566 lines
28 KiB
Python
566 lines
28 KiB
Python
import os
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import sys
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import re
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import math
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import argparse
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import random
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import tempfile
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import functools
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import spaces
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import torch
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import numpy as np
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import torchaudio
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import gradio as gr
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from pathlib import Path
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from .inference import TTS, cfg
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from .train import train
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from .utils import get_devices, setup_logging, timer
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from .utils.io import json_read, json_stringify
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from .emb.qnt import decode_to_wave
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from .data import get_lang_symmap, get_random_prompt
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from .models.arch import AVAILABLE_ATTENTIONS
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try:
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import spaces
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USING_SPACES = True
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spaces_zerogpu_decorator = spaces.GPU
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except ImportError:
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USING_SPACES = False
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def spaces_zerogpu_decorator(func):
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return func
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is_windows = sys.platform.startswith("win")
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tts = None
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layout = {}
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layout["inference_tts"] = {}
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layout["inference_stt"] = {}
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layout["training"] = {}
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layout["dataset"] = {}
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layout["settings"] = {}
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for k in layout.keys():
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layout[k]["inputs"] = { "progress": None }
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layout[k]["outputs"] = {}
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layout[k]["buttons"] = {}
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# there's got to be a better way to go about this
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def gradio_wrapper(inputs):
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def decorated(fun):
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@functools.wraps(fun)
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def wrapped_function(*args, **kwargs):
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for i, key in enumerate(inputs):
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kwargs[key] = args[i]
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try:
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return fun(**kwargs)
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except Exception as e:
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raise gr.Error(str(e))
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return wrapped_function
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return decorated
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# returns a list of models, assuming the models are placed under ./training/ or ./models/ or ./data/models/
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def get_model_paths( paths=[Path("./training/"), Path("./models/"), Path("./data/models/")] ):
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configs = []
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for path in paths:
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if not path.exists():
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continue
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for yaml in path.glob("**/*.yaml"):
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if "/logs/" in str(yaml):
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continue
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configs.append( yaml )
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for sft in path.glob("**/*.sft"):
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if "/logs/" in str(sft):
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continue
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configs.append( sft )
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if is_windows:
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configs = [ str(p) for p in configs ]
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return configs
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def get_dtypes():
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return ["float32", "float16", "bfloat16", "float8_e5m2", "float8_e4m3fn", "auto"]
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def get_attentions():
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return AVAILABLE_ATTENTIONS + ["auto"]
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#@gradio_wrapper(inputs=layout["settings"]["inputs"].keys())
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def load_model( config, device, dtype, attention ):
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gr.Info(f"Loading: {config}")
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try:
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init_tts( config=Path(config), restart=True, device=device, dtype=dtype, attention=attention )
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except Exception as e:
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raise gr.Error(e)
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gr.Info(f"Loaded model")
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def get_speakers():
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return cfg.dataset.training
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def get_languages():
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return get_lang_symmap().keys()
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#@gradio_wrapper(inputs=layout["dataset"]["inputs"].keys())
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def load_sample( speaker ):
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metadata_path = cfg.metadata_dir / f'{speaker}.json'
