forked from mrq/tortoise-tts
Added option: listen path
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parent
3f8302a680
commit
729be135ef
134
app.py
134
app.py
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@ -14,11 +14,12 @@ import gradio.utils
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from datetime import datetime
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from fastapi import FastAPI
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from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.text import split_and_recombine_text
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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)):
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if voice != "microphone":
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voices = [voice]
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@ -321,8 +322,9 @@ def check_for_updates():
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def update_voices():
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return gr.Dropdown.update(choices=sorted(os.listdir("./tortoise/voices")) + ["microphone"])
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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 ):
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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 ):
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args.share = share
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args.listen_path = listen_path
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args.low_vram = low_vram
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args.check_for_updates = check_for_updates
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args.cond_latent_max_chunk_size = cond_latent_max_chunk_size
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@ -333,6 +335,7 @@ def export_exec_settings( share, check_for_updates, low_vram, embed_output_metad
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settings = {
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'share': args.share,
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'listen-path': args.listen_path,
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'low-vram':args.low_vram,
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'check-for-updates':args.check_for_updates,
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'cond-latent-max-chunk-size': args.cond_latent_max_chunk_size,
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@ -345,8 +348,65 @@ def export_exec_settings( share, check_for_updates, low_vram, embed_output_metad
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with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(settings, indent='\t') )
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def setup_args():
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default_arguments = {
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'share': False,
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'listen-path': None,
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'listen-host': '127.0.0.1',
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'listen-port': 8000,
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'check-for-updates': False,
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'low-vram': False,
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'sample-batch-size': None,
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'embed-output-metadata': True,
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'latents-lean-and-mean': True,
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'cond-latent-max-chunk-size': 1000000,
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'concurrency-count': 3,
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}
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if os.path.isfile('./config/exec.json'):
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with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
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overrides = json.load(f)
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for k in overrides:
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default_arguments[k] = overrides[k]
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parser = argparse.ArgumentParser()
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parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
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parser.add_argument("--listen-path", default=default_arguments['listen-path'], help="Path for Gradio to listen on")
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parser.add_argument("--listen-host", default=default_arguments['listen-host'], help="Host for Gradio to listen on")
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parser.add_argument("--listen-port", default=default_arguments['listen-port'], type=int, help="Post for Gradio to listen on")
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parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
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parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
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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)")
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parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
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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")
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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")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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args = parser.parse_args()
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args.embed_output_metadata = not args.no_embed_output_metadata
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return args
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def setup_tortoise():
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print("Initializating TorToiSe...")
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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print("TorToiSe initialized, ready for generation.")
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return tts
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def setup_gradio():
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if not args.share:
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def noop(function, return_value=None):
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def wrapped(*args, **kwargs):
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return return_value
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return wrapped
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gradio.utils.version_check = noop(gradio.utils.version_check)
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gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
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gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
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gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
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gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
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gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
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#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
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def main():
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with gr.Blocks() as webui:
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with gr.Tab("Generate"):
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with gr.Row():
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@ -442,6 +502,7 @@ def main():
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with gr.Row():
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with gr.Column():
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with gr.Box():
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exec_arg_gradio_path = gr.Textbox(label="Gradio Path", value=args.listen_path, placeholder="/")
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exec_arg_share = gr.Checkbox(label="Public Share Gradio", value=args.share)
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exec_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
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exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram)
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@ -457,7 +518,7 @@ def main():
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check_updates_now = gr.Button(value="Check for Updates")
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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]
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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]
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for i in exec_inputs:
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i.change(
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@ -503,56 +564,31 @@ def main():
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#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
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webui.queue(concurrency_count=args.concurrency_count).launch(share=args.share)
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webui.queue(concurrency_count=args.concurrency_count)
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return webui
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if __name__ == "__main__":
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default_arguments = {
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'share': False,
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'check-for-updates': False,
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'low-vram': False,
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'sample-batch-size': None,
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'embed-output-metadata': True,
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'latents-lean-and-mean': True,
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'cond-latent-max-chunk-size': 1000000,
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'concurrency-count': 3,
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}
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args = setup_args()
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if os.path.isfile('./config/exec.json'):
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with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
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overrides = json.load(f)
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for k in overrides:
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default_arguments[k] = overrides[k]
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if args.listen_path is not None and args.listen_path != "/":
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import uvicorn
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uvicorn.run("app:app", host=args.listen_host, port=args.listen_port)
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else:
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webui = setup_gradio().launch(share=args.share, prevent_thread_lock=True)
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tts = setup_tortoise()
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parser = argparse.ArgumentParser()
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parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
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parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
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parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
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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)")
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parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
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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")
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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")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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args = parser.parse_args()
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webui.block_thread()
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elif __name__ == "app":
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import sys
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from fastapi import FastAPI
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args.embed_output_metadata = not args.no_embed_output_metadata
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sys.argv = [sys.argv[0]]
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if not args.share:
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def noop(function, return_value=None):
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def wrapped(*args, **kwargs):
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return return_value
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return wrapped
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gradio.utils.version_check = noop(gradio.utils.version_check)
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gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
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gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
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gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
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gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
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gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
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gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
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app = FastAPI()
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args = setup_args()
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webui = setup_gradio()
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app = gr.mount_gradio_app(app, webui, path=args.listen_path)
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print("Initializating TorToiSe...")
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tts = TextToSpeech(
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minor_optimizations=not args.low_vram,
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)
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main()
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tts = setup_tortoise()
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@ -1,4 +1,4 @@
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call .\tortoise-venv\Scripts\activate.bat
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accelerate launch --num_cpu_threads_per_process=6 app.py
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python app.py
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deactivate
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pause
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@ -9,6 +9,8 @@ from transformers.utils.model_parallel_utils import get_device_map, assert_devic
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from tortoise.models.arch_util import AttentionBlock
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from tortoise.utils.typical_sampling import TypicalLogitsWarper
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from tortoise.utils.device import get_device_count
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def null_position_embeddings(range, dim):
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return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
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@ -49,7 +51,7 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
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def parallelize(self, device_map=None):
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self.device_map = (
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get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
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get_device_map(len(self.transformer.h), range(get_device_count()))
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if device_map is None
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else device_map
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)
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@ -1,9 +1,14 @@
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import torch
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import psutil
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import importlib
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def has_dml():
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import importlib
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loader = importlib.find_loader('torch_directml')
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return loader is not None
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if loader is None:
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return False
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import torch_directml
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return torch_directml.is_available()
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def get_device_name():
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name = 'cpu'
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@ -31,17 +36,38 @@ def get_device(verbose=False):
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return torch.device(name)
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def get_device_batch_size():
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if torch.cuda.is_available():
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available = 1
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name = get_device_name()
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if name == "dml":
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# there's nothing publically accessible in the DML API that exposes this
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# there's a method to get currently used RAM statistics... as tiles
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available = 1
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elif name == "cuda":
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_, available = torch.cuda.mem_get_info()
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availableGb = available / (1024 ** 3)
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if availableGb > 14:
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return 16
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elif availableGb > 10:
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return 8
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elif availableGb > 7:
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return 4
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elif name == "cpu":
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available = psutil.virtual_memory()[4]
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availableGb = available / (1024 ** 3)
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if availableGb > 14:
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return 16
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elif availableGb > 10:
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return 8
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elif availableGb > 7:
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return 4
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return 1
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def get_device_count():
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name = get_device_name()
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if name == "cuda":
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return torch.cuda.device_count()
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if name == "dml":
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import torch_directml
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return torch_directml.device_count()
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return 1
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if has_dml():
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_cumsum = torch.cumsum
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_repeat_interleave = torch.repeat_interleave
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3
update-force.bat
Executable file
3
update-force.bat
Executable file
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@ -0,0 +1,3 @@
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git fetch --all
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git reset --hard origin/main
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call .\update.bat
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3
update-force.sh
Executable file
3
update-force.sh
Executable file
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@ -0,0 +1,3 @@
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git fetch --all
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git reset --hard origin/main
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./update.sh
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