Continue-TTS

<p align="center"> <img alt="Continue-TTS" src="https://github.com/SVECTOR-CORPORATION/Continue-TTS/blob/main/continue-tts-image-banner.jpg?raw=true" width="800"> </p>

Continue-TTS

Text-to-Speech Model Based on Continue-1-OSS

<div align="left" style="line-height: 1;"> <a href="https://spec-chat.tech" target="_blank" style="margin: 2px;"> <img alt="SVECTOR" src="https://img.shields.io/badge/💬%20Spec%20Chat-Spec%20Chat-blue?style=plastic" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/SVECTOR-CORPORATION" target="_blank" style="margin: 2px;"> <img alt="SVECTOR" src="https://img.shields.io/badge/🤗%20Hugging%20Face-SVECTOR-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/SVECTOR-CORPORATION/Continue-TTS/blob/main/LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?color=1e88e5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/SVECTOR-CORPORATION/Continue-TTS" target="_blank" style="margin: 2px;"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Continue--TTS-181717?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div>

Introduction

We are thrilled to introduce Continue-TTS, a fine-tuned text-to-speech model based on the Continue-1-OSS architecture, developed by SVECTOR. This model is specifically trained for high-quality speech synthesis and delivers exceptional voice generation capabilities.

Continue-TTS is engineered to provide:

• Natural Speech: Human-like intonation, emotion, and rhythm that rivals commercial solutions
• 8 Unique Voices: Diverse voice options with distinct personalities and characteristics
• Real-time Generation: Low-latency streaming for interactive applications (~200ms)
• Emotional Expression: Built-in support for laughter, sighs, gasps, and other natural emotions
• Open Source: Fully accessible under Apache 2.0 license for research and commercial use

This model is based on the Continue-1-OSS architecture and combines the power of large language models with neural audio codecs to generate exceptionally natural speech from text.

<audio controls src="https://ik.imagekit.io/svector/efd3e807-49a4-463b-af6d-4069acf7ff3a.wav"></audio>

The sun was setting behind the mountains, painting the sky with soft shades of orange and violet.
She stood there quietly, breathing in the moment. <sigh>
Sometimes, the smallest moments are the ones that change everything.

<audio controls src="https://ik.imagekit.io/svector/c99ff697-291a-4fb7-940a-56b523b9f286.wav?updatedAt=1762362454065"></audio>

<sigh>  
Not every journey is loud.  
Some begin quietly… inside.  
But once they begin, they never stop.  
We continue.

Model Specifications

• Base Architecture: Continue-1-OSS
• Type: Text-to-Speech (TTS) Model
• Parameters: 3 Billion
• Audio Codec: SNAC (24kHz)
• Context Length: 131,072 tokens
• Vocabulary: 156,940 tokens (including 28,672 audio tokens)
• License: Apache 2.0
• Voices: 8 (Nova, Aurora, Stellar, Atlas, Orion, Luna, Phoenix, Ember)

Requirements

To use Continue-TTS, install the required dependencies:

pip install transformers torch
pip install snac  # Audio codec
pip install vllm==0.7.3  # For fast inference (optional but recommended)

Quickstart

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "SVECTOR-CORPORATION/Continue-TTS"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Prepare text with voice
text = "Hello! I am Continue-TTS, a text-to-speech model based on Continue-1-OSS."
voice = "nova"  # Choose: nova, aurora, stellar, atlas, orion, luna, phoenix, ember

# Format prompt (TTS format)
adapted_prompt = f"{voice}: {text}"
prompt_tokens = tokenizer(adapted_prompt, return_tensors="pt")
start_token = torch.tensor([[128259]], dtype=torch.int64)
end_tokens = torch.tensor([[128009, 128260, 128261, 128257]], dtype=torch.int64)
input_ids = torch.cat([start_token, prompt_tokens.input_ids, end_tokens], dim=1)

# Generate audio tokens
outputs = model.generate(
    input_ids.to(model.device),
    max_new_tokens=1200,
    temperature=0.6,
    top_p=0.8,
    repetition_penalty=1.3,
    eos_token_id=49158,  # TTS stop token
    do_sample=True
)

# Decode tokens (audio codes can be decoded using SNAC decoder)
generated_tokens = tokenizer.decode(outputs[0], skip_special_tokens=False)

