What is Soprano TTS?
Soprano TTS is an open-source text-to-speech model that brings together speed and quality in a remarkably compact package. With only 80 million parameters, this model manages to generate spoken audio from text at speeds that would have seemed impossible just a short time ago. The model can process text and produce 10 hours of audio in less than 20 seconds, representing a speed increase of approximately 2000 times faster than real-time playback.
What makes Soprano stand out is its efficiency. While many modern TTS systems require substantial computing resources, Soprano operates with less than 1 GB of VRAM usage. This means it can run on more modest hardware configurations, making high-quality speech synthesis accessible to a broader range of developers and researchers. The model produces audio at 32 kHz sampling rate, delivering clear and detailed sound that meets professional standards.
The architecture takes a different approach from many recent TTS models. Instead of relying on diffusion-based decoders that can be slow, Soprano uses a vocoder-based neural decoder with Vocos architecture. This design choice enables the remarkable speed while maintaining audio quality that listeners find natural and pleasant. The model also supports streaming synthesis, which means audio can begin playing almost immediately, with the first chunks arriving in under 15 milliseconds.
Overview of Soprano TTS
| Feature | Description |
|---|---|
| AI Tool | Soprano TTS |
| Category | Text-to-Speech Model |
| Model Size | 80M Parameters |
| Speed | 2000× Real-time Factor |
| Audio Quality | 32 kHz, High-Fidelity |
| Memory Usage | Under 1 GB VRAM |
| License | Apache-2.0 |
| Repository | github.com/ekwek1/soprano |
Installation
Requirements
To run Soprano TTS, you will need either a Linux or Windows operating system. A CUDA-enabled GPU is required for the current version, though CPU support is planned for future releases. The model is optimized for NVIDIA GPUs, making it well-suited for systems with modern graphics cards.
Install with Wheel
The simplest installation method uses pip to install the pre-built wheel package. After installing Soprano, you will need to install a specific version of PyTorch that supports CUDA 12.6:
pip install soprano-tts
pip uninstall -y torch
pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu126Install from Source
For developers who want to modify the code or contribute to the project, installing from source provides full access to the codebase:
git clone https://github.com/ekwek1/soprano.git
cd soprano
pip install -e .
pip uninstall -y torch
pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu126Backend Options
Soprano uses LMDeploy by default to speed up inference. If LMDeploy cannot be installed in your environment, the model can fall back to using the HuggingFace transformers backend instead. Performance will be slower with this fallback option, but it provides compatibility when LMDeploy is not available. To enable this, pass backend='transformers' when creating the TTS model.
Key Features of Soprano TTS
High-Fidelity 32 kHz Audio
Soprano synthesizes speech at a 32 kHz sampling rate, producing audio quality that listeners find hard to distinguish from higher sampling rates like 44.1 kHz or 48 kHz. This represents a notable improvement over many existing TTS models that output at 24 kHz. The higher sampling rate captures more detail in the sound, resulting in clearer and more natural-sounding speech. For applications where audio quality matters, such as content creation or accessibility tools, this difference becomes noticeable.
Vocoder-Based Neural Decoder
The model employs a vocoder architecture rather than diffusion-based decoders. Diffusion models, while popular in recent TTS systems, require many iterative steps to generate audio, which slows down the process considerably. By choosing a Vocos-based vocoder instead, Soprano achieves waveform generation that is orders of magnitude faster. This architectural decision directly contributes to the remarkable speed of 2000 times real-time, all while maintaining audio quality that remains comparable to slower methods.
Streaming Support
One of the most practical features is the ability to stream audio in real-time. The decoder has a finite receptive field, which means it only needs a small amount of context to generate each audio chunk. This allows Soprano to begin producing audio after generating just 5 tokens, resulting in latency of less than 15 milliseconds before the first audio arrives. The streamed output sounds identical to processing the entire text at once, making it perfect for applications that need immediate response, such as virtual assistants or interactive systems.
