speakturbo-tts▌
emzod/speak-turbo · updated Apr 8, 2026
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Ultra-fast text-to-speech with ~90ms latency and 8 built-in voices.
- ›Delivers audio in approximately 90ms after daemon warmup, with first run taking 2-5 seconds for model initialization
- ›Includes 8 pre-configured voices (alba, marius, javert, jean, fantine, cosette, eponine, azelma) accessible via simple command-line flags
- ›Supports file output with configurable directory allowlisting, quiet mode, and UTF-8 text input including long-form content
- ›Auto-starting daemon with 1-hour idle
speakturbo - Talk to your Claude!
Give your agent the ability to speak to you real-time. Ultra-fast text-to-speech with ~90ms latency and 8 built-in voices.
Quick Start
# Play immediately - you should hear "Hello world" through your speakers
speakturbo "Hello world"
# Output: ⚡ 92ms → ▶ 93ms → ✓ 1245ms
# Verify it's working by saving to file
speakturbo "Hello world" -o test.wav
ls -lh test.wav # Should show ~50-100KB file
Output explained: ⚡ = first audio received, ▶ = playback started, ✓ = done
First Run
The first execution takes 2-5 seconds while the daemon starts and loads the model into memory. Subsequent calls are ~90ms to first sound.
# First run (slow - daemon starting)
speakturbo "Starting up" # ~2-5 seconds
# Second run (fast - daemon already running)
speakturbo "Now I'm fast" # ~90ms
Usage
# Basic - plays immediately (default voice: alba)
speakturbo "Hello world"
# Save to file (no audio playback)
speakturbo "Hello" -o output.wav
# Save to specific file
speakturbo "Goodbye" -o goodbye.wav
# Quiet mode (suppress status messages, still plays audio)
speakturbo "Hello" -q
# List available voices
speakturbo --list-voices
Available Voices
| Voice | Type |
|---|---|
alba |
Female (default) |
marius |
Male |
javert |
Male |
jean |
Male |
fantine |
Female |
cosette |
Female |
eponine |
Female |
azelma |
Female |
Performance
| Metric | Value |
|---|---|
| Time to first sound | ~90ms (daemon warm) |
| First run | 2-5s (daemon startup) |
| Real-time factor | ~4x faster |
| Sample rate | 24kHz mono |
Architecture
speakturbo (Rust CLI, 2.2MB)
│
│ HTTP streaming (port 7125)
▼
speakturbo-daemon (Python + pocket-tts)
│
│ Model in memory, auto-shutdown after 1hr idle
▼
Audio playback (rodio)
Text Input
- Encoding: UTF-8
- Quotes in text: Use escaping:
speakturbo "She said \"hello\"" - Long text: Supported, streams as it generates
Output Path Security
The -o flag only writes to directories that are on the allowlist. By default, these are:
/tmpand system temp directories- Your current working directory
~/.speakturbo/
If you need to write elsewhere, use --allow-dir:
speakturbo "Hello" -o /custom/path/audio.wav --allow-dir /custom/path
To permanently allow a directory, add it to ~/.speakturbo/config:
mkdir -p ~/.speakturbo && echo "/custom/path" >> ~/.speakturbo/config
The config file is one directory per line. Lines starting with # are comments.
Exit Codes
| Code | Meaning |
|---|---|
| 0 | Success (audio played/saved) |
| 1 | Error (daemon connection failed, invalid args) |
When to Use
Use speakturbo when:
- You need instant audio feedback (~90ms)
- Speed matters more than voice variety
- Built-in voices are sufficient
Use speak instead when:
- You need custom voice cloning (Morgan Freeman, etc.)
→
speak "text" --voice ~/.chatter/voices/morgan_freeman.wav - You need emotion tags like
[laugh],[sigh] - Quality/variety matters more than speed
See the speak skill documentation for full usage.
Troubleshooting
No audio plays:
# Check daemon is running
curl http://127.0.0.1:7125/health
# Expected: {"status":"ready","voices":["alba","marius",...]}
# Verify by saving to file and playing manually
speakturbo "test" -o /tmp/test.wav
afplay /tmp/test.wav # macOS
aplay /tmp/test.wav # Linux
Daemon won't start:
# Check port availability
lsof -i :7125
# Manually kill and restart
pkill -f "daemon_streaming"
speakturbo "test" # Auto-restarts daemon
First run is slow: This is expected. The daemon needs to load the ~100MB model into memory. Subsequent calls will be fast (~90ms).
Daemon Management
The daemon auto-starts on first use and auto-shuts down after 1 hour idle.
# Check status
curl http://127.0.0.1:7125/health
# Manual stop
pkill -f "daemon_streaming"
# View logs
cat /tmp/speakturbo.log
Comparison with speak
| Feature | speakturbo | speak |
|---|---|---|
| Time to first sound | ~90ms | ~4-8s |
| Voice cloning | ❌ | ✅ |
| Emotion tags | ❌ | ✅ |
| Voices | 8 built-in | Custom wav files |
| Engine | pocket-tts | Chatterbox |
How to use speakturbo-tts on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add speakturbo-tts
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches speakturbo-tts from GitHub repository emzod/speak-turbo and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate speakturbo-tts. Access the skill through slash commands (e.g., /speakturbo-tts) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★74 reviews- ★★★★★William Ndlovu· Dec 20, 2024
speakturbo-tts reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Luis Robinson· Dec 20, 2024
Keeps context tight: speakturbo-tts is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Luis Jackson· Dec 20, 2024
Registry listing for speakturbo-tts matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kofi Shah· Dec 20, 2024
speakturbo-tts fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Camila Haddad· Dec 16, 2024
Registry listing for speakturbo-tts matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Daniel Kim· Dec 16, 2024
Solid pick for teams standardizing on skills: speakturbo-tts is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Dec 12, 2024
speakturbo-tts has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arjun Sharma· Dec 12, 2024
Useful defaults in speakturbo-tts — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mei Gupta· Dec 4, 2024
Useful defaults in speakturbo-tts — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Anaya Verma· Dec 4, 2024
I recommend speakturbo-tts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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