Vibecode ElevenLabs
track this build5 steps, step by step0%NOT REALLY · don't bother
price $22/moyou'd save $264/yrbuild time not realistically solocategory 🎧 ai audioreplaced by 0 people
A TTS wrapper is easy, but high-quality voice models, voice cloning safety, dubbing workflows, licensing, and compute are the product.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of ElevenLabs. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # ElevenLabs indie build ## Goal Build the smallest trustworthy replacement for the core ElevenLabs workflow for one developer or a tiny team. ## Scope Build a text-to-speech UI around an open model or API, store generated files, and expose voice presets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - voice quality - multilingual dubbing - voice design - safety controls - rights/licensing - model updates If those capabilities are essential, use OpenVoice instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a personal text-to-speech workbench, the DIY slice of ElevenLabs. Requirements: - A Node CLI plus a small local web page (Express, localhost only): paste text, pick a voice preset, get an mp3. - Engine 1: Piper or Kokoro running locally on CPU, free and private. Engine 2: optional OpenAI TTS fallback, key in .env, for when quality beats privacy. - Save every generation to ~/TTS/YYYY-MM-DD/<slug>.mp3 with a sidecar .txt holding the input text plus the engine and voice used. - Voice presets in voices.json: name, engine, voice id, speed. - Batch mode: point it at a folder of .txt files, get a folder of mp3s, for narrating notes or articles. - No accounts, no telemetry, local-first; the only network calls are the optional hosted API. - Out of scope: voice cloning, dubbing, and emotional voice direction. Never clone a real person's voice; that is exactly the part that should not be DIY. - README: model download steps, and state honestly that the voice quality gap versus ElevenLabs is real; frontier voice models plus licensing are the product and cannot be rebuilt solo. ## Required capabilities - TTS API or local model - GPU if local - storage - consent/safety checks - audio export ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of ElevenLabs. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # ElevenLabs indie build ## Goal Build the smallest trustworthy replacement for the core ElevenLabs workflow for one developer or a tiny team. ## Scope Build a text-to-speech UI around an open model or API, store generated files, and expose voice presets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - voice quality - multilingual dubbing - voice design - safety controls - rights/licensing - model updates If those capabilities are essential, use OpenVoice instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a personal text-to-speech workbench, the DIY slice of ElevenLabs. Requirements: - A Node CLI plus a small local web page (Express, localhost only): paste text, pick a voice preset, get an mp3. - Engine 1: Piper or Kokoro running locally on CPU, free and private. Engine 2: optional OpenAI TTS fallback, key in .env, for when quality beats privacy. - Save every generation to ~/TTS/YYYY-MM-DD/<slug>.mp3 with a sidecar .txt holding the input text plus the engine and voice used. - Voice presets in voices.json: name, engine, voice id, speed. - Batch mode: point it at a folder of .txt files, get a folder of mp3s, for narrating notes or articles. - No accounts, no telemetry, local-first; the only network calls are the optional hosted API. - Out of scope: voice cloning, dubbing, and emotional voice direction. Never clone a real person's voice; that is exactly the part that should not be DIY. - README: model download steps, and state honestly that the voice quality gap versus ElevenLabs is real; frontier voice models plus licensing are the product and cannot be rebuilt solo. ## Required capabilities - TTS API or local model - GPU if local - storage - consent/safety checks - audio export ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of ElevenLabs. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # ElevenLabs product brief ## Problem A TTS wrapper is easy, but high-quality voice models, voice cloning safety, dubbing workflows, licensing, and compute are the product. ## Product outcome Build a text-to-speech UI around an open model or API, store generated files, and expose voice presets. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - TTS API or local model - GPU if local - storage - consent/safety checks - audio export ## Explicit non-goals for v1 - voice quality - multilingual dubbing - voice design - safety controls - rights/licensing - model updates ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build me a personal text-to-speech workbench, the DIY slice of ElevenLabs. Requirements: - A Node CLI plus a small local web page (Express, localhost only): paste text, pick a voice preset, get an mp3. - Engine 1: Piper or Kokoro running locally on CPU, free and private. Engine 2: optional OpenAI TTS fallback, key in .env, for when quality beats privacy. - Save every generation to ~/TTS/YYYY-MM-DD/<slug>.mp3 with a sidecar .txt holding the input text plus the engine and voice used. - Voice presets in voices.json: name, engine, voice id, speed. - Batch mode: point it at a folder of .txt files, get a folder of mp3s, for narrating notes or articles. - No accounts, no telemetry, local-first; the only network calls are the optional hosted API. - Out of scope: voice cloning, dubbing, and emotional voice direction. Never clone a real person's voice; that is exactly the part that should not be DIY. - README: model download steps, and state honestly that the voice quality gap versus ElevenLabs is real; frontier voice models plus licensing are the product and cannot be rebuilt solo. