Project Beatrice
commited on
Commit
•
a9864f2
1
Parent(s):
d95893e
Add pretrained models, reduce noise files, and implement minor improvements
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- .gitignore +3 -1
- README.md +13 -2
- assets/default_config.json +3 -3
- assets/images/noimage.png +0 -0
- assets/noise/-A9bKRkhJo0.flac +0 -3
- assets/noise/-Ss70a9E8zk.flac +0 -3
- assets/noise/-Xa-AmWQ71E.flac +0 -3
- assets/noise/-__L3T1Yv_4.flac +0 -3
- assets/noise/-hdolK7F5lM.flac +0 -3
- assets/noise/-jT_4hrpYvo.flac +0 -3
- assets/noise/-n4BEVTbMP8.flac +0 -3
- assets/noise/-oUuF7nuv9M.flac +0 -3
- assets/noise/-pM0aXt3C3s.flac +0 -3
- assets/noise/-rxWFrTlJEY.flac +0 -3
- assets/noise/-u5Kjg1CBgk.flac +0 -3
- assets/noise/-wBISA-_DNM.flac +0 -3
- assets/noise/00kiylShRxU.flac +0 -3
- assets/noise/08INOGepJ9A.flac +0 -3
- assets/noise/0CMjmud6fp4.flac +0 -3
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- assets/noise/0pU2FRZFXPw.flac +0 -3
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- assets/noise/0wRkcrgpFCM.flac +0 -3
- assets/noise/0yneHvRg0dE.flac +0 -3
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- assets/noise/1AXjgucDtO0.flac +0 -3
- assets/noise/1BZWUrkr2pw.flac +0 -3
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- assets/noise/21iXfj6AkqQ.flac +0 -3
- assets/noise/22Esf3dk-M4.flac +0 -3
.gitignore
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poetry.lock
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poetry.lock
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work/*
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__pycache__
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README.md
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Poetry などを使用して、依存ライブラリをインストールしてください。
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```sh
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poetry install
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```
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正しくインストールできていれば、 `python3 beatrice_trainer -h` で以下のようなヘルプが表示されます。
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```
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-
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```
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### 3. Prepare Your Training Data
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python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir>
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```
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学習の状況は、 TensorBoard
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```sh
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tensorboard --logdir <output_dir>
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Poetry などを使用して、依存ライブラリをインストールしてください。
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```sh
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poetry install
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poetry shell
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```
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正しくインストールできていれば、 `python3 beatrice_trainer -h` で以下のようなヘルプが表示されます。
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```
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usage: beatrice_trainer [-h] [-d DATA_DIR] [-o OUT_DIR] [-r] [-c CONFIG]
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options:
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-h, --help show this help message and exit
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-d DATA_DIR, --data_dir DATA_DIR
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directory containing the training data
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-o OUT_DIR, --out_dir OUT_DIR
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output directory
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-r, --resume resume training
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-c CONFIG, --config CONFIG
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path to the config file
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```
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### 3. Prepare Your Training Data
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python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir>
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```
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学習の状況は、 TensorBoard で確認できます。
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```sh
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tensorboard --logdir <output_dir>
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assets/default_config.json
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"grad_weight_fm": 1.0,
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"grad_balancer_ema_decay": 0.995,
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"use_amp": true,
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"num_workers":
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"n_steps":
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"warmup_steps": 10000,
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"in_sample_rate": 16000,
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"out_sample_rate": 24000,
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"in_ir_wav_dir": "assets/ir",
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"in_noise_wav_dir": "assets/noise",
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"in_test_wav_dir": "assets/test",
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"pretrained_file":
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"hidden_channels": 256,
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"san": false
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}
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"grad_weight_fm": 1.0,
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"grad_balancer_ema_decay": 0.995,
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"use_amp": true,
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+
"num_workers": 16,
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"n_steps": 100000,
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"warmup_steps": 10000,
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"in_sample_rate": 16000,
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"out_sample_rate": 24000,
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"in_ir_wav_dir": "assets/ir",
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"in_noise_wav_dir": "assets/noise",
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"in_test_wav_dir": "assets/test",
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"pretrained_file": "assets/pretrained/040c_checkpoint_libritts_r_200_02300000.pt",
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"hidden_channels": 256,
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"san": false
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}
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