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metadata = json_read( metadata_path )
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if not metadata:
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raise gr.Error(f"Metadata not found: {metadata_path}")
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key = random.choice( list(metadata.keys()) )
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path = cfg.data_dir / speaker / f'{key}.enc' # to-do: get proper file extension
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data = json_stringify( metadata[key], pretty=True )
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wav, sr = None, None
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if path.exists():
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artifact = np.load(path, allow_pickle=True)[()]
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codes = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=cfg.device)
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wav, sr = decode_to_wave( codes )
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wav = wav.squeeze(0).cpu().numpy()
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return data, (sr, wav)
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def init_tts(config=None, lora=None, restart=False, device="cuda", dtype="auto", attention=None):
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global tts
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if tts is not None:
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if not restart:
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return tts
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del tts
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tts = None
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parser = argparse.ArgumentParser(allow_abbrev=False, add_help=False)
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parser.add_argument("--yaml", type=Path, default=os.environ.get('VALLE_YAML', None)) # os environ so it can be specified in a HuggingFace Space too
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parser.add_argument("--model", type=Path, default=os.environ.get('VALLE_MODEL', None)) # os environ so it can be specified in a HuggingFace Space too
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parser.add_argument("--lora", type=Path, default=os.environ.get('VALLE_LORA', None)) # os environ so it can be specified in a HuggingFace Space too
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parser.add_argument("--device", type=str, default=device)
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parser.add_argument("--amp", action="store_true")
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parser.add_argument("--dtype", type=str, default=dtype)
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parser.add_argument("--attention", type=str, default=attention)
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args, unknown = parser.parse_known_args()
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if config:
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if config.suffix == ".yaml" and not args.yaml:
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args.yaml = config
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elif config.suffix == ".sft" and not args.model:
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args.model = config
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if lora and not args.lora:
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args.lora = lora
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if args.yaml:
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config = args.yaml
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elif args.model:
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config = args.model
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if args.lora:
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lora = args.lora
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tts = TTS( config=config, lora=args.lora, device=args.device, dtype=args.dtype if args.dtype != "auto" else None, amp=args.amp, attention=args.attention )
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return tts
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@spaces_zerogpu_decorator
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@gradio_wrapper(inputs=layout["inference_tts"]["inputs"].keys())
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def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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if not cfg.models:
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raise Exception("No model loaded.")
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if kwargs.pop("dynamic-sampling", False):
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kwargs['min-ar-temp'] = 0.01 if kwargs['ar-temp'] > 0.01 else 0.0
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kwargs['min-nar-temp'] = 0.0 # 0.85 if kwargs['nar-temp'] > 0.85 else 0.0 # should probably disable it for the NAR
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else:
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kwargs['min-ar-temp'] = -1
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kwargs['min-nar-temp'] = -1
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parser = argparse.ArgumentParser(allow_abbrev=False, add_help=False)
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# I'm very sure I can procedurally generate this list
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parser.add_argument("--text", type=str, default=kwargs["text"])
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parser.add_argument("--task", type=str, default="tts")
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parser.add_argument("--references", type=str, default=kwargs["reference"])
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parser.add_argument("--language", type=str, default=kwargs["language"])
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parser.add_argument("--input-prompt-length", type=float, default=kwargs["input-prompt-length"])
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parser.add_argument("--input-prompt-prefix", action='store_true', default=kwargs["input-prompt-prefix"] if cfg.experimental else False)
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parser.add_argument("--max-ar-steps", type=int, default=int(kwargs["max-seconds"]*cfg.dataset.frames_per_second))
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parser.add_argument("--max-nar-levels", type=int, default=kwargs["max-nar-levels"] if cfg.experimental else 0)
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parser.add_argument("--ar-temp", type=float, default=kwargs["ar-temp"])
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parser.add_argument("--nar-temp", type=float, default=kwargs["nar-temp"])
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parser.add_argument("--min-ar-temp", type=float, default=kwargs["min-ar-temp"])
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parser.add_argument("--min-nar-temp", type=float, default=kwargs["min-nar-temp"])
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parser.add_argument("--prefix-silence", type=float, default=kwargs["prefix-silence"] if cfg.experimental else 0)
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parser.add_argument("--top-p", type=float, default=kwargs["top-p"])
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parser.add_argument("--top-k", type=int, default=kwargs["top-k"])
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parser.add_argument("--min-p", type=float, default=kwargs["min-p"])
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parser.add_argument("--repetition-penalty", type=float, default=kwargs["repetition-penalty"])
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parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
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parser.add_argument("--length-penalty", type=float, default=kwargs["length-penalty"])
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parser.add_argument("--beam-width", type=int, default=kwargs["beam-width"])
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parser.add_argument("--mirostat-tau", type=float, default=kwargs["mirostat-tau"])
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parser.add_argument("--mirostat-eta", type=float, default=kwargs["mirostat-eta"])
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parser.add_argument("--dry-multiplier", type=float, default=kwargs["dry-multiplier"])
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parser.add_argument("--dry-base", type=float, default=kwargs["dry-base"])
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parser.add_argument("--dry-allowed-length", type=int, default=kwargs["dry-allowed-length"])
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parser.add_argument("--entropix-sampling", action="store_true")
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parser.add_argument("--layer-skip", action="store_true")
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parser.add_argument("--layer-skip-exit-layer", type=int, default=kwargs["layer-skip-exit-layer"] if cfg.experimental else -1)
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parser.add_argument("--layer-skip-entropy-threshold", type=int, default=kwargs["layer-skip-entropy-threshold"] if cfg.experimental else 0.1)
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parser.add_argument("--layer-skip-varentropy-threshold", type=int, default=kwargs["layer-skip-varentropy-threshold"] if cfg.experimental else 0.1)
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parser.add_argument("--refine-on-stop", action="store_true")
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args, unknown = parser.parse_known_args()
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if is_windows:
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tmp = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
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else:
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tmp = tempfile.NamedTemporaryFile(suffix='.wav')
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"""
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if not args.references:
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raise Exception("No reference audio provided.")
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"""
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if kwargs.pop("entropix-sampling", False):
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args.entropix_sampling = True
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if kwargs.pop("layer-skip", False):
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args.layer_skip = True
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if kwargs.pop("refine-on-stop", False):
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args.refine_on_stop = True
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tts = init_tts()
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gr.Info("Inferencing...")
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with timer("Inferenced in", callback=lambda msg: gr.Info( msg )) as t:
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wav, sr = tts.inference(
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text=args.text,
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language=args.language,
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task=args.task,
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references=args.references.split(";") if args.references is not None else [],
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out_path=tmp.name,
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max_ar_steps=args.max_ar_steps,
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max_nar_levels=args.max_nar_levels,
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input_prompt_length=args.input_prompt_length,
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input_prompt_prefix=args.input_prompt_prefix,
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prefix_silence=args.prefix_silence,
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ar_temp=args.ar_temp,
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nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp,
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min_nar_temp=args.min_nar_temp,
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top_p=args.top_p,
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top_k=args.top_k,
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min_p=args.min_p,
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beam_width=args.beam_width,
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repetition_penalty=args.repetition_penalty,
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repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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mirostat_tau=args.mirostat_tau,
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mirostat_eta=args.mirostat_eta,
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dry_multiplier=args.dry_multiplier,
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dry_base=args.dry_base,
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dry_allowed_length=args.dry_allowed_length,
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entropix_sampling=args.entropix_sampling,
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layer_skip=args.layer_skip,
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layer_skip_entropy_threshold=args.layer_skip_entropy_threshold,
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layer_skip_varentropy_threshold=args.layer_skip_varentropy_threshold,
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refine_on_stop=args.refine_on_stop,
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)
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wav = wav.squeeze(0).cpu().numpy()
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return (sr, wav)
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@gradio_wrapper(inputs=layout["inference_stt"]["inputs"].keys())
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def do_inference_stt( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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if not cfg.models:
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raise Exception("No model loaded.")
