Wide GPU Selection
DatabaseMart provides a diverse range of NVIDIA GPUs, including models like RTX 3060 Ti, RTX 4090, A100, and V100, catering to various performance needs for Whisper's different model sizes.
Text-to-Speech (TTS) Hosting, Hosted AI Voice Generator
Discover the power of our Text-to-Speech hosting service. Create lifelike audio with our AI voice generator and enhance your projects effortlessly.
Choose Your AI Voice Generator Hosting Plans
Database Mart offers best budget GPU servers for text to speech online. Cost-effective hosted text reader is ideal for hosting your own TTS service.
Express GPU Dedicated Server - P1000
- 32GB RAM
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro P1000
- Microarchitecture: Pascal
- CUDA Cores: 640
- GPU Memory: 4GB GDDR5
- FP32 Performance: 1.894 TFLOPS
Basic GPU Dedicated Server - T1000
- 64GB RAM
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro T1000
- Microarchitecture: Turing
- CUDA Cores: 896
- GPU Memory: 8GB GDDR6
- FP32 Performance: 2.5 TFLOPS
Basic GPU Dedicated Server - GTX 1650
- 64GB RAM
- Eight-Core Xeon E5-2667v3
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GTX 1650
- Microarchitecture: Turing
- CUDA Cores: 896
- GPU Memory: 4GB GDDR5
- FP32 Performance: 3.0 TFLOPS
Flash sale to June 16
Basic GPU Dedicated Server - GTX 1660
- 64GB RAM
- Dual 10-Core Xeon E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GTX 1660
- Microarchitecture: Turing
- CUDA Cores: 1408
- GPU Memory: 6GB GDDR6
- FP32 Performance: 5.0 TFLOPS
Professional GPU Dedicated Server - RTX 2060
- 128GB RAM
- Dual 10-Core E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce RTX 2060
- Microarchitecture: Ampere
- CUDA Cores: 1920
- Tensor Cores: 240
- GPU Memory: 6GB GDDR6
- FP32 Performance: 6.5 TFLOPS
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Flash sale to June 16
Basic GPU Dedicated Server - RTX 4060
- 64GB RAM
- Eight-Core E5-2690
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce RTX 4060
- Microarchitecture: Ada Lovelace
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
New Arrival
Multi-GPU Dedicated Server- 2xRTX 5090
- 256GB RAM
- Dual Gold 6148
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 5090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 20,480
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Flash sale to June 16
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Top Open Source Speech Recognition Models
Here’s a curated list of the Top Open Source Text-to-Speech (TTS) Models as of 2025, selected for their voice quality, community adoption, and ease of integration.
🏆 Top Open Source TTS Models (2025 Edition)
| Model | Key Features | Language Support | Voice Cloning | Inference Speed | License | GitHub |
|---|---|---|---|---|---|---|
| ChatTTS | High-quality, real-time TTS optimized for chatbot speech | 🇨🇳 Chinese, 🇺🇸 English | Planned | ⚡ Fast | Apache 2.0 | 🔗 |
| OpenVoice (MyShell) | Multilingual, real-time cross-lingual voice cloning | 🌍 Multilingual | ✅ Yes (few sec sample) | ⚡ Fast | MIT | 🔗 |
| XTTS v3 (Coqui) | Zero-shot cloning, Hugging Face compatible, production-ready | 🌍 Multilingual | ✅ Yes | ⚡ Fast | Apache 2.0 | 🔗 |
| Tortoise TTS | Extremely natural, expressive, few-shot cloning | 🇺🇸 English (mainly) | ✅ Yes | 🐢 Slow | Apache 2.0 | 🔗 |
| Bark (Suno) | Audio + emotion + sound FX generation | 🌍 Multilingual | ❌ No | 🚀 Medium | MIT | 🔗 |
| VITS / VITS2 | GAN + variational inference, customizable | 🌍 Multilingual | ⚠️ Limited | ⚡ Fast | MIT | 🔗 |
| ESPnet-TTS | Research-friendly toolkit with multiple TTS backends | 🌍 Multilingual | ⚠️ Optional | 🚀 Medium | Apache 2.0 | 🔗 |
| Mozilla TTS (Legacy) | Early open-source model, deprecated but stable | 🇺🇸🇪🇸🇫🇷 Multiple | ⚠️ Basic | 🚀 Medium | MPL 2.0 | 🔗 |
🥇 Best by Category
| Use Case | Recommended Model |
|---|---|
| Real-Time Chatbot Voice | ChatTTS, OpenVoice |
| Voice Cloning | Tortoise, XTTS, OpenVoice |
| Multilingual Support | OpenVoice, XTTS, Bark |
| Expressive/Creative Audio | Bark, Tortoise |
| Lightweight Deployment | VITS2, ChatTTS |
| Research/Training | ESPnet, Coqui TTS |
📌 Notes
- ChatTTS is rising rapidly due to natural tone and responsiveness for AI agents.