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted ElevenLabs capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# ElevenLabs indie build ## Goal Build the smallest trustworthy replacement for the core ElevenLabs workflow for one developer or a tiny team. ## Scope Build a text-to-speech UI around an open model or API, store generated files, and expose voice presets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - voice quality - multilingual dubbing - voice design - safety controls - rights/licensing - model updates If those capabilities are essential, use OpenVoice instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build me a personal text-to-speech workbench, the DIY slice of ElevenLabs. Requirements: - A Node CLI plus a small local web page (Express, localhost only): paste text, pick a voice preset, get an mp3. - Engine 1: Piper or Kokoro running locally on CPU, free and private. Engine 2: optional OpenAI TTS fallback, key in .env, for when quality beats privacy. - Save every generation to ~/TTS/YYYY-MM-DD/<slug>.mp3 with a sidecar .txt holding the input text plus the engine and voice used. - Voice presets in voices.json: name, engine, voice id, speed. - Batch mode: point it at a folder of .txt files, get a folder of mp3s, for narrating notes or articles. - No accounts, no telemetry, local-first; the only network calls are the optional hosted API. - Out of scope: voice cloning, dubbing, and emotional voice direction. Never clone a real person's voice; that is exactly the part that should not be DIY. - README: model download steps, and state honestly that the voice quality gap versus ElevenLabs is real; frontier voice models plus licensing are the product and cannot be rebuilt solo. ## Required capabilities - TTS API or local model - GPU if local - storage - consent/safety checks - audio export ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# ElevenLabs product brief ## Problem A TTS wrapper is easy, but high-quality voice models, voice cloning safety, dubbing workflows, licensing, and compute are the product. ## Product outcome Build a text-to-speech UI around an open model or API, store generated files, and expose voice presets. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - TTS API or local model - GPU if local - storage - consent/safety checks - audio export ## Explicit non-goals for v1 - voice quality - multilingual dubbing - voice design - safety controls - rights/licensing - model updates ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build me a personal text-to-speech workbench, the DIY slice of ElevenLabs. Requirements: - A Node CLI plus a small local web page (Express, localhost only): paste text, pick a voice preset, get an mp3. - Engine 1: Piper or Kokoro running locally on CPU, free and private. Engine 2: optional OpenAI TTS fallback, key in .env, for when quality beats privacy. - Save every generation to ~/TTS/YYYY-MM-DD/<slug>.mp3 with a sidecar .txt holding the input text plus the engine and voice used. - Voice presets in voices.json: name, engine, voice id, speed. - Batch mode: point it at a folder of .txt files, get a folder of mp3s, for narrating notes or articles. - No accounts, no telemetry, local-first; the only network calls are the optional hosted API. - Out of scope: voice cloning, dubbing, and emotional voice direction. Never clone a real person's voice; that is exactly the part that should not be DIY. - README: model download steps, and state honestly that the voice quality gap versus ElevenLabs is real; frontier voice models plus licensing are the product and cannot be rebuilt solo. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted ElevenLabs capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
They pay for convincing voices, controls, and commercial workflow reliability.
xvoice quality
xmultilingual dubbing
xvoice design
xsafety controls
xrights/licensing
xmodel updates
share on X ↗"I just replaced ElevenLabs ($22/mo) with one prompt"
Don't feel like building it? These folks already made it free.
VoiceboxA local voice studio with cloning, seven engines, transcription and a multi-track editor; dubbing still takes manual assembly.open source↗
pyVideoTransDrop in a video and it does the dull chain, transcribe, translate, dub, remux, without billing by the minute.open source↗
TTS WebUIA 10GB local voice lab with an installer and every model drawer open; model licences are your homework.open source↗all 3 free alternatives to ElevenLabs →· no votes, no pay-to-list · just what's real
ElevenLabs pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0/user | $0/user | 10,000 credits/month, roughly 10 minutes of standard text-to-speech in the web app; no rollover |
| starter | $6/user | $5/user | 30,000 credits/month, roughly 30 minutes of standard text-to-speech |
| creator | $22/user | $18.33/user | 121,000 credits/month, roughly 121 minutes of standard text-to-speech |
| pro | $99/user | $82.50/user | 600,000 credits/month, roughly 600 minutes of standard text-to-speech |
| scale | $299/workspace | $249.17/workspace | 1.8 million credits/month, roughly 1,800 TTS minutes, and 3 seats |
| business | $990/workspace | $825/workspace | 6 million credits/month, roughly 6,000 TTS minutes, and 10 seats |
| enterprise | custom | — | Custom credits, seats, concurrency, security, and support |
free tierFree plan: 10,000 credits/month (about 10 standard TTS minutes), 15 included Agents minutes, 4 Agents concurrency, and 0 credit rollover.
billingmonthly + annual; annual plans charge 10 months for 12; usage top-ups and pay-as-you-go are separate
hidden costsCredit burn varies sharply: STT is 330 credits/minute, music 900/minute, SFX 200/generation, voice changer/isolator 1,000/minute, and dubbing 2,000-10,000/minute. Paid rollover is capped at two extra months and is forfeited on downgrade/cancel. PAYG top-ups start at $5, can auto-top-up, are nonrefundable, and expire after 12 months. Agents overages are $0.08/minute, burst concurrency $0.16/minute, plus telephony/LLM charges.
verified 2026-08-14 · source ↗
Vibecode ElevenLabs
Not really. ElevenLabs's value is not the code: . See the honest breakdown above.
How much does ElevenLabs cost?
ElevenLabs costs about $22/month (Creator, checked 2026-07-30), which is $264 per year.
What do I lose by replacing ElevenLabs?
Honestly: voice quality; multilingual dubbing; voice design; safety controls; rights/licensing; model updates. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to ElevenLabs?
Yes: Voicebox (A local voice studio with cloning, seven engines, transcription and a multi-track editor; dubbing still takes manual assembly.) pyVideoTrans (Drop in a video and it does the dull chain, transcribe, translate, dub, remux, without billing by the minute.) TTS WebUI (A 10GB local voice lab with an installer and every model drawer open; model licences are your homework.) All 3 curated free alternatives are at vibecodeit.com/elevenlabs/alternatives. The prompt is for when you want it exactly your way.