actually ar temp 0.5 with rep pen 1.125 seems to have the benefits of better outputs without it degrading some of the time but not all the time
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@ -21,7 +21,7 @@ def main():
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parser.add_argument("--max-ar-steps", type=int, default=12 * cfg.dataset.frames_per_second)
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parser.add_argument("--max-nar-levels", type=int, default=7)
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parser.add_argument("--ar-temp", type=float, default=0.0)
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parser.add_argument("--ar-temp", type=float, default=0.5)
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parser.add_argument("--nar-temp", type=float, default=0.0)
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parser.add_argument("--min-ar-temp", type=float, default=-1.0)
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parser.add_argument("--min-nar-temp", type=float, default=-1.0)
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@ -368,7 +368,7 @@ class AR_NAR(Base):
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mirostat = sampled.scores
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elif sampling_beam_width > 0:
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# expand tuple
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scores = sampled.scores
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s = sampled.scores
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# first step, expand batch
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if batch_size == 1:
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batch_size = sampling_beam_width
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@ -379,7 +379,7 @@ class AR_NAR(Base):
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start_slice = start_slice * sampling_beam_width
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stopped = torch.zeros(batch_size, device=device).bool()
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scores = [ scores[i] + score for i, score in enumerate(scores) ]
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scores = [ scores[i] + score for i, score in enumerate(s) ]
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# append tokens
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for i, ri in enumerate(r):
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@ -351,7 +351,7 @@ with ui:
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layout["inference_tts"]["inputs"]["max-nar-levels"] = gr.Slider(value=7, minimum=0, maximum=7, step=1, label="Max NAR Levels", info="Limits how many steps to perform in the NAR pass.")
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layout["inference_tts"]["inputs"]["input-prompt-length"] = gr.Slider(value=5.0, minimum=0.0, maximum=12.0, step=0.05, label="Input Prompt Repeat/Trim Length", info="Repeats and trims the input prompt down to X seconds. Set 0 to disable.")
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with gr.Row():
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layout["inference_tts"]["inputs"]["ar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy* sample)")
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layout["inference_tts"]["inputs"]["ar-temp"] = gr.Slider(value=0.5, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy* sample)")
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layout["inference_tts"]["inputs"]["nar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (NAR)", info="Modifies the randomness from the samples in the NAR. (0 to greedy sample)")
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with gr.Row():
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if cfg.experimental:
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