forked from camenduru/ai-voice-cloning
clean up, reordered, added some rather liberal loading/unloading auxiliary models, can't really focus right now to keep testing it, report any issues and I'll get around to it
This commit is contained in:
parent
c99cacec2e
commit
d17f6fafb0
626
src/utils.py
626
src/utils.py
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@ -16,6 +16,7 @@ import re
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import urllib.request
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import signal
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import gc
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import subprocess
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import tqdm
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import torch
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@ -40,91 +41,7 @@ tts = None
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webui = None
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voicefixer = None
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whisper_model = None
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def do_gc():
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gc.collect()
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def get_args():
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global args
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return args
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def setup_args():
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global args
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default_arguments = {
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'share': False,
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'listen': None,
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'check-for-updates': False,
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'models-from-local-only': 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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'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
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'voice-fixer-use-cuda': True,
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'force-cpu-for-conditioning-latents': False,
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'defer-tts-load': False,
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'device-override': None,
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'whisper-model': "base",
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'autoregressive-model': None,
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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'output-volume': 1,
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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", default=default_arguments['listen'], help="Path 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("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models")
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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("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
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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.")
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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)")
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parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
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parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
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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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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
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parser.add_argument("--os", default="unix", help="Specifies which OS, easily")
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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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set_device_name(args.device_override)
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args.listen_host = None
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args.listen_port = None
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args.listen_path = None
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if args.listen:
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try:
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match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
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args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
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args.listen_port = match[1] if match[1] != "" else None
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args.listen_path = match[2] if match[2] != "" else "/"
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except Exception as e:
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pass
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if args.listen_port is not None:
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args.listen_port = int(args.listen_port)
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return args
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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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training_process = None
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def generate(
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text,
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@ -154,10 +71,14 @@ def generate(
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global tts
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if not tts:
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# should check if it's loading or unloaded, and load it if it's unloaded
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raise Exception("TTS is uninitialized or still initializing...")
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do_gc()
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unload_whisper()
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unload_voicefixer()
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if voice != "microphone":
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voices = [voice]
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else:
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@ -244,7 +165,7 @@ def generate(
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audio_cache = {}
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resample = None
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# not a ternary in the event for some reason I want to rely on librosa's upsampling interpolator rather than torchaudio's, for some reason
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if tts.output_sample_rate != args.output_sample_rate:
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resampler = torchaudio.transforms.Resample(
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tts.output_sample_rate,
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@ -385,7 +306,10 @@ def generate(
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with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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if args.voice_fixer and voicefixer is not None:
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if args.voice_fixer:
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if not voicefixer:
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load_voicefixer()
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fixed_output_voices = []
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for path in progress.tqdm(output_voices, desc="Running voicefix..."):
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fixed = path.replace(".wav", "_fixed.wav")
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@ -434,23 +358,43 @@ def generate(
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stats,
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)
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import subprocess
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def cancel_generate():
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from tortoise.api import STOP_SIGNAL
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STOP_SIGNAL = True
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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unload_whisper()
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unload_voicefixer()
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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if voice_samples is None:
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return
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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training_process = None
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def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
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try:
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print("Unloading TTS to save VRAM.")
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global tts
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del tts
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tts = None
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trytorch.cuda.empty_cache()
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except Exception as e:
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pass
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global training_process
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torch.multiprocessing.freeze_support()
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do_gc()
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# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
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torch.multiprocessing.freeze_support()
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unload_tts()
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unload_whisper()
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unload_voicefixer()
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cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path]
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print("Spawning process: ", " ".join(cmd))
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@ -510,7 +454,6 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
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return "".join(buffer[-buffer_size:])
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def stop_training():
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global training_process
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if training_process is None:
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@ -519,66 +462,51 @@ def stop_training():
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training_process = None
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return "Training cancelled"
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def setup_voicefixer(restart=False):
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global voicefixer
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if restart:
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del voicefixer
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voicefixer = None
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def prepare_dataset( files, outdir, language=None, progress=None ):
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unload_tts()
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try:
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print("Initializating voice-fixer")
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from voicefixer import VoiceFixer
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voicefixer = VoiceFixer()
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print("initialized voice-fixer")
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except Exception as e:
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print(f"Error occurred while tring to initialize voicefixer: {e}")
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global whisper_model
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if whisper_model is None:
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load_whisper_model()
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def setup_tortoise(restart=False):
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global args
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global tts
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os.makedirs(outdir, exist_ok=True)
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do_gc()
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idx = 0
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results = {}
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transcription = []
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if args.voice_fixer:
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setup_voicefixer(restart=restart)
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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print(f"Transcribing file: {file}")
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result = whisper_model.transcribe(file, language=language if language else "English")
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results[os.path.basename(file)] = result
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if restart:
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del tts
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tts = None
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print(f"Transcribed file: {file}, {len(result['segments'])} found.")
