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Applio

IAHispano

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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音频识别与处理
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MP-SENet-DNS

JacobLinCool

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: https://github.com/yxlu-0102/MP-SENet - Docs: [More Information Needed]

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音频识别与处理
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- This repo contains the inference code to use pretrained human voice gender classifier. - You could also try 🤗Huggingface online demo. First, clone the original github repository For those who need pretrained weights, please download it in here

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音频识别与处理
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for **voicemail detection**. It is trained on a dataset of call recordings to distinguish between **voicemail greetings** and **live human responses**. This model builds on **wav2vec2-xls-r-300m**, a self-supervised speech model trained on large-scale multilingual data. We fine-tuned it on the first two seconds of a call. - Automated voicemail detection in AI-powered call assistants. - Filtering voicemail responses in customer service and sales call automation.

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音频识别与处理
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TIGER-DnR

JusperLee

Mohan Xu * , Kai Li * , Guo Chen, Xiaolin Hu Tsinghua University, Beijing, China * Equal contribution 📜 ICLR 2025 | 🎶 Demo | 🤗 Dataset > TIGER is a lightweight model for speech separation which effectively extracts key acoustic features through frequency band-split, multi-scale and full-frequency-frame modeling. - **[2025-01-23]** We release the code and pre-trained model of TIGER! 🚀 - **[2025-01-23]** We release the TIGER model and the EchoSet dataset! 🚀

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音频识别与处理
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TIGER-speech

JusperLee

TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation Mohan Xu * , Kai Li * , Guo Chen, Xiaolin Hu Tsinghua University, Beijing, China * Equal contribution 📜 ICLR 2025 | 🎶 Demo | 🤗 Dataset > TIGER is a lightweight model for speech separation which effectively extracts key acoustic features through frequency band-split, multi-scale and full-frequency-frame modeling.

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This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2777 - Accuracy: 0.8455 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2

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**Abstract**: The recognition of accented speech still remains a dominant problem in Automatic Speech Recognition (ASR) systems. We approach the classification of accented English speech through the Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Network (ECAPA-TDNN) architecture which has been shown to perform well on a variety of speech tasks. Three models are proposed: one trained from scratch, another two models (one using data augmentation and a baseline model) fine-tuned from the checkpoints of speechbrain/spkrec-ecapa-voxceleb (VoxCeleb). Our results show that the model fine-tuned with data augmentation yield the best results. Most of the misclassifications were structured and expected due to accent similarities, such as the American and Canadian accents. We also explored the internal categorization of embeddings through t-SNE, a dimensionality reduction technique, and found that there was a level of clustering based on phonological similarity. For future work, we would like to explore the implementation of this accent classification system in our suggested framework to improve ASR performance by making it more inclusive to accented speech. This repository provides all the necessary tools to perform accent identification from speech recordings with SpeechBrain. The system uses a model pretrained on the CommonAccent dataset in English (16 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage The provided system can recognize the following 16 accents from short speech recordings in English (EN):

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音频识别与处理
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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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