mrq/vall-e
5
9
Fork 0
Code Issues 8 Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
mrq 2024-10-23 00:03:35 -05:00
parent 8920e5e86b
commit 92e6bff6dc
3 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
vall_e/__main__.py
View File

@ -21,7 +21,7 @@ def main():
parser.add_argument("--max-ar-steps", type=int, default=12 * cfg.dataset.frames_per_second) parser.add_argument("--max-ar-steps", type=int, default=12 * cfg.dataset.frames_per_second)
parser.add_argument("--max-nar-levels", type=int, default=7) parser.add_argument("--max-nar-levels", type=int, default=7)
parser.add_argument("--ar-temp", type=float, default=0.0) parser.add_argument("--ar-temp", type=float, default=0.5)
parser.add_argument("--nar-temp", type=float, default=0.0) parser.add_argument("--nar-temp", type=float, default=0.0)
parser.add_argument("--min-ar-temp", type=float, default=-1.0) parser.add_argument("--min-ar-temp", type=float, default=-1.0)
parser.add_argument("--min-nar-temp", type=float, default=-1.0) parser.add_argument("--min-nar-temp", type=float, default=-1.0)

4
vall_e/models/ar_nar.py
View File

@ -368,7 +368,7 @@ class AR_NAR(Base):
mirostat = sampled.scores mirostat = sampled.scores
elif sampling_beam_width > 0: elif sampling_beam_width > 0:
# expand tuple # expand tuple
scores = sampled.scores s = sampled.scores
# first step, expand batch # first step, expand batch
if batch_size == 1: if batch_size == 1:
batch_size = sampling_beam_width batch_size = sampling_beam_width
@ -379,7 +379,7 @@ class AR_NAR(Base):
start_slice = start_slice * sampling_beam_width start_slice = start_slice * sampling_beam_width
stopped = torch.zeros(batch_size, device=device).bool() stopped = torch.zeros(batch_size, device=device).bool()
scores = [ scores[i] + score for i, score in enumerate(scores) ] scores = [ scores[i] + score for i, score in enumerate(s) ]
# append tokens # append tokens
for i, ri in enumerate(r): for i, ri in enumerate(r):

2
vall_e/webui.py
View File

@ -351,7 +351,7 @@ with ui:
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.") 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.")
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.") 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.")
with gr.Row(): with gr.Row():
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)") 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)")
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)") 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)")
with gr.Row(): with gr.Row():
if cfg.experimental: if cfg.experimental: