Bird-MAE-Huge
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探索 AI 模型 343
Wav2Vec2-Base-BirdSet-XCL
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查看详情 ↗clear
desert-ant-labs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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查看详情 ↗ear
desert-ant-labs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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查看详情 ↗uhm
desert-ant-labsFind every "uh", "um" and "hmm" in an audio file, down to 20ms. Uhm runs on device, does not require ASR, and is trained on English with acoustic transfer to Spanish, French, German, and Dutch. `uhm.mlmodelc/` is a compiled Core ML model directory. The Swift SDK downloads it with the Hugging Face Hub snapshot API, so only changed files are re-fetched on model updates. `uhm.tflite` is what the Kotlin and JavaScript SDKs download; it is converted from `uhm.onnx` and matches it at 99.7 to 100% per-frame argmax. The shipped model is a DistilHuBERT fine-tune, the smaller and more precise Uhm runtime model; the older HuBERT-base tier is no longer published.
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查看详情 ↗music_genres_classification
dima806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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查看详情 ↗This model is a fine-tuned version of facebook/wav2vec2-base-960h for **Speech Emotion Recognition (SER)**. It has been trained using a **Frozen Feature Extractor** strategy to preserve the model's acoustic understanding while adapting to emotion detection. This approach ensures stable performance and prevents "Catastrophic Forgetting," achieving nearly **80% accuracy** on the validation set. **Update:** The "Calm" and "Neutral" classes have been merged to improve classification consistency, resulting in **7 distinct emotion classes**.
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查看详情 ↗smad
duclvQ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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earlab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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查看详情 ↗The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task. The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP
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espnet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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audiobox-aesthetics
facebookThis model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: https://github.com/facebookresearch/audiobox-aesthetics - Paper: https://huggingface.co/papers/2502.05139 --- README below copied from https://github.com/facebookresearch/audiobox-aesthetics * Paper arXiv / MetaAI. * Blogpost ai.meta.com
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查看详情 ↗mms-lid-1024
facebookThis checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 1024 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 1024 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 1024 languages.
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查看详情 ↗mms-lid-126
facebookThis checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 126 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 126 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 126 languages. **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available on PyPI make sure to install `transformers` from source:
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查看详情 ↗mms-lid-2048
facebookThis checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 2048 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 2048 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 2048 languages. **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available on PyPI make sure to install `transformers` from source:
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查看详情 ↗mms-lid-256
facebookThis checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 256 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 256 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 256 languages.
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查看详情 ↗mms-lid-4017
facebookThis checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 4017 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 4017 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 4017 languages.
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查看详情 ↗mms-lid-512
facebook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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查看详情 ↗textless_sm_cs_en
facebook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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查看详情 ↗textless_sm_cs_es
facebook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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查看详情 ↗textless_sm_cs_fr
facebook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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查看详情 ↗textless_sm_de_en
facebook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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
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。