mrq/ai-voice-cloning
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Added option to unsqueeze sample batches after sampling

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mrq 2023-03-21 21:34:26 +00:00
parent 9657c1d4ce
commit 5a5fd9ca87
4 changed files with 8 additions and 3 deletions
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2
modules/dlas

@ -1 +1 @@
Subproject commit 7b5e0592f875772cfed27f00fe16928a503c582a Subproject commit a4afad8837404b6d99c0a7da0f4337da6e34fc61

2
modules/tortoise-tts

@ -1 +1 @@
Subproject commit af78e3978a381e5c38aa83c6be8a9f09eb6efebf Subproject commit 0bcdf81d0444218b4dedaefa5c546d42f36b8130

6
src/utils.py
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@ -2010,6 +2010,7 @@ def setup_args():
'models-from-local-only': False, 'models-from-local-only': False,
'low-vram': False, 'low-vram': False,
'sample-batch-size': None, 'sample-batch-size': None,
'unsqueeze-sample-batches': False,
'embed-output-metadata': True, 'embed-output-metadata': True,
'latents-lean-and-mean': True, 'latents-lean-and-mean': True,
'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it 'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
@ -2067,6 +2068,7 @@ def setup_args():
parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation") parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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("--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("--unsqueeze_sample_batches", default=default_arguments['unsqueeze_sample_batches'], action='store_true', help="Unsqueezes sample batches to process one by one after sampling")
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once") parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)") parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)")
parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)") parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
@ -2131,6 +2133,7 @@ def get_default_settings( hypenated=True ):
'prune-nonfinal-outputs': args.prune_nonfinal_outputs, 'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
'device-override': args.device_override, 'device-override': args.device_override,
'sample-batch-size': args.sample_batch_size, 'sample-batch-size': args.sample_batch_size,
'unsqueeze-sample-batches': args.unsqueeze_sample_batches,
'embed-output-metadata': args.embed_output_metadata, 'embed-output-metadata': args.embed_output_metadata,
'latents-lean-and-mean': args.latents_lean_and_mean, 'latents-lean-and-mean': args.latents_lean_and_mean,
'voice-fixer': args.voice_fixer, 'voice-fixer': args.voice_fixer,
@ -2178,6 +2181,7 @@ def update_args( **kwargs ):
args.prune_nonfinal_outputs = settings['prune_nonfinal_outputs'] args.prune_nonfinal_outputs = settings['prune_nonfinal_outputs']
args.device_override = settings['device_override'] args.device_override = settings['device_override']
args.sample_batch_size = settings['sample_batch_size'] args.sample_batch_size = settings['sample_batch_size']
args.unsqueeze_sample_batches = settings['unsqueeze_sample_batches']
args.embed_output_metadata = settings['embed_output_metadata'] args.embed_output_metadata = settings['embed_output_metadata']
args.latents_lean_and_mean = settings['latents_lean_and_mean'] args.latents_lean_and_mean = settings['latents_lean_and_mean']
args.voice_fixer = settings['voice_fixer'] args.voice_fixer = settings['voice_fixer']
@ -2344,7 +2348,7 @@ def load_tts( restart=False, autoregressive_model=None, diffusion_model=None, vo
tts_loading = True tts_loading = True
print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {vocoder_model})") print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {vocoder_model})")
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json) tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches)
tts_loading = False tts_loading = False
get_model_path('dvae.pth') get_model_path('dvae.pth')

1
src/webui.py
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@ -570,6 +570,7 @@ def setup_gradio():
EXEC_SETTINGS['prune_nonfinal_outputs'] = gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs) EXEC_SETTINGS['prune_nonfinal_outputs'] = gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs)
with gr.Column(): with gr.Column():
EXEC_SETTINGS['sample_batch_size'] = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size) EXEC_SETTINGS['sample_batch_size'] = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
EXEC_SETTINGS['unsqueeze_sample_batches'] = gr.Checkbox(label="Unsqueeze Sample Batches", value=args.unsqueeze_sample_batches)
EXEC_SETTINGS['concurrency_count'] = gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count) EXEC_SETTINGS['concurrency_count'] = gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count)
EXEC_SETTINGS['autocalculate_voice_chunk_duration_size'] = gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size) EXEC_SETTINGS['autocalculate_voice_chunk_duration_size'] = gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size)
EXEC_SETTINGS['output_volume'] = gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume) EXEC_SETTINGS['output_volume'] = gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume)