wavlm猫箱下载安装-Base

Microsoft’s WavLMai分析软件

The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.百度流畅ai制作

Note百度ai智能云: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition百度aiapp, 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.

The model was pre-trained on 960h of Librispeech.即梦下载官方

Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing即梦al

Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei百度aiapp

Abstract下载官方即梦a1
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.

The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm.百度流畅ai制作

Usage

This is an English做al视频怎么赚钱 pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be
used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the SUPERB benchmark.

Note制作ai的软件: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence
of phonemes before fine-tuning.

Speech Recognition

To fine-tune the model for speech recognition, see the official speech recognition example.grok中文版下载

Speech Classification

To fine-tune the model for speech classification, see the official audio classification example.百度ai智能云

Speaker Verification

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Speaker Diarization

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Contribution

The model was contributed by cywang and patrickvonplaten.ima是什么软件

License

The official license can be found here做al视频怎么赚钱