forked from mrq/ai-voice-cloning
oops
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9e64dad785
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12
src/utils.py
12
src/utils.py
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@ -153,7 +153,10 @@ def generate(
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# clamp it down for the insane users who want this
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# clamp it down for the insane users who want this
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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if num_autoregressive_samples < args.sample_batch_size:
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sample_batch_size = args.sample_batch_size
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if not sample_batch_size:
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sample_batch_size = tts.autoregressive_batch_size
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if num_autoregressive_samples < sample_batch_size:
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settings['sample_batch_size'] = num_autoregressive_samples
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settings['sample_batch_size'] = num_autoregressive_samples
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if delimiter is None:
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if delimiter is None:
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@ -493,7 +496,12 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
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progress(it / float(its), f'[{it}/{its}] {it_rate} Training... {status}')
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progress(it / float(its), f'[{it}/{its}] {it_rate} Training... {status}')
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if line.find('INFO: [epoch:') >= 0:
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# it also says Start training from epoch, so it might be better to do that
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if line.find('INFO: Resuming training from epoch:') >= 0:
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match = re.findall(r'iter: ([\d,]+)', line)
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if match and len(match) > 0:
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it = int(match[0].replace(",", ""))
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elif line.find('INFO: [epoch:') >= 0:
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# easily rip out our stats...
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# easily rip out our stats...
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match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
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match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
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if match and len(match) > 0:
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if match and len(match) > 0:
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@ -288,7 +288,7 @@ def setup_gradio():
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seed = gr.Number(value=0, precision=0, label="Seed")
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seed = gr.Number(value=0, precision=0, label="Seed")
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preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
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preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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num_autoregressive_samples = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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