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print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})")
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try:
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
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except Exception as e:
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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load_autoregressive_model(args.autoregressive_model)
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waveform, sampling_rate = torchaudio.load(file)
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num_channels, num_frames = waveform.shape
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get_model_path('dvae.pth')
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print("TorToiSe initialized, ready for generation.")
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return tts
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for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
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start = int(segment['start'] * sampling_rate)
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end = int(segment['end'] * sampling_rate)
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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sliced_waveform = waveform[:, start:end]
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sliced_name = f"{pad(idx, 4)}.wav"
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
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do_gc()
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transcription.append(f"{sliced_name}|{segment['text'].strip()}")
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idx = idx + 1
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with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(transcription))
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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unload_whisper()
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if voice_samples is None:
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return
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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return f"Processed dataset to: {outdir}"
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def calc_iterations( epochs, lines, batch_size ):
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iterations = int(epochs * lines / float(batch_size))
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@ -679,54 +607,6 @@ def save_training_settings( iterations=None, batch_size=None, learning_rate=None
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return f"Training settings saved to: {outfile}"
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def prepare_dataset( files, outdir, language=None, progress=None ):
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global whisper_model
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if whisper_model is None:
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notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
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whisper_model = whisper.load_model(args.whisper_model)
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os.makedirs(outdir, exist_ok=True)
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idx = 0
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results = {}
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transcription = []
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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print(f"Transcribing file: {file}")
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result = whisper_model.transcribe(file, language=language if language else "English")
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results[os.path.basename(file)] = result
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print(f"Transcribed file: {file}, {len(result['segments'])} found.")
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waveform, sampling_rate = torchaudio.load(file)
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num_channels, num_frames = waveform.shape
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for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
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start = int(segment['start'] * sampling_rate)
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end = int(segment['end'] * sampling_rate)
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sliced_waveform = waveform[:, start:end]
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sliced_name = f"{pad(idx, 4)}.wav"
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torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
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transcription.append(f"{sliced_name}|{segment['text'].strip()}")
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idx = idx + 1
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with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(transcription))
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return f"Processed dataset to: {outdir}"
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def reset_generation_settings():
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps({}, indent='\t') )
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return import_generate_settings()
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def import_voices(files, saveAs=None, progress=None):
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global args
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@ -760,7 +640,10 @@ def import_voices(files, saveAs=None, progress=None):
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waveform, sampling_rate = torchaudio.load(filename)
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if args.voice_fixer and voicefixer is not None:
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if args.voice_fixer:
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if not voicefixer:
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load_voicefixer()
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# resample to best bandwidth since voicefixer will do it anyways through librosa
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if sampling_rate != 44100:
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print(f"Resampling imported voice sample: {path}")
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@ -789,35 +672,29 @@ def import_voices(files, saveAs=None, progress=None):
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print(f"Imported voice to {path}")
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def import_generate_settings(file="./config/generate.json"):
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settings, _ = read_generate_settings(file, read_latents=False)
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if settings is None:
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return None
