litagin02
bfb2435b37
Merge branch 'master' into dev
2025-08-24 11:55:14 +09:00
litagin02
4ea3cf12ef
update readme
2025-08-24 11:48:21 +09:00
litagin02
e5f2dafbdd
update
2025-08-24 11:46:53 +09:00
litagin02
998217c010
fmt, credit
2025-08-24 11:39:39 +09:00
litagin02
c51c80fe1a
Ver 2.7.0 maybe
2025-08-24 11:27:27 +09:00
litagin02
4859d64e9f
Change onnxsim to onnxsim-prebuilt
2025-08-24 09:40:38 +09:00
litagin02
c55bc61a45
Add python version file (for e.g. uv?)
2025-08-24 09:39:54 +09:00
litagin02
4eab125451
Sort model files by desceinding mt time
2025-08-24 09:39:27 +09:00
litagin02
f8834b4c74
Merge pull request #193 from kale4eat/fix-pyopenjtalk-worker
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まれに「サーバーに接続できませんでした」エラーが起こる問題修正
2025-08-24 09:37:36 +09:00
litagin02
1d7a7a0d48
Fix colab numpy version issue
2025-03-29 22:40:22 +09:00
litagin02
404b2811de
Merge pull request #194 from tsukumijima/master
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ONNX 推論時のメモリ消費量を大幅に削減 & 前回 PR の実装ミスの修正
2025-03-29 22:06:35 +09:00
tsukumi
d7e2745559
Improve: Update license information
2025-03-26 20:28:34 +09:00
tsukumi
1a44c9d437
Merge branch 'dev' of github.com:litagin02/Style-Bert-VITS2
2025-03-26 20:09:40 +09:00
tsukumi
2aa503233a
Improve: Add option to generate AIVM/AIVMX files directly when running convert_onnx.py, add license information
2025-03-26 20:01:17 +09:00
kale4eat
636f20fcfe
Fix startup error by enabling SO_REUSEADDR option
2024-12-24 21:36:44 +09:00
tsukumi
810ca43615
Improve: Disable enable_cpu_mem_arena for CPU inference only to prevent excessive memory consumption during the inference session of the BERT model
2024-12-22 09:17:31 +09:00
tsukumi
3c218c7087
Improve: Variation of text-to-speech during testing
2024-12-22 07:17:07 +09:00
tsukumi
405c5deddd
Improve: Adjusted the default Execution Provider options
2024-12-22 07:04:20 +09:00
tsukumi
08c439e88e
Refactor: Use I/O Binding during BERT inference and always release memory after inference
2024-12-22 06:49:43 +09:00
tsukumi
15af441cba
Improve: Convert ONNX version of BERT models to FP16
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I have found that half-precision has little effect on speech synthesis quality and, depending on the environment, can reduce file size and memory usage by half, so I have decided to use FP16.
2024-12-19 14:51:30 +09:00
tsukumi
b843804a75
Fix: TTSModel.unload() did not work in PyTorch-independent environments
2024-12-19 13:51:52 +09:00
tsukumi
e1ce12a336
Fix: Use Fast Tokenizer instead of Slow Tokenizer, which makes validation very slow
2024-12-19 13:41:09 +09:00
tsukumi
ebd249bca8
Fix: spm.model is missing
2024-12-19 13:16:25 +09:00
tsukumi
2833fb5eeb
Fix: The conversion script for English BERT to Fast Tokenizer was incorrect, so tokenization was not performed correctly
2024-12-19 11:10:33 +09:00
tsukumi
c1fce3fec7
Improve: Make it possible to convert BERT language models to FP16
2024-12-19 07:16:59 +09:00
tsukumi
e642a4cb69
Merge branch 'litagin02:master' into master
2024-12-08 03:37:27 +09:00
litagin02
3383458317
delete unrelated file
2024-11-20 18:58:15 +09:00
litagin02
9740256c87
Add anime-whisper
2024-11-17 16:47:37 +09:00
litagin02
8d476175a7
hathc run style:fmt
2024-11-17 12:44:27 +09:00
litagin02
a6bc65271c
hatch fmt
2024-11-17 12:41:05 +09:00
litagin02
a6047012dd
Merge pull request #178 from tsukumijima/master
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Fix: Missing onnx dependency
2024-11-17 12:35:17 +09:00
litagin02
065a7ffa0a
Merge pull request #177 from aka7774/master
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Add /g2p (Update server_fastapi.py)
2024-11-17 12:35:02 +09:00
tsukumi
4a92a1abad
Fix: Missing onnxsim dependency
2024-11-13 10:50:44 +09:00
tsukumi
f135a79316
Fix: Missing onnx dependency
2024-11-13 10:43:58 +09:00
litagin02
875186f0c7
Change faster-whisper as default
2024-11-11 10:25:30 +09:00
litagin02
038d07f4e7
Bump ver only
2024-11-11 10:18:55 +09:00
litagin02
2cb5b24244
Merge pull request #165 from tsukumijima/master
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ONNX への変換と ONNXRuntime による推論サポートを追加
2024-11-11 09:44:32 +09:00
Aka Diffusion
3155e2acc1
Update server_fastapi.py
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g2pの内容をjsonで取れるAPI
2024-11-10 15:27:19 +09:00
tsukumi
689217e350
Refactor: TTSModel.convert_to_16_bit_wav() to @staticmethod
2024-11-10 07:11:53 +09:00
tsukumi
20b0b0908b
Fix: Ensure memory is cleared on unload
2024-11-10 07:06:20 +09:00
tsukumi
9723f75f99
Fix: g2p process fails in PyTorch-independent environment
2024-11-08 02:56:05 +09:00
tsukumi
b0b12696f3
Add: Type Hint to null_model_params
2024-10-25 22:37:09 +09:00
tsukumi
375eb7f3a9
Refactor: run "hatch run style:fmt"
2024-10-25 22:18:57 +09:00
tsukumi
d762bf4401
Merge branch 'dev' of github.com:litagin02/Style-Bert-VITS2
2024-10-25 22:16:19 +09:00
tsukumi
d397a58874
Fix: errors during ONNX CPU inference
2024-10-06 16:18:21 +09:00
tsukumi
c4b6c169fc
Fix: Inference failure with some ExecutionProviders
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length_scale, etc. were passed as one-dimensional arrays when they should have been passed as scalar values.
2024-09-28 17:36:26 +09:00
tsukumi
7793e9da43
Remove: Unused import
2024-09-28 13:50:10 +09:00
tsukumi
d935d30e4c
Add: test code for CoreMLExecutionProvider on a trial basis
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Currently ONNXRuntime's CoreML support is not very good, and on my environment, I get errors like “coreml_execution_provider.cc:192 operator() Input (/sdp/flows.3/GatherND_2_output_0) has a dynamic shape ({-1,-1}) but the runtime shape ({0,10}) has zero elements. This is not supported by the CoreML EP.” and inference fails.
I hope this will be fixed in a future version of ONNXRuntime, and leave the test code as it is.
2024-09-23 21:07:27 +09:00
tsukumi
b67e50fd8d
Improve: DirectML inference performance
2024-09-23 20:20:46 +09:00
tsukumi
08af692835
Fix: PyTorch inference test
2024-09-23 15:20:52 +09:00
