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删除多余缩进
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README.md
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README.md
@ -121,9 +121,7 @@ pip install -r requirements.txt
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0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
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1. Environment Variables：
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- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
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- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
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2. Volumes Configuration，The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
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3. shm_size： The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
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4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
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@ -158,7 +156,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
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4. For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/asr/models`.
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5. For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint.
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5. For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint.
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## Dataset Format
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@ -175,7 +173,7 @@ Language dictionary:
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- 'en': English
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- 'ko': Korean
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- 'yue': Cantonese
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Example:
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```
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@ -184,61 +182,56 @@ D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
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## Finetune and inference
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### Open WebUI
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### Open WebUI
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#### Integrated Package Users
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#### Integrated Package Users
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Double-click `go-webui.bat`or use `go-webui.ps1`
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if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps1`
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Double-click `go-webui.bat`or use `go-webui.ps1`
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if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps1`
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#### Others
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#### Others
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```bash
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python webui.py <language(optional)>
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```
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```bash
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python webui.py <language(optional)>
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```
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if you want to switch to V1,then
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if you want to switch to V1,then
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```bash
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python webui.py v1 <language(optional)>
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```
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```bash
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python webui.py v1 <language(optional)>
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```
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Or maunally switch version in WebUI
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### Finetune
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### Finetune
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#### Path Auto-filling is now supported
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#### Path Auto-filling is now supported
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1.Fill in the audio path
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1. Fill in the audio path
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2. Slice the audio into small chunks
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3. Denoise(optinal)
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4. ASR
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5. Proofreading ASR transcriptions
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6. Go to the next Tab, then finetune the model
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2.Slice the audio into small chunks
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### Open Inference WebUI
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3.Denoise(optinal)
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#### Integrated Package Users
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4.ASR
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Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
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5.Proofreading ASR transcriptions
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#### Others
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6.Go to the next Tab, then finetune the model
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```bash
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python GPT_SoVITS/inference_webui.py <language(optional)>
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```
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OR
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### Open Inference WebUI
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#### Integrated Package Users
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Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
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#### Others
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```bash
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python GPT_SoVITS/inference_webui.py <language(optional)>
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```
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OR
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```bash
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python webui.py
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```
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```bash
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python webui.py
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```
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then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
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## V2 Release Notes
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## V2 Release Notes
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New Features:
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@ -248,11 +241,11 @@ New Features:
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3. Pre-trained model extended from 2k hours to 5k hours
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4. Improved synthesis quality for low-quality reference audio
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4. Improved synthesis quality for low-quality reference audio
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[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7) )
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[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
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Use v2 from v1 environment:
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Use v2 from v1 environment:
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1. `pip install -r requirements.txt` to update some packages
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@ -262,7 +255,7 @@ Use v2 from v1 environment:
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Chinese v2 additional: [G2PWModel_1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip)（Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.
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## V3 Release Notes
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## V3 Release Notes
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New Features:
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@ -270,9 +263,9 @@ New Features:
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2. GPT model is more stable, with fewer repetitions and omissions, and it is easier to generate speech with richer emotional expression.
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[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7) )
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[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
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Use v3 from v2 environment:
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Use v3 from v2 environment:
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1. `pip install -r requirements.txt` to update some packages
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@ -310,7 +303,7 @@ python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
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```
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<!-- If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
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```
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python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
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python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
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``` -->
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This is how the audio segmentation of the dataset is done using the command line
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```
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@ -319,7 +312,7 @@ python audio_slicer.py \
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--output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
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--threshold <volume_threshold> \
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--min_length <minimum_duration_of_each_subclip> \
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--min_interval <shortest_time_gap_between_adjacent_subclips>
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--min_interval <shortest_time_gap_between_adjacent_subclips>
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--hop_size <step_size_for_computing_volume_curve>
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```
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This is how dataset ASR processing is done using the command line(Only Chinese)
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