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- **Jul 2024 (v2.3):** - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio. - **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.

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音频识别与处理
输入 音频→ 输出 音频
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NVIDIA NemotronLabs RE-USE is a 9.6-million-parameter **multilingual universal speech enhancement (USE)** model designed to restore speech quality across a wide range of real-world degradations while preserving speech fidelity. It has the following desirable properties: - Robustness to **diverse degradations**, including additive noise, reverberation, clipping, bandwidth limitation, codec artifacts, packet loss and low-quality mics. - Support for **multiple input sampling rates**, including 8, 16, 22.05, 24, 32, 44.1, and 48 kHz. - Strong **language-agnostic** capability, enabling effective performance across different languages. - If better laughter preservation is desired, please use: NVIDIA-NemotronLabs-RE-USE-laughter. (Note: For overlapping speech, this model is trained to preserve the main speaker while suppressing other speakers.) Comparison with other open-source USE models on the (subsets) of the URGENT 2025 non-blind test set, referenced against original anechoic clean speech.

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音频识别与处理
输入 音频→ 输出 音频
价格待核验多模态与渠道费用须分别核对
查看详情 ↗
来源 / 文档 ↗

NVIDIA NemotronLabs RE-USE-laughter is a 9.6-million-parameter **multilingual universal speech enhancement (USE)** model designed to restore speech quality across a wide range of real-world degradations while preserving speech fidelity. It has the following desirable properties: - **This model better preserves laughter while restoring the quality of the input speech.** - Robustness to **diverse degradations**, including additive noise, reverberation, clipping, bandwidth limitation, codec artifacts, packet loss and low-quality mics. - Support for **multiple input sampling rates**, including 8, 16, 22.05, 24, 32, 44.1, and 48 kHz. - Strong **language-agnostic** capability, enabling effective performance across different languages. (Note: For overlapping speech, this model is trained to preserve the main speaker while suppressing other speakers.) Comparison with other open-source USE models on the (subsets) of the URGENT 2025 non-blind test set, referenced against original anechoic clean speech.

译文排队中 · 当前显示来源原文

音频识别与处理
输入 音频→ 输出 音频
价格待核验多模态与渠道费用须分别核对
查看详情 ↗
来源 / 文档 ↗

RE-USE

NVIDIA

** For the commercially friendly model, please refer to NVIDIA-NemotronLabs-RE-USE and NVIDIA-NemotronLabs-RE-USE-laughter ** In universal speech enhancement, the goal is to restore the **quality** of diverse degraded speech while preserving **fidelity**, ensuring that all other factors remain unchanged, e.g., linguistic content, speaker identity, emotion, accent, and other paralinguistic attributes. Inspired by the **distortion–perception trade-off theory**, our proposed single model achieves a good balance between these two objectives and has the following desirable properties: 1. (For **Mamba** setup)Pre-built Docker environments can be downloaded here to simplify **Mamba** setup.

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音频识别与处理
输入 音频→ 输出 音频
价格待核验多模态与渠道费用须分别核对
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**Model Overview:** open-vakgyata is an open-source language identification model capable of detecting and classifying indian languages from speech inputs. **Supported Languages:** | Language | Code | |----------------------|-------| | English (India) | en-IN | | Hindi | hi-IN | | Odia | or-IN | | Bengali | bn-IN | | Tamil | ta-IN | | Telugu | te-IN | | Kannada | kn-IN | | Malayalam | ml-IN | | Marathi | mr-IN | | Gujarati | gu-IN | **Specification** - Supported Sampling Rate: 16000 - Recomonded Audio Format: 16kHz, 16bit PCM

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音频识别与处理
输入 音频→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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