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Added option: listen path

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mrq 2023-02-09 20:42:38 +00:00
parent 3f8302a680
commit 729be135ef
7 changed files with 132 additions and 62 deletions
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134
app.py
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@ -14,11 +14,12 @@ import gradio.utils
from datetime import datetime
from fastapi import FastAPI
from tortoise.api import TextToSpeech
from tortoise.utils.audio import load_audio, load_voice, load_voices
from tortoise.utils.text import split_and_recombine_text
def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, progress=gr.Progress(track_tqdm=True)):
if voice != "microphone":
voices = [voice]
@ -321,8 +322,9 @@ def check_for_updates():
def update_voices():
return gr.Dropdown.update(choices=sorted(os.listdir("./tortoise/voices")) + ["microphone"])
def export_exec_settings( share, check_for_updates, low_vram, embed_output_metadata, latents_lean_and_mean, cond_latent_max_chunk_size, sample_batch_size, concurrency_count ):
def export_exec_settings( share, listen_path, check_for_updates, low_vram, embed_output_metadata, latents_lean_and_mean, cond_latent_max_chunk_size, sample_batch_size, concurrency_count ):
args.share = share
args.listen_path = listen_path
args.low_vram = low_vram
args.check_for_updates = check_for_updates
args.cond_latent_max_chunk_size = cond_latent_max_chunk_size
@ -333,6 +335,7 @@ def export_exec_settings( share, check_for_updates, low_vram, embed_output_metad
settings = {
'share': args.share,
'listen-path': args.listen_path,
'low-vram':args.low_vram,
'check-for-updates':args.check_for_updates,
'cond-latent-max-chunk-size': args.cond_latent_max_chunk_size,
@ -345,8 +348,65 @@ def export_exec_settings( share, check_for_updates, low_vram, embed_output_metad
with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(settings, indent='\t') )
def setup_args():
default_arguments = {
'share': False,
'listen-path': None,
'listen-host': '127.0.0.1',
'listen-port': 8000,
'check-for-updates': False,
'low-vram': False,
'sample-batch-size': None,
'embed-output-metadata': True,
'latents-lean-and-mean': True,
'cond-latent-max-chunk-size': 1000000,
'concurrency-count': 3,
}
if os.path.isfile('./config/exec.json'):
with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
overrides = json.load(f)
for k in overrides:
default_arguments[k] = overrides[k]
parser = argparse.ArgumentParser()
parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
parser.add_argument("--listen-path", default=default_arguments['listen-path'], help="Path for Gradio to listen on")
parser.add_argument("--listen-host", default=default_arguments['listen-host'], help="Host for Gradio to listen on")
parser.add_argument("--listen-port", default=default_arguments['listen-port'], type=int, help="Post for Gradio to listen on")
parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)")
parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
parser.add_argument("--cond-latent-max-chunk-size", default=default_arguments['cond-latent-max-chunk-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
args = parser.parse_args()
args.embed_output_metadata = not args.no_embed_output_metadata
return args
def setup_tortoise():
print("Initializating TorToiSe...")
tts = TextToSpeech(minor_optimizations=not args.low_vram)
print("TorToiSe initialized, ready for generation.")
return tts
def setup_gradio():
if not args.share:
def noop(function, return_value=None):
def wrapped(*args, **kwargs):
return return_value
return wrapped
gradio.utils.version_check = noop(gradio.utils.version_check)
gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
def main():
with gr.Blocks() as webui:
with gr.Tab("Generate"):
with gr.Row():
@ -442,6 +502,7 @@ def main():
with gr.Row():
with gr.Column():
with gr.Box():
exec_arg_gradio_path = gr.Textbox(label="Gradio Path", value=args.listen_path, placeholder="/")
exec_arg_share = gr.Checkbox(label="Public Share Gradio", value=args.share)
exec_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram)
@ -457,7 +518,7 @@ def main():
check_updates_now = gr.Button(value="Check for Updates")
exec_inputs = [exec_arg_share, exec_check_for_updates, exec_arg_low_vram, exec_arg_embed_output_metadata, exec_arg_latents_lean_and_mean, exec_arg_cond_latent_max_chunk_size, exec_arg_sample_batch_size, exec_arg_concurrency_count]
exec_inputs = [exec_arg_share, exec_arg_gradio_path, exec_check_for_updates, exec_arg_low_vram, exec_arg_embed_output_metadata, exec_arg_latents_lean_and_mean, exec_arg_cond_latent_max_chunk_size, exec_arg_sample_batch_size, exec_arg_concurrency_count]
for i in exec_inputs:
i.change(
@ -503,56 +564,31 @@ def main():
#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
webui.queue(concurrency_count=args.concurrency_count).launch(share=args.share)
webui.queue(concurrency_count=args.concurrency_count)
return webui
if __name__ == "__main__":
default_arguments = {
'share': False,
'check-for-updates': False,
'low-vram': False,
'sample-batch-size': None,
'embed-output-metadata': True,
'latents-lean-and-mean': True,
'cond-latent-max-chunk-size': 1000000,
'concurrency-count': 3,
}
args = setup_args()
if os.path.isfile('./config/exec.json'):
with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
overrides = json.load(f)
for k in overrides:
default_arguments[k] = overrides[k]
if args.listen_path is not None and args.listen_path != "/":
import uvicorn
uvicorn.run("app:app", host=args.listen_host, port=args.listen_port)
else:
webui = setup_gradio().launch(share=args.share, prevent_thread_lock=True)
tts = setup_tortoise()
parser = argparse.ArgumentParser()
parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)")
parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
parser.add_argument("--cond-latent-max-chunk-size", default=default_arguments['cond-latent-max-chunk-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
args = parser.parse_args()
webui.block_thread()
elif __name__ == "app":
import sys
from fastapi import FastAPI
args.embed_output_metadata = not args.no_embed_output_metadata
sys.argv = [sys.argv[0]]
if not args.share:
def noop(function, return_value=None):
def wrapped(*args, **kwargs):
return return_value
return wrapped
gradio.utils.version_check = noop(gradio.utils.version_check)
gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
app = FastAPI()
args = setup_args()
webui = setup_gradio()
app = gr.mount_gradio_app(app, webui, path=args.listen_path)
print("Initializating TorToiSe...")
tts = TextToSpeech(
minor_optimizations=not args.low_vram,
)
main()
tts = setup_tortoise()

