langlearn-tts 0.1.1

AWS Polly TTS MCP server and CLI for language learning

langlearn-tts

Text-to-speech toolkit for language learning. Provides both an MCP server (for Claude Desktop) and a CLI with identical functionality.

Currently supports AWS Polly with ElevenLabs and OpenAI TTS backends planned. The goal: pick the TTS provider that fits your setup — no AWS account required once alternative backends ship.

Features

• Single synthesis — convert text to MP3 in any supported language
• Batch synthesis — synthesize multiple texts, optionally merged into one file
• Pair synthesis — stitch two languages together: [English audio] [pause] [L2 audio]
• Pair batch — batch process vocabulary lists as stitched pairs
• Auto-play — MCP tools play audio immediately after synthesis via afplay
• Configurable speech rate — default 90% speed for learner-friendly pacing
• 93 voices, 41 languages — any voice from the AWS Polly voice list works out of the box

Prerequisites

• Python 3.13+
• uv — Python package manager
• ffmpeg — required for audio stitching
• AWS credentials configured (see AWS Configuration)

Installation

From PyPI (recommended)

# Using uv
uv tool install langlearn-tts

# Or using pip
pip install langlearn-tts

From source (development)

git clone https://github.com/jmf-pobox/langlearn-tts-mcp.git
cd langlearn-tts-mcp
uv sync

Verify the installation:

langlearn-tts --help

AWS Configuration

The tool requires AWS credentials with polly:SynthesizeSpeech and polly:DescribeVoices permissions. Configure using any standard AWS method:

Option A — AWS CLI (recommended):

aws configure

Option B — Environment variables:

export AWS_ACCESS_KEY_ID=your-key
export AWS_SECRET_ACCESS_KEY=your-secret
export AWS_DEFAULT_REGION=us-east-1

Option C — Credentials file (~/.aws/credentials):

[default]
aws_access_key_id = your-key
aws_secret_access_key = your-secret
region = us-east-1

ffmpeg

Audio stitching (pairs, merged batches) requires ffmpeg:

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

Claude Desktop Setup

Automatic (recommended)

langlearn-tts install

This registers the MCP server with Claude Desktop. Options:

• --output-dir PATH — custom audio output directory (default: ~/Claude-Audio)
• --uvx-path PATH — override the uvx binary path

Restart Claude Desktop after running install.

Manual

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "langlearn-tts": {
      "command": "/absolute/path/to/uvx",
      "args": ["--from", "langlearn-tts", "langlearn-tts-server"],
      "env": {
        "POLLY_OUTPUT_DIR": "/absolute/path/to/output/directory"
      }
    }
  }
}

Claude Desktop does not inherit your shell PATH. All paths must be absolute. Find your uvx path with which uvx.

The POLLY_OUTPUT_DIR environment variable sets the default output directory. If unset, files are saved to ~/Claude-Audio/.

Restart Claude Desktop after editing the config.

Troubleshooting

langlearn-tts doctor

Checks Python version, ffmpeg, AWS credentials, Polly access, uvx, Claude Desktop config, and output directory. Required checks must pass (exit code 1 on failure); optional checks show ○ markers.

Voices

Any voice from the AWS Polly voice list is supported. Voice names are case-insensitive. The tool queries the Polly API on first use and caches the result.

Common voices for language learning:

Voice	Language	Engine
joanna	English (US)	neural
matthew	English (US)	neural
daniel	German	neural
vicki	German	neural
lucia	Spanish (European)	neural
lupe	Spanish (US)	neural
léa	French	neural
tatyana	Russian	standard
seoyeon	Korean	neural
takumi	Japanese	neural
zhiyu	Chinese (Mandarin)	neural

The engine (neural, standard, generative, long-form) is selected automatically — neural preferred when available.

CLI Usage

# Single synthesis
langlearn-tts synthesize "Guten Morgen" --voice daniel -o morning.mp3

# Custom speech rate (percentage, default 90)
langlearn-tts synthesize "Привет" --voice tatyana --rate 70 -o privet.mp3

# Pair: English + German stitched with a pause
langlearn-tts synthesize-pair "good morning" "Guten Morgen" \
  --voice1 joanna --voice2 daniel -o pair.mp3

# Batch from JSON file (["hello", "world", "good morning"])
langlearn-tts synthesize-batch words.json -d output/

# Batch merged into single file
langlearn-tts synthesize-batch words.json -d output/ --merge --pause 800

# Pair batch from JSON file ([["strong", "stark"], ["house", "Haus"]])
langlearn-tts synthesize-pair-batch pairs.json -d output/

MCP Tools

All four tools are available in Claude Desktop once the server is configured:

Tool	Description
synthesize	Single text to MP3
synthesize_batch	Multiple texts, optionally merged
synthesize_pair	Two texts stitched with a pause
synthesize_pair_batch	Multiple pairs, optionally merged

Each tool accepts auto_play (default: true) to play audio immediately after synthesis.

Roadmap

Provider Abstraction Layer

A TTSProvider protocol that decouples CLI/MCP tools from any specific backend. Enables --provider flag, provider auto-detection from API keys, and provider-specific doctor checks.

ElevenLabs Backend

Highest voice quality. 29+ languages, 5,000+ voices, voice cloning. Setup: pip install langlearn-tts[elevenlabs] + ELEVENLABS_API_KEY env var. Free tier: 10K chars/month.

OpenAI TTS Backend

Broadest adoption — most users already have an OpenAI key. 6 built-in voices, 50+ languages. Setup: pip install langlearn-tts[openai] + OPENAI_API_KEY env var. $15/1M chars (tts-1).

Development

# Install with dev dependencies
uv sync --all-extras

# Run tests
uv run pytest tests/ -v

# Linting and formatting
uv run ruff check src/ tests/
uv run ruff format src/ tests/

# Type checking
uv run mypy src/ tests/
uv run pyright src/ tests/

License

MIT