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def get_voice_list(dir=get_voice_dir()):
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os.makedirs(dir, exist_ok=True)
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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"]
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return (
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None if 'text' not in settings else settings['text'],
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None if 'delimiter' not in settings else settings['delimiter'],
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None if 'emotion' not in settings else settings['emotion'],
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None if 'prompt' not in settings else settings['prompt'],
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None if 'voice' not in settings else settings['voice'],
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None,
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None,
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None if 'seed' not in settings else settings['seed'],
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None if 'candidates' not in settings else settings['candidates'],
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None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
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None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
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0.8 if 'temperature' not in settings else settings['temperature'],
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"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
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8 if 'breathing_room' not in settings else settings['breathing_room'],
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0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
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0.8 if 'top_p' not in settings else settings['top_p'],
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1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
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1.0 if 'length_penalty' not in settings else settings['length_penalty'],
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2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
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2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
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None if 'experimentals' not in settings else settings['experimentals'],
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)
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def get_autoregressive_models(dir="./models/finetunes/"):
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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" ])
|
||||
|
||||
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)) ])
|
||||
|
||||
def get_training_list(dir="./training/"):
|
||||
return sorted([f'./training/{d}/train.yaml' 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.yaml" in os.listdir(os.path.join(dir, d)) ])
|
||||
|
||||
def do_gc():
|
||||
gc.collect()
|
||||
try:
|
||||
trytorch.cuda.empty_cache()
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
def pad(num, zeroes):
|
||||
return str(num).zfill(zeroes+1)
|
||||
|
||||
def curl(url):
|
||||
try:
|
||||
|
|
@ -866,71 +743,102 @@ def check_for_updates():
|
|||
|
||||
return False
|
||||
|
||||
def reload_tts():
|
||||
setup_tortoise(restart=True)
|
||||
def enumerate_progress(iterable, desc=None, progress=None, verbose=None):
|
||||
if verbose and desc is not None:
|
||||
print(desc)
|
||||
|
||||
def cancel_generate():
|
||||
from tortoise.api import STOP_SIGNAL
|
||||
STOP_SIGNAL = True
|
||||
if progress is None:
|
||||
return tqdm(iterable, disable=not verbose)
|
||||
return progress.tqdm(iterable, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)
|
||||
|
||||
def get_voice_list(dir=get_voice_dir()):
|
||||
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"]
|
||||
def notify_progress(message, progress=None, verbose=True):
|
||||
if verbose:
|
||||
print(message)
|
||||
|
||||
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" ])
|
||||
if progress is None:
|
||||
return
|
||||
|
||||
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)) ])
|
||||
progress(0, desc=message)
|
||||
|
||||
def get_training_list(dir="./training/"):
|
||||
return sorted([f'./training/{d}/train.yaml' 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.yaml" in os.listdir(os.path.join(dir, d)) ])
|
||||
def get_args():
|
||||
global args
|
||||
return args
|
||||
|
||||
def update_whisper_model(name):
|
||||
global whisper_model
|
||||
if whisper_model:
|
||||
del whisper_model
|
||||
whisper_model = None
|
||||
def setup_args():
|
||||
global args
|
||||
|
||||
default_arguments = {
|
||||
'share': False,
|
||||
'listen': None,
|
||||
'check-for-updates': False,
|
||||
'models-from-local-only': False,
|
||||
'low-vram': False,
|
||||
'sample-batch-size': None,
|
||||
'embed-output-metadata': True,
|
||||
'latents-lean-and-mean': True,
|
||||
'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
|
||||
'voice-fixer-use-cuda': True,
|
||||
'force-cpu-for-conditioning-latents': False,
|
||||
'defer-tts-load': False,
|
||||
'device-override': None,
|
||||
'whisper-model': "base",
|
||||
'autoregressive-model': None,
|
||||
'concurrency-count': 2,
|
||||
'output-sample-rate': 44100,
|
||||
'output-volume': 1,
|
||||
}
|
||||
|
||||
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", default=default_arguments['listen'], help="Path 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("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models")
|
||||
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("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
|
||||
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("--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.")
|
||||
parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
|
||||
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
|
||||
parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
|
||||
parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
|
||||
|
||||
args.whisper_model = name
|
||||
parser.add_argument("--os", default="unix", help="Specifies which OS, easily")
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"Loading Whisper model: {args.whisper_model}")
|
||||
whisper_model = whisper.load_model(args.whisper_model)
|
||||
args.embed_output_metadata = not args.no_embed_output_metadata
|
||||
|
||||
def update_autoregressive_model(autoregressive_model_path):
|
||||
args.autoregressive_model = autoregressive_model_path
|
||||
save_args_settings()
|
||||
print(f'Stored autoregressive model to settings: {autoregressive_model_path}')
|
||||
if not args.device_override:
|
||||
set_device_name(args.device_override)
|
||||
|
||||
global tts
|
||||
if not tts:
|
||||
raise Exception("TTS is uninitialized or still initializing...")
