aivmlib-web/dist/schemas/style-bert-vits2.mjs.map

Aivis Voice Model File (.aivm/.aivmx) Utility Library for Web

{"version":3,"file":"style-bert-vits2.mjs","sources":["../../src/schemas/style-bert-vits2.ts"],"sourcesContent":["import { z } from 'zod';\n\n\n/* Style-Bert-VITS2 のハイパーパラメータの型 */\nexport type StyleBertVITS2HyperParameters = z.infer<typeof StyleBertVITS2HyperParametersSchema>;\n\n/**\n * Style-Bert-VITS2 のハイパーパラメータのスキーマ\n * 学習モデルの作成時期によって詳細なパラメータの有無が異なるため、実装上必須のパラメータ以外は optional としている\n * 以下は Style-Bert-VITS2 v2.4.1 のハイパーパラメータスキーマ定義を TypeScript 向けに改変したもの\n * ref: https://github.com/litagin02/Style-Bert-VITS2/blob/2.4.1/style_bert_vits2/models/hyper_parameters.py\n */\nexport const StyleBertVITS2HyperParametersSchema = z.object({\n    model_name: z.string(),\n    version: z.string(),\n    train: z.object({\n        log_interval: z.number().int().optional(),\n        eval_interval: z.number().int().optional(),\n        seed: z.number().int().optional(),\n        epochs: z.number().int().optional(),\n        learning_rate: z.number().optional(),\n        betas: z.tuple([z.number(), z.number()]).optional(),\n        eps: z.number().optional(),\n        batch_size: z.number().int().optional(),\n        bf16_run: z.boolean().optional(),\n        fp16_run: z.boolean().optional(),\n        lr_decay: z.number().optional(),\n        segment_size: z.number().int().optional(),\n        init_lr_ratio: z.number().int().optional(),\n        warmup_epochs: z.number().int().optional(),\n        c_mel: z.number().int().optional(),\n        c_kl: z.number().optional(),\n        c_commit: z.number().int().optional(),\n        skip_optimizer: z.boolean().optional(),\n        freeze_ZH_bert: z.boolean().optional(),\n        freeze_JP_bert: z.boolean().optional(),\n        freeze_EN_bert: z.boolean().optional(),\n        freeze_emo: z.boolean().optional(),\n        freeze_style: z.boolean().optional(),\n        freeze_decoder: z.boolean().optional(),\n    }),\n    data: z.object({\n        use_jp_extra: z.boolean().optional(),\n        training_files: z.string().optional(),\n        validation_files: z.string().optional(),\n        max_wav_value: z.number().optional(),\n        sampling_rate: z.number().int().optional(),\n        filter_length: z.number().int().optional(),\n        hop_length: z.number().int().optional(),\n        win_length: z.number().int().optional(),\n        n_mel_channels: z.number().int().optional(),\n        mel_fmin: z.number().optional(),\n        mel_fmax: z.number().nullable().optional(),\n        add_blank: z.boolean().optional(),\n        n_speakers: z.number().int(),\n        cleaned_text: z.boolean().optional(),\n        spk2id: z.record(z.string(), z.number().int()),\n        num_styles: z.number().int(),\n        style2id: z.record(z.string(), z.number().int()),\n    }),\n    model: z.object({\n        use_spk_conditioned_encoder: z.boolean().optional(),\n        use_noise_scaled_mas: z.boolean().optional(),\n        use_mel_posterior_encoder: z.boolean().optional(),\n        use_duration_discriminator: z.boolean().optional(),\n  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