Using Continue-TTS Package (Recommended)

For easier usage with audio generation, use the Continue-TTS package:

pip install continue-speech

from continue_tts import Continue1Model
import wave

# Initialize model
model = Continue1Model(model_name="SVECTOR-CORPORATION/Continue-TTS", max_model_len=2048)

# Generate speech
text = "Welcome to Continue-TTS! This model is built on Continue-1-OSS."
audio_chunks = model.generate_speech(prompt=text, voice="nova")

# Save to file
with wave.open("output.wav", "wb") as wf:
    wf.setnchannels(1)
    wf.setsampwidth(2)
    wf.setframerate(24000)
    for chunk in audio_chunks:
        wf.writeframes(chunk)

Available Voices

Continue-TTS includes 8 professionally designed voices:

Advanced Features

Emotion Tags

Add natural emotions to your speech:

text = "This is incredible! <laugh> I can't believe how natural it sounds. <gasp>"

Supported emotions:

• <laugh> - Natural laughter
• <chuckle> - Light laugh
• <sigh> - Expressive sigh
• <gasp> - Surprised gasp
• <cough> - Cough sound
• <yawn> - Yawn
• <groan> - Groan
• <sniffle> - Sniffle

Custom Generation Parameters

Fine-tune generation quality:

audio = model.generate_speech(
    prompt="Your text here",
    voice="nova",
    temperature=0.6,        # Lower = more consistent, Higher = more varied
    top_p=0.8,             # Nucleus sampling threshold
    max_tokens=1200,       # Maximum audio length
    repetition_penalty=1.3 # Prevent token repetition
)

Use Cases

Continue-TTS excels at:

• Audiobook Narration: Natural storytelling with emotional expression
• Virtual Assistants: Conversational AI with personality
• Accessibility: Text-to-speech for visually impaired users
• Content Creation: Voiceovers for videos, podcasts, and presentations
• Gaming: Dynamic character voices and dialogue
• Education: Interactive learning materials with voice
• Customer Service: Natural-sounding automated responses

Performance

• Quality: State-of-the-art natural speech synthesis
• Latency: ~200ms for streaming generation (GPU)
• Speed: Real-time on GPU, slower on CPU
• Memory: ~7GB GPU RAM (FP16), ~14GB (FP32)
• Sample Rate: 24kHz (high quality audio)

Model Architecture

Continue-TTS is built on the Continue-1-OSS and combines:

• Base Model: Continue-1-OSS (LLaMA-based, 3.3B parameters)
• Audio Codec: SNAC multi-scale neural audio codec
• Token Structure: 7 audio tokens per frame (hierarchical encoding)
• Training: Fine-tuned on few hours of diverse speech data

The model generates audio tokens autoregressively, which are then decoded into waveforms using the SNAC neural codec.

Training

Continue-TTS was fine-tuned on the Continue-1-OSS using:

• High-quality speech datasets covering diverse accents and styles
• Multi-speaker recordings for voice diversity
• Emotional speech data for expressive synthesis
• Conversational and narrative content

Training utilized:

• Continue-1-OSS as base
• Custom tokenizer with 28,672 audio tokens
• Multi-stage training (pretraining + fine-tuning)
• Optimized for naturalness and emotion

Limitations

As with any TTS model, Continue-TTS has certain limitations:

• Pronunciation: May struggle with unusual names, technical terms, or non-English words
• Consistency: Long-form generation may have minor quality variations
• Accents: Primarily trained on specific accent patterns
• Compute: Requires GPU for real-time generation (CPU is slower)
• Language: Currently optimized for English

Ethical Considerations

SVECTOR is committed to responsible AI development. Users should:

• Transparency: Disclose when audio is AI-generated
• Consent: Do not clone voices without explicit permission
• Verification: Implement safeguards against deepfakes and misinformation
• Attribution: Credit the model when used in public projects
• Responsible Use: Avoid generating harmful, deceptive, or illegal content

License

This model is released under the Apache License 2.0. See the LICENSE file for complete details.

Acknowledgments

Continue-1-OSS builds upon advances in neural speech synthesis, large language models, and neural audio codecs. We thank the open-source community for their contributions to these foundational technologies.

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<p align="center"> <i>Developed by <a href="https://www.svector.co.in">SVECTOR</a></i> </p>