What are Open Source Noise Suppression Models?
Open source noise suppression models are specialized AI systems designed to reduce unwanted background noise and improve audio quality in speech and audio processing applications. Using advanced deep learning architectures and signal processing techniques, these models can effectively filter out noise while preserving speech clarity and naturalness. They enable developers and creators to build cleaner, more professional audio experiences with unprecedented accessibility. These models foster collaboration, accelerate innovation, and democratize access to powerful audio processing tools, enabling a wide range of applications from voice assistants to professional audio production.
Fish Speech V1.5
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model that employs an innovative DualAR architecture with dual autoregressive transformer design. It supports multiple languages with over 300,000 hours of training data for English and Chinese, and over 100,000 hours for Japanese. The model achieved exceptional performance with an ELO score of 1339 in TTS Arena evaluations, and demonstrates superior audio clarity with low error rates: 3.5% WER and 1.2% CER for English, and 1.3% CER for Chinese characters.
Fish Speech V1.5: Leading TTS with Superior Audio Quality
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model that employs an innovative DualAR architecture with dual autoregressive transformer design. It supports multiple languages with over 300,000 hours of training data for English and Chinese, and over 100,000 hours for Japanese. In independent evaluations by TTS Arena, the model performed exceptionally well, with an ELO score of 1339. The model achieved a word error rate (WER) of 3.5% and a character error rate (CER) of 1.2% for English, and a CER of 1.3% for Chinese characters, demonstrating exceptional audio clarity and noise-free synthesis.
Pros
- Innovative DualAR architecture for superior audio quality.
- Multilingual support with extensive training data.
- Top-ranked performance with 1339 ELO score.
Cons
- Higher pricing compared to other TTS models.
- May require technical expertise for optimal deployment.
Why We Love It
- It delivers exceptional audio clarity with minimal artifacts, making it ideal for professional applications requiring clean, noise-free speech synthesis.
CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on a large language model with unified streaming/non-streaming framework design. It achieves ultra-low latency of 150ms while maintaining high synthesis quality. Compared to version 1.0, pronunciation error rates are reduced by 30%-50%, MOS scores improved from 5.4 to 5.53, and it supports fine-grained control over emotions and dialects across multiple languages including Chinese dialects, English, Japanese, and Korean.
CosyVoice2-0.5B: Advanced Streaming with Noise Reduction
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing a unified streaming/non-streaming framework design. The model enhances audio quality through finite scalar quantization (FSQ) and develops a chunk-aware causal streaming model. In streaming mode, it achieves ultra-low latency of 150ms while maintaining synthesis quality almost identical to non-streaming mode. Compared to version 1.0, the pronunciation error rate has been reduced by 30%-50%, the MOS score has improved from 5.4 to 5.53, demonstrating significant noise suppression and audio clarity improvements.
Pros
- Ultra-low latency of 150ms in streaming mode.
- 30%-50% reduction in pronunciation errors.
- Improved MOS score from 5.4 to 5.53.
Cons
- Smaller parameter count may limit some advanced features.
- Streaming quality depends on network conditions.
Why We Love It
- It combines real-time processing with significant noise reduction improvements, making it perfect for live applications requiring clean audio output.
IndexTTS-2
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model designed for precise duration control and enhanced speech clarity. It addresses noise suppression challenges in emotional expressions by incorporating GPT latent representations and a novel three-stage training paradigm. The model achieves disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion while maintaining superior audio quality and outperforming state-of-the-art models in word error rate and speaker similarity.
IndexTTS-2: Zero-Shot TTS with Advanced Noise Control
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model designed to address duration control challenges while maintaining superior audio clarity. It incorporates GPT latent representations and utilizes a novel three-stage training paradigm to enhance speech clarity, particularly in highly emotional expressions. The model features disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion. Experimental results show that IndexTTS2 outperforms state-of-the-art zero-shot TTS models in word error rate, speaker similarity, and emotional fidelity while maintaining excellent noise suppression capabilities.
Pros
- Advanced zero-shot capabilities with precise duration control.
- Enhanced speech clarity through GPT latent representations.
- Superior performance in error rates and speaker similarity.
Cons
- More complex architecture may require additional computational resources.
- Zero-shot performance may vary with input quality.
Why We Love It
- It excels in maintaining clean audio quality across emotional expressions while providing unprecedented control over speech characteristics, ideal for professional audio applications.
AI Model Comparison
In this table, we compare 2025's leading open source models for noise suppression, each with unique strengths in audio processing. Fish Speech V1.5 offers exceptional multilingual clarity, CosyVoice2-0.5B provides real-time streaming with improved audio quality, while IndexTTS-2 excels in zero-shot generation with advanced noise control. This side-by-side view helps you choose the right tool for your specific audio processing and noise suppression goals.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | Fish Speech V1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Superior multilingual clarity |
| 2 | CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | Ultra-low latency streaming |
| 3 | IndexTTS-2 | IndexTeam | Text-to-Speech | $7.15/M UTF-8 bytes | Zero-shot with emotion control |
Frequently Asked Questions
Our top three picks for 2025 are Fish Speech V1.5, CosyVoice2-0.5B, and IndexTTS-2. Each of these models stood out for their innovation in audio quality, noise reduction capabilities, and unique approaches to solving challenges in clean speech synthesis and audio processing.
Our analysis shows different leaders for various needs. Fish Speech V1.5 is ideal for multilingual applications requiring maximum audio clarity. CosyVoice2-0.5B excels in real-time streaming scenarios with significant noise reduction improvements. IndexTTS-2 is perfect for applications requiring emotional speech synthesis while maintaining clean audio output.