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if kwargs.pop("dynamic-sampling", False):
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kwargs['min-ar-temp'] = 0.85 if kwargs['ar-temp'] > 0.85 else 0.0
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else:
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kwargs['min-ar-temp'] = -1
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parser = argparse.ArgumentParser(allow_abbrev=False, add_help=False)
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# I'm very sure I can procedurally generate this list
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parser.add_argument("--references", type=str, default=kwargs["reference"])
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parser.add_argument("--language", type=str, default=kwargs["language"])
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parser.add_argument("--max-ar-steps", type=int, default=0)
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parser.add_argument("--ar-temp", type=float, default=kwargs["ar-temp"])
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parser.add_argument("--min-ar-temp", type=float, default=kwargs["min-ar-temp"])
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parser.add_argument("--top-p", type=float, default=kwargs["top-p"])
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parser.add_argument("--top-k", type=int, default=kwargs["top-k"])
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parser.add_argument("--min-p", type=int, default=kwargs["min-p"])
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parser.add_argument("--repetition-penalty", type=float, default=kwargs["repetition-penalty"])
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parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
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parser.add_argument("--length-penalty", type=float, default=kwargs["length-penalty"])
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parser.add_argument("--beam-width", type=int, default=kwargs["beam-width"])
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parser.add_argument("--mirostat-tau", type=float, default=kwargs["mirostat-tau"])
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parser.add_argument("--mirostat-eta", type=float, default=kwargs["mirostat-eta"])
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parser.add_argument("--dry-multiplier", type=float, default=kwargs["dry-multiplier"])
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parser.add_argument("--dry-base", type=float, default=kwargs["dry-base"])
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parser.add_argument("--dry-allowed-length", type=int, default=kwargs["dry-allowed-length"])
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parser.add_argument("--entropix-sampling", action="store_true")
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args, unknown = parser.parse_known_args()
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"""
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if not args.references:
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raise Exception("No reference audio provided.")
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"""
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args.references = args.references.split(";") if args.references is not None else []
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if args.max_ar_steps == 0:
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for i, path in enumerate( args.references ):
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metadata = torchaudio.info(path)
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duration = metadata.num_frames / metadata.sample_rate
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args.max_ar_steps += duration
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args.max_ar_steps = math.floor( args.max_ar_steps * 20 ) # assume 20 tokens per second
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if kwargs.pop("entropix-sampling", False):
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args.entropix_sampling = True
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tts = init_tts()
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gr.Info("Inferencing...")
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with timer("Inferenced in") as t:
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text = tts.inference(
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text="",
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language=args.language,
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task="stt",
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references=args.references,
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max_ar_steps=args.max_ar_steps,
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ar_temp=args.ar_temp,
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min_ar_temp=args.min_ar_temp,
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top_p=args.top_p,
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top_k=args.top_k,
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min_p=args.min_p,
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repetition_penalty=args.repetition_penalty,
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repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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mirostat_tau=args.mirostat_tau,
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mirostat_eta=args.mirostat_eta,
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dry_multiplier=args.dry_multiplier,
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dry_base=args.dry_base,
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dry_allowed_length=args.dry_allowed_length,
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entropix_sampling=args.entropix_sampling,
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)
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return text
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"""
|
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@gradio_wrapper(inputs=layout["training"]["inputs"].keys())
|
|
def do_training( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
|
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while True:
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metrics = next(it)
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yield metrics
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"""
|
|
|
|
# setup args
|
|
parser = argparse.ArgumentParser(allow_abbrev=False)
|
|
parser.add_argument("--yaml", type=Path, default=os.environ.get('VALLE_YAML', None)) # os environ so it can be specified in a HuggingFace Space too
|
|
parser.add_argument("--model", type=Path, default=os.environ.get('VALLE_MODEL', None)) # os environ so it can be specified in a HuggingFace Space too
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|
parser.add_argument("--listen", default=None, help="Path for Gradio to listen on")
|
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parser.add_argument("--share", action="store_true")
|
|
parser.add_argument("--render_markdown", action="store_true", default="VALLE_YAML" in os.environ)
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|
args, unknown = parser.parse_known_args()
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|
|
|
args.listen_host = None
|
|
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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|
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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:
|
|
pass
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|
|
if args.listen_port is not None:
|
|
args.listen_port = int(args.listen_port)
|
|
if args.listen_port == 0:
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args.listen_port = None
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|
|
# setup gradio
|
|
ui = gr.Blocks()
|
|
with ui:
|
|
with gr.Tab("Inference"):
|
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with gr.Tab("Text-to-Speech"):
|
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with gr.Row():
|
|
with gr.Column(scale=8):
|
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layout["inference_tts"]["inputs"]["text"] = gr.Textbox(lines=5, value=get_random_prompt, label="Input Prompt")
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with gr.Row():
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with gr.Column(scale=1):
|
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layout["inference_tts"]["inputs"]["reference"] = gr.Audio(label="Audio Input", sources=["upload"], type="filepath") #, info="Reference audio for TTS")
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# layout["inference_tts"]["stop"] = gr.Button(value="Stop")
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layout["inference_tts"]["outputs"]["output"] = gr.Audio(label="Output")
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|
layout["inference_tts"]["buttons"]["inference"] = gr.Button(value="Inference")
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with gr.Column(scale=7):
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|
with gr.Tab("Basic Settings"):
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["max-seconds"] = gr.Slider(value=12, minimum=1, maximum=32, step=0.1, label="Maximum Seconds", info="Limits how many steps to perform in the AR pass.")
