@ -223,7 +223,17 @@ To import a voice, click `Import Voice`. Remember to click `Refresh Voice List`
This tab will contain a collection of sub-tabs pertaining to training.
#### Configuration
#### Prepare Dataset
This section will aid in preparing the dataset for fine-tuning.
With it, you simply select a voice, then click the button, and wait for the console to tell you it's done. The results will be saved to `./training/{voice name}/`.
The web UI will leverage [openai/whisper](https://github.com/openai/whisper) to transcribe a given sample source, and split them into convenient pieces.
**!**NOTE**!**: transcription leverages FFMPEG, so please make sure you either have an FFMPEG installed visible to your PATH, or drop the binary in the `./bin/` folder.
#### Generate Configuration
This will generate the YAML necessary to feed into training. For now, you can set:
* `Batch Size`: size of batches for training, more batches = faster training, at the cost of higher VRAM. setting this to 1 will lead to problems
@ -255,6 +265,10 @@ Below are settings that override the default launch arguments. Some of these req
* `Embed Output Metadata`: enables embedding the settings and latents used to generate that audio clip inside that audio clip. Metadata is stored as a JSON string in the `lyrics` tag.
* `Slimmer Computed Latents`: falls back to the original, 12.9KiB way of storing latents (without the extra bits required for using the CVVP model).
* `Voice Fixer`: runs each generated audio clip through `voicefixer`, if available and installed.
* `Use CUDA for Voice Fixer`: allows voicefixer to use CUDA. Speeds up cleaning the output, but at the cost of more VRAM consumed. Disable if you OOM.
* `Device Override`: overrides the device name used to pass to PyTorch for hardware acceleration. You can use the accompanied `list_devices.py` script to map valid strings to GPU names. You can also pass `cpu` if you want to fallback to software mode.
* `Whisper Model`: the specific model to use for Whisper transcription, when preparing a dataset to finetune with.
* `Voice Latent Max Chunk Size`: during the voice latents calculation pass, this limits how large, in bytes, a chunk can be. Large values can run into VRAM OOM errors.
* `Sample Batch Size`: sets the batch size when generating autoregressive samples. Bigger batches result in faster compute, at the cost of increased VRAM consumption. Leave to 0 to calculate a "best" fit.
* `Concurrency Count`: how many Gradio events the queue can process at once. Leave this over 1 if you want to modify settings in the UI that updates other settings while generating audio clips.
parser.add_argument("--voice-fixer-use-cuda",action='store_true',default=default_arguments['voice-fixer-use-cuda'],help="Hints to voicefixer to use CUDA, if available.")
parser.add_argument("--force-cpu-for-conditioning-latents",default=default_arguments['force-cpu-for-conditioning-latents'],action='store_true',help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--device-override",default=default_arguments['device-override'],help="A device string to override pass through Torch")
parser.add_argument("--whisper-model",default=default_arguments['whisper-model'],help="Specifies which whisper model to use for transcription.")
parser.add_argument("--sample-batch-size",default=default_arguments['sample-batch-size'],type=int,help="Sets how many batches to use during the autoregressive samples pass")
parser.add_argument("--concurrency-count",type=int,default=default_arguments['concurrency-count'],help="How many Gradio events to process at once")
parser.add_argument("--output-sample-rate",type=int,default=default_arguments['output-sample-rate'],help="Sample rate to resample the output to (from 24KHz)")
