script with your config; model saves checkpoints via Lightning callbacks (GPT_SoVITS/AR/models/t2s_lightning_module.py)

Replace the Vocoder with a Different Neural Vocoder

1. Implement new vocoder architecture (Generator and Discriminator classes) (GPT_SoVITS/BigVGAN/bigvgan.py)
2. Create inference wrapper for the new vocoder (GPT_SoVITS/BigVGAN/inference.py)
3. Update TTS.py to instantiate and call your vocoder instead of BigVGAN (GPT_SoVITS/TTS_infer_pack/TTS.py)
4. Ensure mel-spectrogram shape and sample rate compatibility with your vocoder (GPT_SoVITS/AR/models/t2s_model.py)

Export Model to ONNX for Deployment

1. Use existing ONNX model class as template for conversion (GPT_SoVITS/AR/models/t2s_model_onnx.py)
2. Trace or export your modified AR model to ONNX format (GPT_SoVITS/AR/models/utils.py)
3. Verify ONNX model inference produces identical outputs to PyTorch (GPT_SoVITS/TTS_infer_pack/TTS.py)
4. Deploy ONNX model with ONNX Runtime for CPU/edge inference (GPT_SoVITS/TTS_infer_pack/TTS.py)

CUDA requirement: Code heavily uses CUDA (embedding_cudagraph.py, BigVGAN CUDA kernels); CPU-only setups will be very slow or fail. Model loading: Expects pre-trained checkpoints in specific directories (likely defined in GPT_SoVITS/AR/utils/io.py); missing checkpoints will cause silent failures or cryptic errors. Audio preprocessing: Uses librosa and nnAudio which may have different resampling algorithms—ensure consistent sample rates (typically 22050 Hz or 44100 Hz) across training and inference. ONNX export complexity: Multiple ONNX variants exist (t2s_model_onnx.py, t2s_lightning_module_onnx.py) that may not be kept in sync with the main model—export/import can fail silently. Phonemizer language dependency: Text processing assumes a specific phoneme set (symbols.py); unsupported languages will silently map to OOV tokens. Bucket sampler state: The bucket_sampler.py maintains distributed training state that must be properly saved/loaded; resuming training without correct bucket state can corrupt batch construction.

• Autoregressive Sequence Modeling — The core AR model in GPT_SoVITS/AR/ uses autoregressive decoding to generate mel-spectrogram tokens one-by-one conditioned on text and speaker embeddings; understanding this is essential for modifying inference speed, sampling strategies, or training stability.
• KV-Cache Optimization — The patched_mha_with_cache.py module implements key-value caching to avoid recomputing attention over previously generated tokens during inference; critical for understanding why separate inference and training modules exist and how to optimize deployment.
• CUDA Graph Compilation — Files like t2s_model_cudagraph.py and embedding_cudagraph.py use NVIDIA CUDA graphs to fuse multiple GPU kernels into a single graph, reducing launch overhead; understanding this is necessary for contributing GPU optimizations or diagnosing why certain model versions are faster.
• Bucket Sampling for Variable-Length Sequences — The bucket_sampler.py groups audio samples of similar lengths into buckets to minimize padding and maximize GPU utilization during training; key to understanding why distributed training state management is complex and why batch construction differs from standard samplers.
• Mel-Spectrogram and Vocoding — GPT-SoVITS operates in two stages: autoregressive model generates mel-spectrogram tokens, then BigVGAN vocoder converts mel-spectrograms to raw waveform; understanding the mel-scale frequency representation and vocoding artifacts is essential for diagnosing audio quality issues.
• ONNX Runtime Export and Inference — Multiple ONNX variants (t2s_model_onnx.py, t2s_lightning_module_onnx.py) allow model deployment on CPU and non-NVIDIA hardware; understanding ONNX opset support and operator compatibility is necessary for cross-platform inference or model optimization.
• Few-Shot Learning via Phoneme-Level Conditioning — The model achieves few-shot TTS by conditioning on a speaker embedding extracted from a short reference clip, paired with phoneme-level text input; this design allows generalization to new speakers without retraining the core model, which is the core innovation that makes 1-minute training possible.