2
start.bat
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@ -1,4 +1,4 @@
call .\tortoise-venv\Scripts\activate.bat
accelerate launch --num_cpu_threads_per_process=6 app.py
python app.py
deactivate
pause

4
tortoise/models/autoregressive.py
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@ -9,6 +9,8 @@ from transformers.utils.model_parallel_utils import get_device_map, assert_devic
from tortoise.models.arch_util import AttentionBlock
from tortoise.utils.typical_sampling import TypicalLogitsWarper
from tortoise.utils.device import get_device_count
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
@ -49,7 +51,7 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
def parallelize(self, device_map=None):
self.device_map = (
get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
get_device_map(len(self.transformer.h), range(get_device_count()))
if device_map is None
else device_map
)

32
tortoise/utils/device.py
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@ -1,9 +1,14 @@
import torch
import psutil
import importlib
def has_dml():
import importlib
loader = importlib.find_loader('torch_directml')
return loader is not None
if loader is None:
return False
import torch_directml
return torch_directml.is_available()
def get_device_name():
name = 'cpu'
@ -31,8 +36,18 @@ def get_device(verbose=False):
return torch.device(name)
def get_device_batch_size():
if torch.cuda.is_available():
available = 1
name = get_device_name()
if name == "dml":
# there's nothing publically accessible in the DML API that exposes this
# there's a method to get currently used RAM statistics... as tiles
available = 1
elif name == "cuda":
_, available = torch.cuda.mem_get_info()
elif name == "cpu":
available = psutil.virtual_memory()[4]
availableGb = available / (1024 ** 3)
if availableGb > 14:
return 16
@ -42,6 +57,17 @@ def get_device_batch_size():
return 4
return 1
def get_device_count():
name = get_device_name()
if name == "cuda":
return torch.cuda.device_count()
if name == "dml":
import torch_directml
return torch_directml.device_count()
return 1
if has_dml():
_cumsum = torch.cumsum
_repeat_interleave = torch.repeat_interleave

3
update-force.bat Executable file
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@ -0,0 +1,3 @@
git fetch --all
git reset --hard origin/main
call .\update.bat

3
update-force.sh Executable file
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@ -0,0 +1,3 @@
git fetch --all
git reset --hard origin/main
./update.sh