Voice Conversion Pipeline

Transform video audio from one person's voice to another using a comprehensive 5-step processing pipeline.

What This Skill Does

This skill helps you work with Cvoice, a voice recognition and synthesis tool that converts video audio from one speaker to another. It guides you through the complete pipeline: audio extraction, speech-to-text transcription, AI-enhanced text improvement, voice-cloned text-to-speech synthesis, and final video merging.

Step-by-Step Instructions

1. Understand the Pipeline Architecture

The system follows a modular pipeline pattern with these core components:

- `PipelineComponent[T, U]`: Generic base for all components

- `AudioProcessor`, `TextProcessor`, `VideoProcessor`: Specialized processors

- `Pipeline`: Main orchestrator

- `VoiceClonePipeline`: Orchestrates all 5 steps

- `PipelineConfig`: Configuration for pipeline settings

- `PipelineResult`: Processing outcomes

2. Set Up the Development Environment

```bash

Install dependencies using UV (preferred)

uv sync

uv sync --dev # Include dev dependencies

Verify installation

uv run cvoice info

```

3. Run the Complete Voice Conversion Pipeline

```bash

Process a video with voice conversion

uv run cvoice process input.mp4 --reference-audio reference.wav

With additional options

uv run cvoice process input.mp4 \

--reference-audio reference.wav \

--language en \

--output output.mp4 \

--keep-intermediates # Keep intermediate files for debugging

```

4. Run Individual Pipeline Steps

```bash

Step 1: Extract audio from video

uv run cvoice extract-audio input.mp4 --output audio.wav

Step 2: Transcribe audio to text

uv run cvoice transcribe audio.wav --language en

Step 3: Text improvement (handled automatically in pipeline)

Step 4: Synthesize speech with voice cloning

uv run cvoice synthesize "Hello world" --reference-audio ref.wav

Step 5: Merge audio back to video (handled in main pipeline)

```

5. Batch Processing

```bash

Process multiple videos

uv run cvoice batch input_dir/ --reference-audio reference.wav --output-dir output_dir/

```

6. Test the Implementation

```bash

Run all tests

uv run pytest

Run with coverage report

uv run pytest --cov=src --cov-report=html --cov-report=term-missing

Run specific component tests

uv run pytest tests/test_core/test_pipeline.py

Run specific test method

uv run pytest tests/test_core/test_pipeline.py::TestVoiceClonePipeline::test_pipeline_initialization

```

7. Code Quality Checks

```bash

Format code

uv run ruff format .

Lint and auto-fix issues

uv run ruff check .

uv run ruff check --fix .

Type checking

uv run mypy src/

```

8. Implement Custom Pipeline Components

All components use context managers for resource cleanup:

```python

from cvoice.core.base import PipelineComponent

class CustomProcessor(PipelineComponent[InputType, OutputType]):

def validate_input(self, data: InputType) -> bool:

# Validate input data

return True

def process(self, data: InputType) -> OutputType:

# Process data

return result

def __enter__(self):

# Load resources

return self

def __exit__(self, *args):

# Cleanup resources

pass

Usage

with CustomProcessor() as processor:

result = processor.process(input_data)

```

9. Configuration Management

The pipeline uses a centralized configuration system:

10. Error Handling and Debugging

Testing Strategy

Tests are organized by component:

Common fixtures in `tests/conftest.py`:

Heavy AI models and external APIs are mocked in tests to avoid loading large models during testing.

Performance Considerations

Key Dependencies

**Core:**

**Optional:**

Examples

**Basic voice conversion:**

```bash

uv run cvoice process interview.mp4 --reference-audio celebrity_voice.wav

```

**Batch processing with specific language:**

```bash

uv run cvoice batch videos/ --reference-audio target_voice.wav --language es --output-dir processed/

```

**Extract audio for manual inspection:**

```bash

uv run cvoice extract-audio video.mp4 --output extracted.wav

```

**Test transcription quality:**

```bash

uv run cvoice transcribe audio.wav --language en

```

Constraints