univnet-dev

モデル紹介

The UnivNet model is a state-of-the-art neural vocoder which synthesizes audio waveforms from full-band MEL spectrograms, introduced in "UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation" by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, Juntae Kim. UnivNet is a generative adversarial network (GAN) in which the generator is trained to convert real (or fake, during training) log MEL spectrograms to waveforms, and the discriminator is trained to classify whether input waveforms are real or fake. From the original paper abstract: > Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acoustic information as possible. However, in some models employing full-band mel-spectrograms, an over-smoothing problem occurs as part of which non-sharp spectrograms are generated. To address this problem, we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms in real time. Inspired by works in the field of voice activity detection, we added a multi-resolution spectrogram discriminator that employs multiple linear spectrogram magnitudes computed using various parameter sets. Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input. In an evaluation on a dataset containing information on hundreds of speakers, UnivNet obtained the best objective and subjective results among competing models for both seen and unseen speakers. These results, including the best subjective score for text-to-speech, demonstrate the potential for fast adaptation to new speakers without a need for training from scratch. This checkpoint was released as part of an unofficial implementation by maum-ai (on which the `transformers` implementation is also based). As far as I know, there is no official model or code release by the original authors from Kakao Enterprise.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

よくある質問

出典と追跡記録

许可证：bsd-3-clause。模型介绍与第三方评测均以原来源的具体版本和方法为准。