|
||||
args.listen_host = None
|
||||
args.listen_port = None
|
||||
args.listen_path = None
|
||||
if args.listen:
|
||||
try:
|
||||
match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
|
||||
|
||||
print(f"Loading model: {autoregressive_model_path}")
|
||||
args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
|
||||
args.listen_port = match[1] if match[1] != "" else None
|
||||
args.listen_path = match[2] if match[2] != "" else "/"
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path):
|
||||
tts.load_autoregressive_model(autoregressive_model_path)
|
||||
# polyfill in case a user did NOT update the packages
|
||||
# this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path
|
||||
else:
|
||||
from tortoise.models.autoregressive import UnifiedVoice
|
||||
|
||||
tts.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', tts.models_dir)
|
||||
|
||||
del tts.autoregressive
|
||||
tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
|
||||
model_dim=1024,
|
||||
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
|
||||
train_solo_embeddings=False).cpu().eval()
|
||||
tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
|
||||
tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
|
||||
if tts.preloaded_tensors:
|
||||
tts.autoregressive = tts.autoregressive.to(tts.device)
|
||||
|
||||
print(f"Loaded model: {tts.autoregressive_model_path}")
|
||||
if args.listen_port is not None:
|
||||
args.listen_port = int(args.listen_port)
|
||||
|
||||
return autoregressive_model_path
|
||||
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 ):
|
||||
global args
|
||||
|
|
@ -980,6 +888,44 @@ def save_args_settings():
|
|||
with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
|
||||
f.write(json.dumps(settings, indent='\t') )
|
||||
|
||||
|
||||
|
||||
def import_generate_settings(file="./config/generate.json"):
|
||||
settings, _ = read_generate_settings(file, read_latents=False)
|
||||
|
||||
if settings is None:
|
||||
return None
|
||||
|
||||
return (
|
||||
None if 'text' not in settings else settings['text'],
|
||||
None if 'delimiter' not in settings else settings['delimiter'],
|
||||
None if 'emotion' not in settings else settings['emotion'],
|
||||
None if 'prompt' not in settings else settings['prompt'],
|
||||
None if 'voice' not in settings else settings['voice'],
|
||||
None,
|
||||
None,
|
||||
None if 'seed' not in settings else settings['seed'],
|
||||
None if 'candidates' not in settings else settings['candidates'],
|
||||
None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
|
||||
None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
|
||||
0.8 if 'temperature' not in settings else settings['temperature'],
|
||||
"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
|
||||
8 if 'breathing_room' not in settings else settings['breathing_room'],
|
||||
0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
|
||||
0.8 if 'top_p' not in settings else settings['top_p'],
|
||||
1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
|
||||
1.0 if 'length_penalty' not in settings else settings['length_penalty'],
|
||||
2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
|
||||
2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
|
||||
None if 'experimentals' not in settings else settings['experimentals'],
|
||||
)
|
||||
|
||||
|
||||
def reset_generation_settings():
|
||||
with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
|
||||
f.write(json.dumps({}, indent='\t') )
|
||||
return import_generate_settings()
|
||||
|
||||
def read_generate_settings(file, read_latents=True, read_json=True):
|
||||
j = None
|
||||
latents = None
|
||||
|
|
@ -1013,19 +959,119 @@ def read_generate_settings(file, read_latents=True, read_json=True):
|
|||
latents,
|
||||
)
|
||||
|
||||
def enumerate_progress(iterable, desc=None, progress=None, verbose=None):
|
||||
if verbose and desc is not None:
|
||||
print(desc)
|
||||
def load_tts(restart=False):
|
||||
global args
|
||||
global tts
|
||||
|
||||
if progress is None:
|
||||
return tqdm(iterable, disable=not verbose)
|
||||
return progress.tqdm(iterable, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)
|
||||
if restart:
|
||||
unload_tts()
|
||||
|
||||
def notify_progress(message, progress=None, verbose=True):
|
||||
if verbose:
|
||||
print(message)
|
||||
print(f"Loading TorToiSe... (using model: {args.autoregressive_model})")
|
||||
try:
|
||||
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
|
||||
except Exception as e:
|
||||
tts = TextToSpeech(minor_optimizations=not args.low_vram)
|
||||
load_autoregressive_model(args.autoregressive_model)
|
||||
|
||||
if progress is None:
|
||||
return
|
||||
get_model_path('dvae.pth')
|
||||
print("Loaded TorToiSe, ready for generation.")