• coqui-ai/TTS — Production-grade open-source TTS engine with similar voice cloning goals but broader language support and more mature inference pipelines; main alternative in the ecosystem.
• openai/whisper — While a speech-to-text model, it's commonly used upstream in GPT-SoVITS pipelines for automatic speech recognition to generate training labels (mentioned as integrated WebUI tool).
• NVIDIA/BigVGAN — Original BigVGAN vocoder repo that GPT-SoVITS forks and extends; essential for understanding the waveform generation component.
• RVC-Boss/RVC — Sister project from the same author; Retrieval-based Voice Conversion system that shares similar voice cloning philosophy and often used together with GPT-SoVITS for full voice customization pipelines.
• bark-team/bark — Alternative few-shot TTS approach by Suno; different architecture (uses codec-based tokens) but solves the same 'train on minimal data' problem, useful for comparing design choices.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add unit tests for GPT_SoVITS/AR/text_processing module

The text processing pipeline (phonemizer.py, symbols.py) is critical for TTS quality but has no visible test coverage. This is a high-impact area where bugs directly affect output. Testing phonemization across different languages and edge cases would catch regressions early.

• [ ] Create GPT_SoVITS/AR/text_processing/tests/ directory
• [ ] Write tests for phonemizer.py covering multiple languages and special characters
• [ ] Write tests for symbols.py to validate symbol mappings and edge cases
• [ ] Add pytest configuration to .pre-commit-config.yaml for pre-commit hook
• [ ] Create GitHub Actions workflow in .github/workflows/ to run tests on PR

Add Docker GPU validation CI workflow

The repo has Dockerfile and docker-publish.yaml but no CI workflow validating that GPU-enabled Docker images can be built and run. Given this is a CUDA/torch-heavy project, automated Docker build validation would prevent broken releases.

• [ ] Create .github/workflows/docker-gpu-validation.yaml workflow
• [ ] Add build step for Dockerfile with GPU support flags
• [ ] Add smoke test step that verifies CUDA availability and torch GPU tensors work inside container
• [ ] Test against both the windows packages build (from .github/build_windows_packages.ps1 pattern) and Linux
• [ ] Cache Docker layers to avoid slow rebuilds on every PR

Add integration tests for GPT_SoVITS/AR/models inference pipeline

The t2s_model.py, t2s_model_onnx.py, and t2s_lightning_module.py files represent the core inference engine. There are ONNX variants but no visible integration tests verifying model loading, inference, and output shape validation across PyTorch/ONNX backends.

• [ ] Create GPT_SoVITS/AR/models/tests/ directory with test_inference.py
• [ ] Add test that loads a minimal t2s_model and validates output shapes for dummy input
• [ ] Add test comparing PyTorch (t2s_model.py) vs ONNX (t2s_model_onnx.py) inference results for numerical parity
• [ ] Add test for embedding_cudagraph.py and t2s_model_cudagraph.py CUDA graph execution
• [ ] Add tests to .github/workflows/ that run with and without CUDA to catch device-specific bugs

• Add unit tests for GPT_SoVITS/AR/text_processing/phonemizer.py covering edge cases (empty strings, punctuation, non-ASCII characters) and validation of phoneme output; currently no dedicated test file exists in the repo.
• Create comprehensive docstrings and type hints for the GPT_SoVITS/AR/data/bucket_sampler.py module explaining distributed sampler state, bucket construction logic, and the role of max_tokens_per_batch; the bucketing algorithm is underdocumented.
• Build an inference benchmark script comparing latency and memory usage across the three vocoder paths (standard BigVGAN, ONNX export, CUDA graph variant) on different GPU types; would help users choose the right deployment strategy.