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|
layout["inference_tts"]["inputs"]["input-prompt-length"] = gr.Slider(value=5.0, minimum=0.0, maximum=12.0, step=0.05, label="Input Prompt Repeat/Trim Length", info="Repeats and trims the input prompt down to X seconds. Set 0 to disable.")
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with gr.Row():
|
|
layout["inference_tts"]["inputs"]["ar-temp"] = gr.Slider(value=0.5, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy* sample)")
|
|
layout["inference_tts"]["inputs"]["nar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (NAR)", info="Modifies the randomness from the samples in the NAR. (0 to greedy sample)")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["language"] = gr.Dropdown(choices=get_languages(), label="Language", value="en")
|
|
with gr.Tab("Sampler Settings"):
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
|
|
layout["inference_tts"]["inputs"]["top-k"] = gr.Slider(value=0, minimum=0, maximum=1024, step=1, label="Top K", info="Limits the samples to the top K of probabilities.")
|
|
layout["inference_tts"]["inputs"]["min-p"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Min P")
|
|
layout["inference_tts"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.5, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
|
|
layout["inference_tts"]["inputs"]["repetition-penalty-decay"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty Length Decay", info="Modifies the reptition penalty based on how far back in time the token appeared in the sequence.")
|
|
layout["inference_tts"]["inputs"]["length-penalty"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Length Penalty", info="(AR only) Modifies the probability of a stop token based on the current length of the sequence.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["mirostat-tau"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="Mirostat τ (Tau)", info="The \"surprise\" value when performing mirostat sampling. 0 to disable.")
|
|
layout["inference_tts"]["inputs"]["mirostat-eta"] = gr.Slider(value=0.0, minimum=0.0, maximum=2.0, step=0.05, label="Mirostat η (Eta)", info="The \"learning rate\" during mirostat sampling applied to the maximum surprise.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["dry-multiplier"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="DRY Multiplier", info="The multiplying factor for the DRY score penalty (0 to disable DRY sampling).")
|
|
layout["inference_tts"]["inputs"]["dry-base"] = gr.Slider(value=1.75, minimum=0.0, maximum=8.0, step=0.05, label="DRY Base", info="The base of the exponent in the DRY score penalty")
|
|
layout["inference_tts"]["inputs"]["dry-allowed-length"] = gr.Slider(value=2, minimum=0, maximum=75, step=1, label="Allowed Length", info="The maximimum length a token can be to perform DRY penalty with.")
|
|
if cfg.experimental:
|
|
with gr.Tab("Experimental Settings"):
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["max-nar-levels"] = gr.Slider(value=7, minimum=0, maximum=7, step=1, label="Max NAR Levels", info="Limits how many steps to perform in the NAR pass.")
|
|
layout["inference_tts"]["inputs"]["input-prompt-prefix"] = gr.Checkbox(label="Input Prompt as Prefix", info="Treats the input prompt clip as the prefix of the generated sequence.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["prefix-silence"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Silence Prefix Duration", info="Amount of silence to prefix to the output response before beginning inference.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
|
|
layout["inference_tts"]["inputs"]["entropix-sampling"] = gr.Checkbox(label="Entropix Sampling", info="Dynamically samples based on entropy/varentropy values from the logits / attention scores.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["layer-skip"] = gr.Checkbox(label="Layer Skip", info="Performs self-speculative early exit 'sampling'")
|
|
layout["inference_tts"]["inputs"]["refine-on-stop"] = gr.Checkbox(label="Refine on <stop>", info="Uses the last step's logits for the AR sequence instead.")