Efficient Neural Audio Codec
Soprano represents speech using a neural codec that compresses audio information to approximately 15 tokens per second at only 0.2 kilobits per second. This extreme compression makes generation very fast and keeps memory usage low. The codec manages to preserve audio quality even at this high compression ratio, demonstrating sophisticated encoding techniques. Fewer tokens mean the model can generate speech more quickly and handle longer texts without running into memory constraints.
Sentence-Level Processing
The model processes each sentence independently, which provides several benefits. This approach enables generation of text of any length, since the model does not accumulate context across the entire document. Each sentence gets fresh processing, which helps maintain stability and consistency throughout long passages. For real-time applications, this means the system can handle ongoing speech synthesis for extended periods without degradation in quality or performance.
Usage Examples
Getting Started
First, import and create the TTS model. You can configure the backend, device, cache size, and batch size:
from soprano import SopranoTTS
model = SopranoTTS(backend='auto', device='cuda', cache_size_mb=10, decoder_batch_size=1)Tip: Increase cache_size_mb and decoder_batch_size to boost inference speed at the cost of higher memory usage.
Basic Inference
Generate speech from text with a single line of code:
out = model.infer("Soprano is an extremely lightweight text to speech model.")Save Output to File
Provide a filename to save the generated audio directly:
out = model.infer("Soprano is an extremely lightweight text to speech model.", "out.wav")Custom Sampling Parameters
Adjust temperature, top_p, and repetition_penalty to control the variation in output:
out = model.infer(
"Soprano is an extremely lightweight text to speech model.",
temperature=0.3,
top_p=0.95,
repetition_penalty=1.2,
)Batched Inference
Process multiple texts together for improved efficiency:
out = model.infer_batch(["Soprano is an extremely lightweight text to speech model."] * 10)Streaming Inference
Generate audio in chunks for real-time playback:
import torch
stream = model.infer_stream("Soprano is an extremely lightweight text to speech model.", chunk_size=1)
# Audio chunks can be accessed via an iterator
chunks = []
for chunk in stream:
chunks.append(chunk) # first chunk arrives in <15 ms!
out = torch.cat(chunks)Usage Tips
- Soprano works best when each sentence is between 2 and 15 seconds long
- Convert numbers and special characters to their phonetic form for better results (1+1 becomes "one plus one")
- If results are not satisfactory, regenerate for a different output or adjust sampling settings
- Avoid improper grammar such as not using contractions or multiple spaces
Try Soprano TTS Live Demo
Experience Soprano TTS in action with this interactive demo. Enter your own text and hear the model generate speech in real-time. This demo is hosted on Hugging Face Spaces and provides a hands-on way to explore the capabilities of Soprano.
Pros and Cons
Pros
- Extremely fast inference at 2000× real-time
- Lightweight model with only 80M parameters
- High-quality 32 kHz audio output
- Real-time streaming with low latency
- Easy to deploy with under 1 GB VRAM
- Open source under Apache-2.0 license
- Simple API for quick integration
Cons
- Requires CUDA-enabled GPU (no CPU support yet)
- Single voice only (no voice cloning currently)
- English language only at this stage
- Limited training data (1000 hours)
- No style control or emotion adjustment
- May mispronounce numbers and special characters
Limitations and Future Development
Current Limitations
Soprano was created by a second-year undergraduate researcher as an initial exploration into TTS models. The model was pretrained on 1000 hours of audio data, which is approximately 100 times less than what many commercial TTS models use. This means that both stability and quality will see significant improvements as training data increases in future versions.
The current focus has been on optimizing for speed, which explains why some features are not yet available. Voice cloning, style control, and multilingual support were intentionally left out of this first version to keep the model simple and fast. Now that the core architecture has proven successful, future versions can build on this foundation to add these capabilities.
Planned Features
- Command-line interface for easier usage
- Server and API inference for web applications
- Additional language model backends for flexibility
- CPU support to run without GPU requirement
- Voice cloning to replicate specific speakers
- Multilingual support for languages beyond English