From 9e64dad785bc6d6e0e948784b27028d5d4746e25 Mon Sep 17 00:00:00 2001
From: mrq <barry.quiggles@protonmail.com>
Date: Tue, 21 Feb 2023 21:50:05 +0000
Subject: [PATCH] clamp batch size to sample count when generating for the
sickos that want that, added setting to remove non-final output after a
generation, something else I forgot already
---
src/utils.py | 79 ++++++++++++++++++++++++++++++++++++++++++----------
src/webui.py | 10 ++++---
2 files changed, 71 insertions(+), 18 deletions(-)
diff --git a/src/utils.py b/src/utils.py
index 970804d..eafe425 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -151,6 +151,11 @@ def generate(
'cvvp_amount': cvvp_weight,
}
+ # clamp it down for the insane users who want this
+ # it would be wiser to enforce the sample size to the batch size, but this is what the user wants
+ if num_autoregressive_samples < args.sample_batch_size:
+ settings['sample_batch_size'] = num_autoregressive_samples
+
if delimiter is None:
delimiter = "\n"
elif delimiter == "\\n":
@@ -301,30 +306,61 @@ def generate(
'time': time.time()-full_start_time,
}
+ """
# kludgy yucky codesmells
for name in audio_cache:
if 'output' not in audio_cache[name]:
continue
- output_voices.append(f'{outdir}/{voice}_{name}.wav')
- with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
- f.write(json.dumps(info, indent='\t') )
+ #output_voices.append(f'{outdir}/{voice}_{name}.wav')
+ output_voices.append(name)
+ if not args.embed_output_metadata:
+ with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
+ f.write(json.dumps(info, indent='\t') )
+ """
if args.voice_fixer:
if not voicefixer:
load_voicefixer()
- fixed_output_voices = []
- for path in progress.tqdm(output_voices, desc="Running voicefix..."):
- fixed = path.replace(".wav", "_fixed.wav")
+ fixed_cache = {}
+ for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
+ del audio_cache[name]['audio']
+ if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+ continue
+
+ path = f'{outdir}/{voice}_{name}.wav'
+ fixed = f'{outdir}/{voice}_{name}_fixed.wav'
voicefixer.restore(
input=path,
output=fixed,
cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
#mode=mode,
)
- fixed_output_voices.append(fixed)
- output_voices = fixed_output_voices
+
+ fixed_cache[f'{name}_fixed'] = {
+ 'text': audio_cache[name]['text'],
+ 'time': audio_cache[name]['time'],
+ 'output': True
+ }
+ audio_cache[name]['output'] = False
+
+ for name in fixed_cache:
+ audio_cache[name] = fixed_cache[name]
+
+ for name in audio_cache:
+ if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+ if args.prune_nonfinal_outputs:
+ audio_cache[name]['pruned'] = True
+ os.remove(f'{outdir}/{voice}_{name}.wav')
+ continue
+
+ output_voices.append(f'{outdir}/{voice}_{name}.wav')
+
+ if not args.embed_output_metadata:
+ with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
+ f.write(json.dumps(info, indent='\t') )
+
if voice and voice != "random" and conditioning_latents is not None:
with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
@@ -332,6 +368,9 @@ def generate(
if args.embed_output_metadata:
for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
+ if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
+ continue
+
info['text'] = audio_cache[name]['text']
info['time'] = audio_cache[name]['time']
@@ -490,7 +529,7 @@ def stop_training():
training_process.kill()
return "Training cancelled"
-def get_halfp_model():
+def get_halfp_model_path():
autoregressive_model_path = get_model_path('autoregressive.pth')
return autoregressive_model_path.replace(".pth", "_half.pth")
@@ -501,7 +540,7 @@ def convert_to_halfp():
for k in model:
model[k] = model[k].half()
- outfile = get_halfp_model()
+ outfile = get_halfp_model_path()
torch.save(model, outfile)
print(f'Converted model to half precision: {outfile}')
@@ -733,13 +772,21 @@ def import_voices(files, saveAs=None, progress=None):
print(f"Imported voice to {path}")
-def get_voice_list(dir=get_voice_dir()):
+def get_voice_list(dir=get_voice_dir(), append_defaults=False):
os.makedirs(dir, exist_ok=True)
- return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"]
+ res = sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
+ if append_defaults:
+ res = res + ["random", "microphone"]
+ return res
def get_autoregressive_models(dir="./models/finetunes/"):
os.makedirs(dir, exist_ok=True)
- return [get_model_path('autoregressive.pth')] + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
+ base = [get_model_path('autoregressive.pth')]
+ halfp = get_halfp_model_path()
+ if os.path.exists(halfp):
+ base.append(halfp)
+
+ return base + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
def get_dataset_list(dir="./training/"):
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 and "train.txt" in os.listdir(os.path.join(dir, d)) ])
@@ -842,6 +889,7 @@ def setup_args():
'force-cpu-for-conditioning-latents': False,
'defer-tts-load': False,
'device-override': None,
+ 'prune-nonfinal-outputs': True,
'whisper-model': "base",
'autoregressive-model': None,
'concurrency-count': 2,
@@ -867,6 +915,7 @@ def setup_args():
parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
+ 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("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
@@ -901,7 +950,7 @@ def setup_args():
return args
-def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
+def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
global args
args.listen = listen
@@ -911,6 +960,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
args.low_vram = low_vram
args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
args.defer_tts_load = defer_tts_load
+ args.prune_nonfinal_outputs = prune_nonfinal_outputs
args.device_override = device_override
args.sample_batch_size = sample_batch_size
args.embed_output_metadata = embed_output_metadata
@@ -932,6 +982,7 @@ def save_args_settings():
'models-from-local-only':args.models_from_local_only,
'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
'defer-tts-load': args.defer_tts_load,
+ 'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
'device-override': args.device_override,
'whisper-model': args.whisper_model,
'autoregressive-model': args.autoregressive_model,
diff --git a/src/webui.py b/src/webui.py
index fb45620..a6ab00c 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -238,7 +238,7 @@ def save_training_settings_proxy( epochs, batch_size, learning_rate, learning_ra
def update_voices():
return (
- gr.Dropdown.update(choices=get_voice_list()),
+ gr.Dropdown.update(choices=get_voice_list(append_defaults=True)),
gr.Dropdown.update(choices=get_voice_list()),
gr.Dropdown.update(choices=get_voice_list("./results/")),
)
@@ -277,7 +277,8 @@ def setup_gradio():
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)")
- voice = gr.Dropdown(get_voice_list(), label="Voice", type="value")
+ voice_list = get_voice_list(append_defaults=True)
+ voice = gr.Dropdown(choices=voice_list, label="Voice", type="value", value=voice_list[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" )
refresh_voices = gr.Button(value="Refresh Voice List")
voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1)
@@ -410,14 +411,15 @@ def setup_gradio():
gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda),
gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load),
+ gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs),
gr.Textbox(label="Device Override", value=args.device_override),
]
with gr.Column():
exec_inputs = exec_inputs + [
gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
- gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
- gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
+ gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
+ gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
]
autoregressive_models = get_autoregressive_models()