|
|
with gr.Row():
|
|
layout["inference_tts"]["inputs"]["layer-skip-exit-layer"] = gr.Slider(value=11, minimum=0, maximum=11, step=1, label="Layer Skip Exit Layer", info="Maximum model layer to exit early from.")
|
|
layout["inference_tts"]["inputs"]["layer-skip-entropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Entropy Threshold", info="Entropy threshold for early-exit")
|
|
layout["inference_tts"]["inputs"]["layer-skip-varentropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Varentropy Threshold", info="Varentropy threshold for early-exit")
|
|
|
|
|
|
layout["inference_tts"]["buttons"]["inference"].click(
|
|
fn=do_inference_tts,
|
|
inputs=[ x for x in layout["inference_tts"]["inputs"].values() if x is not None],
|
|
outputs=[ x for x in layout["inference_tts"]["outputs"].values() if x is not None]
|
|
)
|
|
|
|
with gr.Tab("Speech to Text"):
|
|
with gr.Row():
|
|
with gr.Column(scale=8):
|
|
layout["inference_stt"]["outputs"]["ouput"] = gr.Textbox(lines=1, label="Output Transcription")
|
|
with gr.Row():
|
|
with gr.Column(scale=1):
|
|
layout["inference_stt"]["inputs"]["reference"] = gr.Audio(label="Audio Input", sources=["upload"], type="filepath") #, info="Reference audio for TTS")
|
|
# layout["inference_stt"]["stop"] = gr.Button(value="Stop")
|
|
layout["inference_stt"]["buttons"]["inference"] = gr.Button(value="Inference")
|
|
with gr.Column(scale=7):
|
|
with gr.Tab("Basic Settings"):
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["ar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy sample)")
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
|
|
layout["inference_stt"]["inputs"]["language"] = gr.Dropdown(choices=get_languages(), label="Language", value="en")
|
|
with gr.Tab("Sampler Settings"):
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
|
|
layout["inference_stt"]["inputs"]["top-k"] = gr.Slider(value=0, minimum=0, maximum=1024, step=1, label="Top K", info="Limits the samples to the top K of probabilities.")
|
|
layout["inference_stt"]["inputs"]["min-p"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Min P")
|
|
layout["inference_stt"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.25, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
|
|
layout["inference_stt"]["inputs"]["repetition-penalty-decay"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty Length Decay", info="Modifies the reptition penalty based on how far back in time the token appeared in the sequence.")
|
|
layout["inference_stt"]["inputs"]["length-penalty"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Length Penalty", info="(AR only) Modifies the probability of a stop token based on the current length of the sequence.")
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["mirostat-tau"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="Mirostat τ (Tau)", info="The \"surprise\" value when performing mirostat sampling. 0 to disable.")
|
|
layout["inference_stt"]["inputs"]["mirostat-eta"] = gr.Slider(value=0.0, minimum=0.0, maximum=2.0, step=0.05, label="Mirostat η (Eta)", info="The \"learning rate\" during mirostat sampling applied to the maximum surprise.")
|
|
with gr.Row():
|
|
layout["inference_stt"]["inputs"]["dry-multiplier"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="DRY Multiplier", info="The multiplying factor for the DRY score penalty (0 to disable DRY sampling).")
|
|
layout["inference_stt"]["inputs"]["dry-base"] = gr.Slider(value=1.75, minimum=0.0, maximum=8.0, step=0.05, label="DRY Base", info="The base of the exponent in the DRY score penalty")
|
|
layout["inference_stt"]["inputs"]["dry-allowed-length"] = gr.Slider(value=2, minimum=0, maximum=75, step=1, label="Allowed Length", info="The maximimum length a token can be to perform DRY penalty with.")
