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Browse files- .gitattributes +1 -0
- D_convbasedv1_48k.pth +3 -0
- G_convbasedv1_48k.pth +3 -0
- README.md +85 -0
- assets/loss_d_total.png +0 -0
- assets/loss_g_fm.png +0 -0
- assets/loss_g_kl.png +3 -0
- assets/loss_g_mel.png +0 -0
- assets/loss_g_total.png +0 -0
- config.json +46 -0
.gitattributes
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D_convbasedv1_48k.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e1c8a6730363c5ce6890b55ace3117ced9a0718d49500b8a12738fb6ae250e1e
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size 285697566
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G_convbasedv1_48k.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd018b82e685e8ba8b818bbf2e10e6879b1d8637d04168aed555d53e75fbf681
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size 151115402
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README.md
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---
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license: apache-2.0
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---
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---
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language:
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- zh
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- en
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tags:
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- speech-synthesis
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- text-to-speech
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- voice-conversion
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- pytorch
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- audio
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- chinese-tts
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- multi-speaker
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- convolution
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- encoder-decoder
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- aishell
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- vctk
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license: apache-2.0
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datasets:
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- aishell
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- thchs30
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- primewords
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- vctk
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library_name: pytorch
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pipeline_tag: text-to-speech
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---
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# Convbased
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Convbased是一个高性能的中文语音合成模型,基于卷积神经网络和编码器-解码器架构设计。该模型在多个中文数据集上进行训练,支持多说话人和多方言的语音合成。
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- 更快的训练收敛速度
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- 更稳定的训练过程
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- 更好的语音质量输出
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- 支持多种中文方言(普通话、粤语、闽南语、四川话、温州话等)
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- 多说话人语音合成能力
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## 模型信息
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### 训练规模
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- **说话人数量**: 476个不同说话人
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- **训练时长**: 35天连续训练
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- **模型类型**: 编码器 + 解码器架构
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- **总训练数据**: 约467小时高质量语音数据
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### 模型架构
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- **编码器**: 基于卷积的文本编码器
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- **解码器**: 声学特征解码器
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- **判别器**: 对抗训练判别器
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- **损失函数**: 组合损失(Mel频谱损失 + KL散度损失 + 特征匹配损失)
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## 训练曲线
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模型训练过程中的各项损失函数变化:
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*判别器总损失*
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*生成器特征匹配损失*
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*KL散度损失*
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*Mel频谱损失*
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*生成器总损失*
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## 训练数据集
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本模型使用以下高质量中文语音数据集进行训练:
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| 数据集名称 | 时长(小时) | 描述 |
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|-------------------|-------------|------|
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| data_aishell | 178 | 中文普通话语音识别数据集 |
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| data_thchs30 | 30 | 清华大学中文语音数据集 |
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| primewords_md_2018| 178 | 中文语音合成数据集 |
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| VCTK | 44 | 英文多说话人数据集 |
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| 四川方言 | 4 | 四川话方言数据 |
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| 闽南语 | 3 | 闽南话方言数据 |
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| 粤语 | 3 | 粤语方言数据 |
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| 温州方言 | 7 | 温州话方言数据 |
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| 噪声 | 20 | 噪声环境语音数据 |
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*本模型致力于推进中文语音合成技术的发展,为中文TTS应用提供高质量的解决方案。*
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assets/loss_d_total.png
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assets/loss_g_fm.png
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assets/loss_g_kl.png
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Git LFS Details
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assets/loss_g_mel.png
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assets/loss_g_total.png
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config.json
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{
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"train": {
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"log_interval": 200,
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"seed": 1234,
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"learning_rate": 1e-4,
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"betas": [0.8, 0.99],
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"eps": 1e-9,
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"lr_decay": 0.999875,
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"segment_size": 17280,
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"c_mel": 45,
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"c_kl": 1.0
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},
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"data": {
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"max_wav_value": 32768.0,
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"sample_rate": 48000,
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"filter_length": 2048,
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"hop_length": 480,
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"win_length": 2048,
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"n_mel_channels": 128,
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"mel_fmin": 0.0,
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"mel_fmax": null
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},
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"model": {
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"text_enc_hidden_dim": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0,
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"resblock": "1",
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"resblock_kernel_sizes": [3, 7, 11],
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"resblock_dilation_sizes": [
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[1, 3, 5],
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[1, 3, 5],
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[1, 3, 5]
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],
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"upsample_rates": [12, 10, 2, 2],
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [24, 20, 4, 4],
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"use_spectral_norm": false,
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"gin_channels": 256,
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"spk_embed_dim": 476
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}
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}
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