chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,381 @@
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import json
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import os
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import pathlib
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import sys
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from collections import OrderedDict
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from pathlib import Path
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import click
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from typing import Tuple
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root_dir = Path(__file__).resolve().parent.parent
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os.environ['PYTHONPATH'] = str(root_dir)
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sys.path.insert(0, str(root_dir))
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def find_exp(exp):
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if not (root_dir / 'checkpoints' / exp).exists():
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for subdir in (root_dir / 'checkpoints').iterdir():
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if not subdir.is_dir():
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continue
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if subdir.name.startswith(exp):
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print(f'| match ckpt by prefix: {subdir.name}')
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exp = subdir.name
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break
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else:
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raise click.BadParameter(
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f'There are no matching exp starting with \'{exp}\' in \'checkpoints\' folder. '
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'Please specify \'--exp\' as the folder name or prefix.'
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)
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else:
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print(f'| found ckpt by name: {exp}')
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return exp
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@click.group()
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def main():
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pass
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@main.command(help='Run DiffSinger acoustic model inference')
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@click.argument(
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'proj', type=click.Path(
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exists=True, file_okay=True, dir_okay=False, readable=True,
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path_type=pathlib.Path, resolve_path=True
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),
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metavar='DS_FILE'
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)
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@click.option(
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'--exp', type=str,
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required=True, metavar='EXP',
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callback=lambda ctx, param, value: find_exp(value),
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help='Selection of model'
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)
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@click.option(
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'--ckpt', type=click.IntRange(min=0),
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required=False, metavar='STEPS',
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help='Selection of checkpoint training steps'
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)
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@click.option(
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'--spk', type=click.STRING,
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required=False,
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help='Speaker name or mixture of speakers'
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)
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@click.option(
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'--lang', type=click.STRING,
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required=False,
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help='Default language name'
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)
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@click.option(
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'--out', type=click.Path(
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file_okay=False, dir_okay=True, path_type=pathlib.Path
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),
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required=False,
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help='Path of the output folder'
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)
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@click.option(
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'--title', type=click.STRING,
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required=False,
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help='Title of output file'
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)
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@click.option(
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'--num', type=click.IntRange(min=1),
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required=False, default=1,
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help='Number of runs'
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)
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@click.option(
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'--key', type=click.INT,
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required=False, default=0,
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help='Key transition of pitch'
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)
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@click.option(
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'--gender', type=click.FloatRange(min=-1, max=1),
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required=False,
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help='Formant shifting (gender control)'
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)
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@click.option(
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'--seed', type=click.INT,
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required=False, default=-1,
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help='Random seed of the inference'
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)
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@click.option(
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'--depth', type=click.FloatRange(min=0, max=1),
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required=False,
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help='Shallow diffusion depth'
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)
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@click.option(
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'--steps', type=click.IntRange(min=1),
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required=False,
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help='Diffusion sampling steps'
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)
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@click.option(
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'--mel', is_flag=True,
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help='Save intermediate mel format instead of waveform'
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)
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def acoustic(
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proj: pathlib.Path,
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exp: str,
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ckpt: int,
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spk: str,
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lang: str,
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out: pathlib.Path,
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title: str,
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num: int,
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key: int,
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gender: float,
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seed: int,
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depth: float,
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steps: int,
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mel: bool
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):
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name = proj.stem if not title else title
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if out is None:
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out = proj.parent
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with open(proj, 'r', encoding='utf-8') as f:
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params = json.load(f)
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if not isinstance(params, list):
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params = [params]
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if len(params) == 0:
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print('The input file is empty.')
