Model overview
The NeMo NanoCodec is a neural audio codec that leverages finite scalar quantization and adversarial training with large speech language models to achieve state-of-the-art audio compression across different bitrate and frame rate ranges. Model variant details: Model | Sample Rate | Frame Rate | Bit Rate | # Codebooks | Codebook Size | Embed Dim | FSQ Levels | :-----------:|:-----------:|:----------:|:----------:|:-----------:|:-------------:|:-----------:|:------------:| 1.78kbps-12.5fps| 22050 | 12.5 | 1.78kpbs | 13 | 2016 | 52 | [8, 7, 6, 6] | 0.6kbps-12.5fps | 22050 | 12.5 | 0.6kpbs | 4 | 4032 | 16 | [9, 8, 8, 7] | 1.89kbps-21.5fps | 22050 | 21.5 | 1.89kpbs | 8 | 2016 | 32 | [8, 7, 6, 6] | ⚠️ **Note on 0.6kbps-12.5fps** This variant is designed for **fine-tuning with a limited set of speakers**, as shown in our S2S Duplex paper. It is **not recommended** for general-purpose audio encoding or decoding.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
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Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。