|
||||
return tts
|
||||
|
||||
progress(0, desc=message)
|
||||
setup_tortoise = load_tts
|
||||
|
||||
def unload_tts():
|
||||
global tts
|
||||
|
||||
if tts:
|
||||
print("Unloading TTS")
|
||||
del tts
|
||||
tts = None
|
||||
do_gc()
|
||||
|
||||
def reload_tts():
|
||||
setup_tortoise(restart=True)
|
||||
|
||||
def update_autoregressive_model(autoregressive_model_path):
|
||||
args.autoregressive_model = autoregressive_model_path
|
||||
save_args_settings()
|
||||
print(f'Stored autoregressive model to settings: {autoregressive_model_path}')
|
||||
|
||||
global tts
|
||||
if not tts:
|
||||
raise Exception("TTS is uninitialized or still initializing...")
|
||||
|
||||
print(f"Loading model: {autoregressive_model_path}")
|
||||
|
||||
if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path):
|
||||
tts.load_autoregressive_model(autoregressive_model_path)
|
||||
# polyfill in case a user did NOT update the packages
|
||||
# this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path
|
||||
else:
|
||||
from tortoise.models.autoregressive import UnifiedVoice
|
||||
|
||||
tts.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', tts.models_dir)
|
||||
|
||||
del tts.autoregressive
|
||||
tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
|
||||
model_dim=1024,
|
||||
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
|
||||
train_solo_embeddings=False).cpu().eval()
|
||||
tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
|
||||
tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
|
||||
if tts.preloaded_tensors:
|
||||
tts.autoregressive = tts.autoregressive.to(tts.device)
|
||||
|
||||
print(f"Loaded model: {tts.autoregressive_model_path}")
|
||||
|
||||
do_gc()
|
||||
|
||||
return autoregressive_model_path
|
||||
|
||||
def load_voicefixer(restart=False):
|
||||
global voicefixer
|
||||
|
||||
if restart:
|
||||
unload_voicefixer()
|
||||
|
||||
try:
|
||||
print("Loading Voicefixer")
|
||||
from voicefixer import VoiceFixer
|
||||
voicefixer = VoiceFixer()
|
||||
except Exception as e:
|
||||
print(f"Error occurred while tring to initialize voicefixer: {e}")
|
||||
|
||||
def unload_voicefixer():
|
||||
global voicefixer
|
||||
|
||||
if voicefixer:
|
||||
print("Unloading Voicefixer")
|
||||
del voicefixer
|
||||
voicefixer = None
|
||||
|
||||
do_gc()
|
||||
|
||||
def load_whisper_model(name=None, progress=None):
|
||||
if not name:
|
||||
name = args.whisper_model
|
||||
else:
|
||||
args.whisper_model = name
|
||||
|
||||
notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
|
||||
whisper_model = whisper.load_model(args.whisper_model)
|
||||
|
||||
def unload_whisper():
|
||||
global whisper_model
|
||||
|
||||
if whisper_model:
|
||||
print("Unloading Whisper")
|
||||
del whisper_model
|
||||
whisper_model = None
|
||||
|
||||
do_gc()
|
||||
|
||||
def update_whisper_model(name, progress=None):
|
||||
global whisper_model
|
||||
if whisper_model:
|
||||
unload_whisper()
|
||||
|
||||
load_whisper_model(name)
|
||||
Loading…
Reference in New Issue
Block a user