- OpenVoice enables impressive cross-language cloning with minimal voice data.
- XTTS v3 is easy to deploy in production, Hugging Face compatible.
- Tortoise still wins in voice realism but at the cost of speed and compute.
Why Choose our GPU Servers for TTS Hosting?
Database Mart enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
Premium Hardware
Our GPU dedicated servers and VPS are equipped with high-quality NVIDIA graphics cards, efficient Intel CPUs, pure SSD storage, and renowned memory brands such as Samsung and Hynix.
Dedicated Resources
Each server comes with dedicated GPU cards, ensuring consistent performance without resource contention.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.
Secure & Reliable
Enjoy 99.9% uptime, daily backups, and enterprise-grade security. Your data—and your art—is safe with us.
24/7/365 Free Expert Support
Our dedicated support team is comprised of experienced professionals. From initial deployment to ongoing maintenance and troubleshooting, we're here to provide the assistance you need, whenever you need it, without extra fee.
How to Install AI Voice Generator ChatTTS
Here’s a step-by-step guide to installing and running ChatTTS, the open-source AI voice generator that delivers high-quality, natural speech in English and Mandarin Chinese.
Order and login a GPU server
Clone the Repository and Create a Virtual Environment
Install Dependencies and Required Libraries
Running a Voice Generation Example
FAQs of Text to Speech Hosting
The most commonly asked questions about Whisper Speech to Text hosting service below.
What is Text-to-Speech (TTS)?
Text-to-Speech (TTS) is a type of assistive and generative AI technology that converts written text into spoken voice output using synthetic speech.
What's TTS used in?
Text-to-Speech (TTS) is widely used in virtual assistants, screen readers, customer service systems, and content creation tools like audiobooks and AI voiceovers. TTS enhances accessibility for the visually impaired and supports multitasking by reading messages, articles, or directions aloud. Emerging uses include real-time dubbing, voice cloning, and AI-powered character voices in games and the metaverse.
How can I scale a TTS service to many users?
Use GPU load balancing (multiple worker nodes), Add caching for repeated prompts, Queue requests with Redis + Celery, Deploy behind Nginx / API Gateway
Which models can run without a GPU?
Some lightweight models (e.g., VITS, ChatTTS) can run on CPU with slower performance. However, real-time use or scaling requires a GPU.
What frameworks are used for TTS hosting?
1. PyTorch (almost all TTS models), 2. ONNX (for optimization, if supported), 3. Docker (for containerized deployment), 4. NVIDIA Triton Inference Server (for scaling)
How much VRAM do I need? How about infer speed?
For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090 GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.3.
Can I do voice cloning or style transfer?
Yes, if the model supports it (e.g., Tortoise, XTTS, OpenVoice). Most require a few seconds to a minute of voice samples.
Troubleshooting - No GPU found, use CPU instead
Please make sure that the machine you are using has an NVIDIA GPU card installed and the driver is correctly installed, and the nvidia-smi command output is normal.
Then, you need to install the gpu version of torch, first execute
pip uninstall -y torch
If your cuda is 11.x, execute
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
If it is 12.x, execute
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
Then, you need to install the gpu version of torch, first execute
pip uninstall -y torch
If your cuda is 11.x, execute
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
If it is 12.x, execute
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
Troubleshooting - RuntimeError: Couldn't find appropriate backend to handle uri output1.wav and format wav.
If you use torchaudio, you need to install ffmpeg software. Download ffmpeg and add Path var on Windows, and execute on Linux
apt update
apt install ffmpeg -y
# Sample code:
torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000, format='wav')
It is recommended to use the soundfile package
pip install soundfile
# Sample code:
soundfile.write("output1.wav", wavs[0][0], 24000)
apt update
apt install ffmpeg -y
# Sample code:
torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000, format='wav')
It is recommended to use the soundfile package
pip install soundfile
# Sample code:
soundfile.write("output1.wav", wavs[0][0], 24000)