|
|
|
|
layout["inference_stt"]["buttons"]["inference"].click(
|
|
fn=do_inference_stt,
|
|
inputs=[ x for x in layout["inference_stt"]["inputs"].values() if x is not None],
|
|
outputs=[ x for x in layout["inference_stt"]["outputs"].values() if x is not None]
|
|
)
|
|
|
|
|
|
"""
|
|
with gr.Tab("Training"):
|
|
with gr.Row():
|
|
with gr.Column(scale=1):
|
|
layout["training"]["outputs"]["console"] = gr.Textbox(lines=8, label="Console Log")
|
|
with gr.Row():
|
|
with gr.Column(scale=1):
|
|
layout["training"]["buttons"]["train"] = gr.Button(value="Train")
|
|
|
|
layout["training"]["buttons"]["train"].click(
|
|
fn=do_training,
|
|
outputs=[ x for x in layout["training"]["outputs"].values() if x is not None],
|
|
)
|
|
"""
|
|
|
|
if not USING_SPACES:
|
|
with gr.Tab("Dataset"):
|
|
with gr.Row():
|
|
with gr.Column(scale=7):
|
|
layout["dataset"]["outputs"]["transcription"] = gr.Textbox(lines=5, label="Sample Metadata")
|
|
with gr.Column(scale=1):
|
|
layout["dataset"]["inputs"]["speaker"] = gr.Dropdown(choices=get_speakers(), label="Speakers")
|
|
layout["dataset"]["outputs"]["audio"] = gr.Audio(label="Output")
|
|
layout["dataset"]["buttons"]["sample"] = gr.Button(value="Sample")
|
|
|
|
layout["dataset"]["buttons"]["sample"].click(
|
|
fn=load_sample,
|
|
inputs=[ x for x in layout["dataset"]["inputs"].values() if x is not None],
|
|
outputs=[ x for x in layout["dataset"]["outputs"].values() if x is not None],
|
|
)
|
|
|
|
if not USING_SPACES:
|
|
with gr.Tab("Settings"):
|
|
with gr.Row():
|
|
with gr.Column(scale=7):
|
|
with gr.Row():
|
|
layout["settings"]["inputs"]["models"] = gr.Dropdown(choices=get_model_paths(), value=args.yaml or args.model, label="Model")
|
|
layout["settings"]["inputs"]["device"] = gr.Dropdown(choices=get_devices(), value="cuda:0", label="Device")
|
|
layout["settings"]["inputs"]["dtype"] = gr.Dropdown(choices=get_dtypes(), value="auto", label="Precision")
|
|
layout["settings"]["inputs"]["attentions"] = gr.Dropdown(choices=get_attentions(), value="auto", label="Attentions")
|
|
with gr.Column(scale=1):
|
|
layout["settings"]["buttons"]["load"] = gr.Button(value="Load Model")
|
|
|
|
layout["settings"]["buttons"]["load"].click(
|
|
fn=load_model,
|
|
inputs=[ x for x in layout["settings"]["inputs"].values() if x is not None],
|
|
outputs=[ x for x in layout["settings"]["outputs"].values() if x is not None],
|
|
)
|
|
|
|
if os.path.exists("README.md") and args.render_markdown:
|
|
md = open("README.md", "r", encoding="utf-8").read()
|
|
# remove HF's metadata
|
|
if md.startswith("---\n"):
|
|
md = "".join(md.split("---")[2:])
|
|
gr.Markdown(md)
|
|
|
|
def start( lock=True ):
|
|
setup_logging()
|
|
|
|
ui.queue(max_size=8)
|
|
ui.launch(share=args.share, server_name=args.listen_host, server_port=args.listen_port, prevent_thread_lock=not lock)
|
|
|
|
if __name__ == "__main__":
|
|
if not USING_SPACES:
|
|
start()
|
|
else:
|
|
ui.queue().launch() |