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DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset. This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard. Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

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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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BSRoformer-GGUF

chenmozhijin

Official GGUF model repository for the BSRoformer.cpp project. This repository contains BS Roformer/Mel-Band-Roformer models converted to the GGUF format, enabling high-performance, cross-platform local inference using the MelBandRoformer.cpp inference engine. This repository contains GGUF quantized versions of the following original models: | Original Model | Original Author | Recommended (Q8_0) | All Versions | | :--- | :--- | :--- | :--- | | mel-band-roformer-deux | becruily | Download | View Models | | BS-RoFormer | anvuew | Download | View Models | | MelBandRoformers (voc_fv6) | GaboxR67 | Download | View Models | To meet different hardware requirements, various quantized versions are provided: - **q8_0** (Recommended): 8-bit quantization. Significantly reduces memory usage and bandwidth requirements while maintaining audio quality almost identical to FP32. - **fp16**: 16-bit floating point. Suitable for scenarios requiring maximum precision. - **q4_0 / q4_1 / q5_0 / q5_1**: Lower-bit quantization, suitable for devices with low VRAM/RAM. *(Note: Norm and Bias weights remain in FP32 to ensure numerical stability)* Please use with the BSRoformer.cpp command-line tool. Download the executable for your system from BSRoformer.cpp (or compile it yourself). Download the desired `.gguf` file (e.g., `voc_fv6-Q8_0.gguf`) using the direct links in the **Model List** above, or browse all models from the `Files and versions` page of this repository. ```bash ./bs_roformer-cli model_q8_0.gguf input.wav output.wav ./bs_roformer-cli model_q8_0.gguf input.wav output.wav --overlap 2

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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 ↗

GGUF / ggml conversions of `Qwen/Qwen3-TTS-Tokenizer-12Hz` for use with the `qwen3-tts` backend in **CrispStrobe/CrispASR**. Qwen3-TTS-Tokenizer-12Hz is the separate **speech tokenizer / codec** used by the Qwen3-TTS family: - 10 supported languages: `zh en ja ko de fr ru pt es it` - 12.5 Hz, 16-codebook speech representation - used for reference-audio encoding, voice-pack baking, and final waveform decode - Apache-2.0 licence

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 ↗
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。

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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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