unispeech-1350-en-353-fr-ft-1h
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unispeech-1350-en-90-it-ft-1h
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View details ↗unispeech-large-1500h-cv
MicrosoftThe large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang
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View details ↗unispeech-large-fr-ft
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View details ↗unispeech-sat-base
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View details ↗unispeech-sat-base-100h-libri-ft
MicrosoftA unispeech-sat-base model that was fine-tuned on 100h hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
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View details ↗unispeech-sat-base-plus
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View details ↗unispeech-sat-base-plus-sd
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View details ↗unispeech-sat-base-plus-sv
MicrosoftThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
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View details ↗unispeech-sat-base-sd
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View details ↗unispeech-sat-base-sv
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View details ↗unispeech-sat-large
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View details ↗unispeech-sat-large-sd
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View details ↗unispeech-sat-large-sv
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View details ↗unixcoder-base
MicrosoftUniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. - **Developed by:** Microsoft Team - **Shared by [Optional]:** Hugging Face - **Model type:** Feature Engineering - **Language(s) (NLP):** en - **License:** Apache-2.0 - **Related Models:** - **Parent Model:** RoBERTa - **Resources for more information:** - Associated Paper We implement a class to use UniXcoder and you can follow the code to build UniXcoder. You can download the class by
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View details ↗unixcoder-base-nine
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View details ↗unixcoder-base-unimodal
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View details ↗UserLM-8b
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View details ↗UserRepGen-4B
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View details ↗vermeer-B
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View details ↗vermeer-L
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View details ↗vermeer-L-AP
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View details ↗vermeer-XL
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