• High · Unspecified dependency versions in requirements.txt — requirements.txt, extra-req.txt. Most dependencies lack pinned versions (e.g., torch, numpy, scipy, tensorboard, soundfile, matplotlib, pesq, auraloss, tqdm, nnAudio, ninja). Only huggingface_hub specifies a minimum version. This allows installation of potentially vulnerable dependency versions without explicit control, increasing supply chain risk. Fix: Pin all dependency versions explicitly (e.g., 'torch==2.1.0' instead of 'torch'). Use tools like pip-audit to identify known vulnerabilities in locked versions.
• High · Multiple exposed ports without access control — Dockerfile (EXPOSE command), docker-compose.yaml (ports section). Docker configuration exposes ports 9871, 9872, 9873, 9874, 9880 without any documented security controls, firewall rules, or authentication mechanisms. These ports are directly accessible to network traffic if the container is exposed. Fix: Document which ports require public access vs. internal-only. Implement network policies, reverse proxy with authentication (nginx/traefik), and firewall rules. Consider using Docker networks instead of host-level exposure for internal services.
• High · Overly permissive Docker volumes in docker-compose.yaml — docker-compose.yaml (volumes section for both services). docker-compose.yaml mounts the entire project directory (.) into the container at /workspace/GPT-SoVITS with read-write access. This allows container processes to modify source code, dependencies, and configuration files on the host system. Fix: Use read-only mounts where possible (add ':ro' suffix). Separate input/output directories from source code. Use volume mounts instead of bind mounts for sensitive data. Implement proper access controls.
• High · Missing security context in Docker containers — docker-compose.yaml (services configuration). docker-compose.yaml does not specify user contexts, capabilities, or security options. Containers likely run as root, providing full system access if compromised. Fix: Add 'user: 1000:1000' or equivalent non-root user. Drop unnecessary capabilities with 'cap_drop: ALL' and add back only required ones. Use 'security_opt: [no-new-privileges]'.
• Medium · Potentially unsafe package from untrusted source — Dockerfile (FROM xxxxrt666/torch-base:cu${CUDA_VERSION}-${TORCH_BASE}). Base Docker image uses 'xxxxrt666/torch-base' - an unverified custom image from Docker Hub. This external dependency could contain malicious code or vulnerabilities not visible in this repository. Fix: Use official PyTorch images (pytorch/pytorch) or build base images in-house with documented, audited content. Scan base images with Trivy or similar tools before use. Implement image signature verification.
• Medium · Large shared memory allocation without limits — docker-compose.yaml (shm_size configuration). docker-compose.yaml allocates 16GB of shared memory (shm_size: '16g') for both services without resource constraints. This could enable denial-of-service or excessive resource consumption. Fix: Set appropriate resource limits (cpu, memory, cpus limits) for services. Validate shm_size allocation based on actual application needs. Monitor resource usage in production.
• Medium · No input validation evident in codebase structure — GPT_SoVITS/AR/data/dataset.py, GPT_SoVITS/BigVGAN/inference.py, GPT_SoVITS/BigVGAN/meldataset.py. File structure suggests audio processing pipelines (AR, BigVGAN, inference modules) that likely accept user-supplied audio files and text input. No evident validation or sanitization in file structure for preventing malicious input. Fix: Implement strict file format validation (MIME type, file signature verification). Sanitize text inputs before processing. Implement file size limits. Add comprehensive error handling for malformed inputs.
• Medium · Exposed CUDA capability with GPU access — undefined. docker-compose.yaml uses 'runtime: nvidia' to expose GPU resources. Combined with volume mounts and missing security context, this could allow container escape or GPU-based Fix: undefined

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
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If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/RVC-Boss/GPT-SoVITS shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live RVC-Boss/GPT-SoVITS repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/RVC-Boss/GPT-SoVITS.

What it runs against: a local clone of RVC-Boss/GPT-SoVITS — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in RVC-Boss/GPT-SoVITS | Confirms the artifact applies here, not a fork | | 2 | License is still MIT | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 39 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of RVC-Boss/GPT-SoVITS. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/RVC-Boss/GPT-SoVITS.git
#   cd GPT-SoVITS
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of RVC-Boss/GPT-SoVITS and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "RVC-Boss/GPT-SoVITS(\\.git)?\\b" \\
  && ok "origin remote is RVC-Boss/GPT-SoVITS" \\
  || miss "origin remote is not RVC-Boss/GPT-SoVITS (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(MIT)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"MIT\"" package.json 2>/dev/null) \\
  && ok "license is MIT" \\
  || miss "license drift — was MIT at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "GPT_SoVITS/AR/models/t2s_model.py" \\
  && ok "GPT_SoVITS/AR/models/t2s_model.py" \\
  || miss "missing critical file: GPT_SoVITS/AR/models/t2s_model.py"
test -f "GPT_SoVITS/TTS_infer_pack/TTS.py" \\
  && ok "GPT_SoVITS/TTS_infer_pack/TTS.py" \\
  || miss "missing critical file: GPT_SoVITS/TTS_infer_pack/TTS.py"
test -f "GPT_SoVITS/BigVGAN/bigvgan.py" \\
  && ok "GPT_SoVITS/BigVGAN/bigvgan.py" \\
  || miss "missing critical file: GPT_SoVITS/BigVGAN/bigvgan.py"
test -f "GPT_SoVITS/AR/data/dataset.py" \\
  && ok "GPT_SoVITS/AR/data/dataset.py" \\
  || miss "missing critical file: GPT_SoVITS/AR/data/dataset.py"
test -f "GPT_SoVITS/AR/modules/transformer.py" \\
  && ok "GPT_SoVITS/AR/modules/transformer.py" \\
  || miss "missing critical file: GPT_SoVITS/AR/modules/transformer.py"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 39 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~9d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/RVC-Boss/GPT-SoVITS"
  exit 1
fi

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