From 5be14abc216660171cb043d409a0a0cfef61e4c9 Mon Sep 17 00:00:00 2001
From: mrq <barry.quiggles@protonmail.com>
Date: Sun, 5 Mar 2023 23:55:27 +0000
Subject: [PATCH] UI cleanup, actually fix syncing the epoch counter (i hope),
setting auto-suggest voice chunk size whatever to 0 will just split based on
the average duration length, signal when a NaN info value is detected
(there's some safeties in the training, but it will inevitably fuck the
model)
---
src/utils.py | 23 +++++++++++++++--------
src/webui.py | 47 ++++++++++++++++++++++++++++-------------------
2 files changed, 43 insertions(+), 27 deletions(-)
diff --git a/src/utils.py b/src/utils.py
index 8424c6c..41dd15f 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -233,7 +233,7 @@ def generate(
if emotion == "Custom":
if prompt and prompt.strip() != "":
cut_text = f"[{prompt},] {cut_text}"
- else:
+ elif emotion != "None":
cut_text = f"[I am really {emotion.lower()},] {cut_text}"
progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
@@ -464,14 +464,21 @@ def update_baseline_for_latents_chunks( voice ):
return 1
files = os.listdir(path)
+
+ total = 0
total_duration = 0
+
for file in files:
if file[-4:] != ".wav":
continue
+
metadata = torchaudio.info(f'{path}/{file}')
duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
total_duration += duration
+ total = total + 1
+ if args.autocalculate_voice_chunk_duration_size == 0:
+ return int(total_duration / total) if total > 0 else 1
return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1
def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
@@ -550,6 +557,8 @@ class TrainingState():
self.eta = "?"
self.eta_hhmmss = "?"
+ self.nan_detected = False
+
self.last_info_check_at = 0
self.statistics = []
self.losses = []
@@ -701,13 +710,10 @@ class TrainingState():
info_line = line.split("INFO:")[-1]
# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
if ': nan' in info_line:
- should_return = True
-
- print("! NAN DETECTED !")
- self.buffer.append("! NAN DETECTED !")
+ self.nan_detected = True
# easily rip out our stats...
- match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
+ match = re.findall(r'\b([a-z_0-9]+?)\b: *?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
if match and len(match) > 0:
for k, v in match:
self.info[k] = float(v.replace(",", ""))
@@ -862,6 +868,8 @@ class TrainingState():
self.metrics['loss'] = ", ".join(self.metrics['loss'])
message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
+ if self.nan_detected:
+ message = f"[!NaN DETECTED!] {message}"
if message:
percent = self.it / float(self.its) # self.epoch / float(self.epochs)
@@ -965,7 +973,6 @@ def stop_training():
try:
children = [p.info for p in psutil.process_iter(attrs=['pid', 'name', 'cmdline']) if './src/train.py' in p.info['cmdline']]
except Exception as e:
- print(e)
pass
training_state.process.stdout.close()
@@ -1419,7 +1426,7 @@ def setup_args():
'prune-nonfinal-outputs': True,
'use-bigvgan-vocoder': True,
'concurrency-count': 2,
- 'autocalculate-voice-chunk-duration-size': 10,
+ 'autocalculate-voice-chunk-duration-size': 0,
'output-sample-rate': 44100,
'output-volume': 1,
diff --git a/src/webui.py b/src/webui.py
index 3ca76d0..f9b06ca 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -180,9 +180,9 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
return (
gr.update(value=j, visible=j is not None),
- gr.update(visible=j is not None),
gr.update(value=latents, visible=latents is not None),
- None if j is None else j['voice']
+ None if j is None else j['voice'],
+ gr.update(visible=j is not None),
)
def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
@@ -378,15 +378,15 @@ def setup_gradio():
with gr.Tab("Generate"):
with gr.Row():
with gr.Column():
- text = gr.Textbox(lines=4, label="Prompt")
+ text = gr.Textbox(lines=4, label="Input Prompt")
with gr.Row():
with gr.Column():
delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
- emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True )
- prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
+ emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
+ prompt = gr.Textbox(lines=1, label="Custom Emotion")
voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
- mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
+ mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
with gr.Row():
refresh_voices = gr.Button(value="Refresh Voice List")
@@ -397,6 +397,11 @@ def setup_gradio():
inputs=voice,
outputs=voice_latents_chunks
)
+ voice.change(
+ fn=lambda value: gr.update(visible=value == "microphone"),
+ inputs=voice,
+ outputs=mic_audio,
+ )
with gr.Column():
candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
seed = gr.Number(value=0, precision=0, label="Seed")
@@ -406,16 +411,17 @@ def setup_gradio():
diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
- breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
- diffusion_sampler = gr.Radio(
- ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
- value="P", label="Diffusion Samplers", type="value" )
show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
reset_generation_settings_button = gr.Button(value="Reset to Default")
with gr.Column(visible=False) as col:
experimental_column = col
experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
+ breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
+ diffusion_sampler = gr.Radio(
+ ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
+ value="DDIM", label="Diffusion Samplers", type="value"
+ )
cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
@@ -460,10 +466,12 @@ def setup_gradio():
audio_in = gr.Files(type="file", label="Audio Input", file_types=["audio"])
import_voice_name = gr.Textbox(label="Voice Name")
import_voice_button = gr.Button(value="Import Voice")
- with gr.Column():
- metadata_out = gr.JSON(label="Audio Metadata", visible=False)
- copy_button = gr.Button(value="Copy Settings", visible=False)
- latents_out = gr.File(type="binary", label="Voice Latents", visible=False)
+ with gr.Column(visible=False) as col:
+ utilities_metadata_column = col
+
+ metadata_out = gr.JSON(label="Audio Metadata")
+ copy_button = gr.Button(value="Copy Settings")
+ latents_out = gr.File(type="binary", label="Voice Latents")
with gr.Tab("Training"):
with gr.Tab("Prepare Dataset"):
with gr.Row():
@@ -662,9 +670,9 @@ def setup_gradio():
inputs=audio_in,
outputs=[
metadata_out,
- copy_button,
latents_out,
- import_voice_name
+ import_voice_name,
+ utilities_metadata_column,
]
)
@@ -697,9 +705,10 @@ def setup_gradio():
outputs=voice,
)
- prompt.change(fn=lambda value: gr.update(value="Custom"),
- inputs=prompt,
- outputs=emotion
+ emotion.change(
+ fn=lambda value: gr.update(visible=value == "Custom"),
+ inputs=emotion,
+ outputs=prompt
)
mic_audio.change(fn=lambda value: gr.update(value="microphone"),
inputs=mic_audio,