ekwek
/

Soprano-80M

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-to-speech
 ---
+# Soprano: Instant, Ultra‑Realistic Text‑to‑Speech
+<!-- Embedded demo video (placeholder) -->
+<p align="center">
+</p>
+---
+## Overview
+**Soprano** is an ultra‑lightweight, open‑source text‑to‑speech (TTS) model designed for real‑time, high‑fidelity speech synthesis at unprecedented speed, all while remaining compact and easy to deploy.
+With only **80M parameters**, Soprano achieves a real‑time factor (RTF) of **~2000×**, capable of generating **10 hours of audio in under 20 seconds**. Soprano uses a **seamless streaming** technique that enables true real‑time synthesis in **<15 ms**, multiple orders of magnitude faster than existing TTS pipelines.
+This space contains the **model weights** for Soprano. The LLM uses a standard Qwen3 architecture, and the decoder is a Vocos model fine-tuned on the output hidden states of the LLM.
+Github: https://github.com/ekwek1/soprano
+Model Demo: https://huggingface.co/spaces/ekwek/Soprano-TTS
+---
+## Installation
+**Requirements**: Linux or Windows, CUDA‑enabled GPU required (CPU support coming soon).
+### One‑line install
+```bash
+pip install soprano-tts
+```
+### Install from source
+```bash
+git clone https://github.com/ekwek1/soprano.git
+cd soprano
+pip install -e .
+```
+> **Note**: Soprano uses **LMDeploy** to accelerate inference by default. If LMDeploy cannot be installed in your environment, Soprano can fall back to the HuggingFace **transformers** backend (with slower performance). To enable this, pass `backend='transformers'` when creating the TTS model.
+---
+## Usage
+```python
+from soprano import SopranoTTS
+model = SopranoTTS()
+```
+### Basic inference
+```python
+out = model.infer("Hello world!")
+```
+### Save output to a file
+```python
+out = model.infer("Hello world!", "out.wav")
+```
+### Custom sampling parameters
+```python
+out = model.infer(
+    "Hello world!",
+    temperature=0.3,
+    top_p=0.95,
+    repetition_penalty=1.2,
+)
+```
+### Batched inference
+```python
+out = model.infer_batch(["Hello world!"] * 10)
+```
+#### Save batch outputs to a directory
+```python
+out = model.infer_batch(["Hello world!"] * 10, "/dir")
+```
+### Streaming inference
+```python
+import torch
+stream = model.infer_stream("Hello world!", chunk_size=1)
+# Audio chunks can be accessed via an iterator
+chunks = []
+for chunk in stream:
+    chunks.append(chunk)
+out = torch.cat(chunks)
+```
+---
+## Key Features
+### 1. High‑fidelity 32 kHz audio
+Soprano synthesizes speech at **32 kHz**, delivering clarity that is perceptually indistinguishable from 44.1 kHz audio and significantly higher quality than the 24 kHz output used by many existing TTS models.
+### 2. Vocos‑based neural decoder
+Instead of slow diffusion decoders, Soprano uses a **Vocos‑based decoder**, enabling **orders‑of‑magnitude faster** waveform generation while maintaining comparable perceptual quality.
+### 3. Seamless real‑time streaming
+A novel streaming strategy leverages the decoder’s receptive field to generate audio with **ultra‑low latency**. The streamed output is acoustically identical to offline synthesis, enabling interactive applications with sub‑frame delays.
+### 4. State‑of‑the‑art neural audio codec
+Speech is represented using a **neural codec** that compresses audio to **~15 tokens/sec** at just **0.2 kbps**, allowing extremely fast generation and efficient memory usage without sacrificing quality.
+### 5. Sentence‑level streaming for infinite context
+Each sentence is generated independently, enabling **effectively infinite generation length** while maintaining stability and real‑time performance for long‑form generation.
+---
+## License
+This project is licensed under the **Apache-2.0** license. See `LICENSE` for details.