Open source alternative to ElevenLabs, PlayHT, Resemble AI and Cartesia
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Generate natural-sounding speech from any voice sample using flow matching and Diffusion Transformer, with MIT-licensed code and zero-shot cloning.
F5-TTS provides zero-shot voice cloning via a local API (Gradio) or CLI, removing per-character costs of managed TTS services. A reference audio clip is all you need to clone a voice and generate new speech at any volume your GPU can handle.
Your app serves fewer than a few hundred minutes of audio per month. At that volume, a managed API may be cheaper and simpler than provisioning a dedicated GPU server.
The codebase exposes the full training and inference pipeline, including the Sway Sampling implementation and E2 TTS baseline. The Sway Sampling strategy is designed to transfer to other flow-matching TTS models without retraining, making this a practical starting point for method development.
You need pre-trained multilingual models beyond what the base Emilia weights cover. The CC-BY-NC restriction on the weights may also block certain institutional research settings that require a fully open license.
Self-hosting F5-TTS replaces per-character or per-minute API billing with infrastructure costs. The TensorRT-LLM runtime achieves an RTF of 0.039 on a single L20 GPU at concurrency 2, making it viable for real-time or near-real-time generation.
You need a managed API with SLAs, support contracts, and no GPU operations overhead. Managed platforms handle all infrastructure so your team does not have to.
The 100,000-hour Emilia training gives the base model strong coverage of multiple languages and accents. Code-switching (mixing two languages mid-sentence) works out of the box, which benefits content targeting mixed-language markets.
Your target language is low-resource and unlikely to appear in the Emilia dataset. Cloning quality for rare languages or dialects may be below production standard.
Generating high-quality speech that matches a specific person's voice requires access to large language models that most teams cannot train from scratch. Commercial TTS platforms like ElevenLabs and PlayHT solve this with managed APIs, but they charge per character or per minute of generated audio. For any product generating more than a few minutes of speech per day, those charges add up, and every audio sample you send is processed by a third-party server.
Beyond cost, the technical challenge of voice cloning has historically required complex pipelines with duration models, text encoders, and phoneme alignment to produce natural output. Teams building voice features either maintain that complexity themselves or accept vendor lock-in by using a managed service.
Clone any voice from a short audio reference without fine-tuning. The model pads input text to match the reference audio length and generates matching speech through a flow-matching denoising pass. No target-speaker training data or phoneme alignment required.
An inference-time flow step sampling strategy that improves performance and efficiency without retraining the model. Achieves an RTF of 0.15 on standard hardware and 0.039 with TensorRT-LLM on a single NVIDIA L20 GPU. Can be applied to other flow-matching TTS models.
Trained on 100,000 hours of the Emilia multilingual dataset. Generates speech in multiple languages and can code-switch (mix two languages) within a single output. This capability is available out of the box without custom configuration.
A browser-based interface for TTS, multi-style and multi-speaker generation, and voice chat powered by Qwen2.5-3B-Instruct. Launch with `f5-tts_infer-gradio` or through a Docker container on port 7860.
A Triton and TensorRT-LLM runtime for production deployment. Benchmarked on a single NVIDIA L20 GPU, it reaches an average latency of 253ms at concurrency 2 and an RTF of 0.039. Standard PyTorch on the same GPU runs at RTF 0.147.
Runs on NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (MPS). RDNA 3.5 and RDNA 4 GPUs (Radeon 8050S/8060S, RX 9060/9070 series) require ROCm 7.x; ROCm 6.x lacks support for these architectures.
Both tools synthesize speech from text and a reference audio sample, but their deployment models differ. ElevenLabs is a managed cloud API with no self-hosting path; F5-TTS runs on your own hardware.
| Feature | F5-TTS | ElevenLabs |
|---|---|---|
| License | MIT (code) / CC-BY-NC (weights) | Proprietary |
| Self-hosting | Yes | No |
| Pricing | Free to run (GPU infrastructure cost) | Paid plans, per-character billing |
| Zero-shot cloning | Yes | Yes |
| Multilingual | Yes (100K-hour Emilia dataset) | Yes |
| Deployment | pip / Docker / CLI | Managed REST API |
F5-TTS is the better choice when you need audio to stay on your own servers, want to eliminate per-character billing on production workloads, or need to modify the synthesis pipeline. ElevenLabs is worth considering when you need a turnkey managed API with no GPU infrastructure, or when you want its voice library and cloning studio without any model management.
PlayHT is a hosted TTS platform with a similar zero-shot cloning pitch. The key difference is infrastructure ownership versus ease of integration.
| Feature | F5-TTS | PlayHT |
|---|---|---|
| License | MIT (code) / CC-BY-NC (weights) | Proprietary |
| Self-hosting | Yes | No |
| Pricing | Free (GPU cost only) | Subscription, per-minute |
| Code-switching | Yes | Limited |
| Inference latency | RTF 0.039 (TensorRT-LLM, L20) | Managed, varies |
F5-TTS wins when you process audio at scale and want to avoid per-minute charges, or when your application involves multilingual code-switching. PlayHT is the better fit for teams that want quick API integration without model management or GPU provisioning.
Install F5-TTS as a pip package, or deploy via Docker for a Gradio web interface with GPU support.
```bash
pip install f5-tts
docker container run --rm -it --gpus=all -p 7860:7860 ghcr.io/swivid/f5-tts:main
```The code is MIT licensed and free for any use, including commercial. The bundled pre-trained model weights carry a CC-BY-NC license due to the Emilia training dataset, which restricts commercial use of those specific weights. Teams with commercial requirements can train their own weights using the MIT-licensed training code on a dataset they hold the rights to.
A GPU is required for practical inference. NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (MPS) are all supported. A single NVIDIA L20 GPU reaches an RTF of 0.039 with TensorRT-LLM and 0.147 with standard PyTorch. CPU inference works but is too slow for real-time or near-real-time use cases.
You provide a short reference audio clip and its transcription. F5-TTS pads the input text with filler tokens to match the reference audio length, then runs a flow-matching denoising pass through a Diffusion Transformer to generate speech in that voice. No fine-tuning or speaker-specific training data is needed.
Yes. The repository includes training and finetuning code using Hugging Face Accelerate, and a Gradio interface for quick-start finetuning. The README recommends creating a dedicated conda environment and matching the PyTorch version to your GPU type before starting.
The base model was trained on the Emilia multilingual dataset (100,000 hours of audio), which covers multiple languages. It supports code-switching (mixing two languages mid-sentence). Custom language support can be configured via the SHARED.md inference configuration. Quality for low-resource languages not well-represented in Emilia may be limited.
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