asr-crdnn-rnnlm-librispeech
speechbrain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 ↗DISCOVER & COMPARE
探索 AI 模型 27
asr-transformer-transformerlm-librispeech
speechbrain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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗cnn14-esc50
speechbrain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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗emotion-recognition-wav2vec2-IEMOCAP
speechbrainThis repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on IEMOCAP test set is: This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗google_speech_command_xvector
speechbrain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。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗lang-id-voxlingua107-ecapa
speechbrainThis is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese).
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查看详情 ↗m-ctc-t-large
speechbrain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 ↗metricgan-plus-voicebank
speechbrainThis repository provides all the necessary tools to perform enhancement with SpeechBrain. For a better experience we encourage you to learn more about SpeechBrain. The model performance is: The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *enhance_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *enhance_batch* as in the example. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗mtl-mimic-voicebank
speechbrain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 ↗sepformer-dns4-16k-enhancement
speechbrain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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-libri2mix
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on Libri2Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The model performance is 20.6 dB on the test set of Libri2Mix dataset. The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-libri3mix
speechbrain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。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-wham16k-enhancement
speechbrain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。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-whamr
speechbrain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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-whamr-enhancement
speechbrain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。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-whamr16k
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset. The system expects input recordings sampled at 16kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-wsj02mix
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The model performance is 22.4 dB on the test set of WSJ0-2Mix dataset. The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
译文排队中 · 当前显示来源原文
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sepformer-wsj03mix
speechbrain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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗soundchoice-g2p
speechbrain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。
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗spkrec-ecapa-voxceleb
speechbrainThis repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on Voxceleb1-test set(Cleaned) is: This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
译文排队中 · 当前显示来源原文
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗spkrec-resnet-voxceleb
speechbrainThis repository provides all the necessary tools to perform speaker verification with a pretrained ResNet TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1 + Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on Voxceleb1-test set(Cleaned) is: This system is composed of an ResNet TDNN model. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗spkrec-xvect-voxceleb
speechbrainThis repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. The system is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The given model performance on Voxceleb1-test set (Cleaned) is: This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗tts-hifigan-libritts-22050Hz
speechbrainThis repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 22050 Hz. The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. Alternatives to this models are the following: - tts-hifigan-libritts-16kHz (same model trained on the same dataset, but for a sample rate of 16000 Hz) - tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz).
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价格待核验多模态与渠道费用须分别核对
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。