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exit()
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from utils.infer_utils import trans_key, parse_commandline_spk_mix
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if key != 0:
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params = trans_key(params, key)
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key_suffix = '%+dkey' % key
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if not title:
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name += key_suffix
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print(f'| key transition: {key:+d}')
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sys.argv = [
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sys.argv[0],
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'--exp_name',
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exp,
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'--infer'
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]
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from utils.hparams import set_hparams, hparams
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set_hparams()
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# Check for vocoder path
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assert mel or (root_dir / hparams['vocoder_ckpt']).exists(), \
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f'Vocoder ckpt \'{hparams["vocoder_ckpt"]}\' not found. ' \
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f'Please put it to the checkpoints directory to run inference.'
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# For compatibility:
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# migrate timesteps, K_step, K_step_infer, diff_speedup to time_scale_factor, T_start, T_start_infer, sampling_steps
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if 'diff_speedup' not in hparams and 'pndm_speedup' in hparams:
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hparams['diff_speedup'] = hparams['pndm_speedup']
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if 'T_start' not in hparams:
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hparams['T_start'] = 1 - hparams['K_step'] / hparams['timesteps']
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if 'T_start_infer' not in hparams:
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hparams['T_start_infer'] = 1 - hparams['K_step_infer'] / hparams['timesteps']
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if 'sampling_steps' not in hparams:
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if hparams['use_shallow_diffusion']:
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hparams['sampling_steps'] = hparams['K_step_infer'] // hparams['diff_speedup']
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else:
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hparams['sampling_steps'] = hparams['timesteps'] // hparams['diff_speedup']
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if 'time_scale_factor' not in hparams:
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hparams['time_scale_factor'] = hparams['timesteps']
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if depth is not None:
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assert depth <= 1 - hparams['T_start'], (
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f"Depth should not be larger than 1 - T_start ({1 - hparams['T_start']})"
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)
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hparams['K_step_infer'] = round(hparams['timesteps'] * depth)
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hparams['T_start_infer'] = 1 - depth
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if steps is not None:
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if hparams['use_shallow_diffusion']:
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step_size = (1 - hparams['T_start_infer']) / steps
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if 'K_step_infer' in hparams:
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hparams['diff_speedup'] = round(step_size * hparams['K_step_infer'])
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else:
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if 'timesteps' in hparams:
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hparams['diff_speedup'] = round(hparams['timesteps'] / steps)
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hparams['sampling_steps'] = steps
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spk_mix = parse_commandline_spk_mix(spk) if hparams['use_spk_id'] and spk is not None else None
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for param in params:
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if gender is not None and hparams['use_key_shift_embed']:
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param['gender'] = gender
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if spk_mix is not None:
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param['spk_mix'] = spk_mix
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if lang is not None:
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param['lang'] = lang
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from inference.ds_acoustic import DiffSingerAcousticInfer
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infer_ins = DiffSingerAcousticInfer(load_vocoder=not mel, ckpt_steps=ckpt)
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print(f'| Model: {type(infer_ins.model)}')
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try:
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infer_ins.run_inference(
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params, out_dir=out, title=name, num_runs=num,
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spk_mix=spk_mix, seed=seed, save_mel=mel
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)
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except KeyboardInterrupt:
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exit(-1)
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@main.command(help='Run DiffSinger variance model inference')
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@click.argument(
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'proj', type=click.Path(
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exists=True, file_okay=True, dir_okay=False, readable=True,
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path_type=pathlib.Path, resolve_path=True
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),
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metavar='DS_FILE'
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)
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@click.option(
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'--exp', type=str,
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required=True, metavar='EXP',
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callback=lambda ctx, param, value: find_exp(value),
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help='Selection of model'
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)
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@click.option(
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'--ckpt', type=click.IntRange(min=0),
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required=False, metavar='STEPS',
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help='Selection of checkpoint training steps'
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)
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@click.option(
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'--predict', type=click.STRING,
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multiple=True, metavar='TAGS',
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help='Parameters to predict'
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)
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@click.option(
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'--spk', type=click.STRING,
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required=False,
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help='Speaker name or mixture of speakers'
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)
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@click.option(
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'--lang', type=click.STRING,
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required=False,
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help='Default language name'
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)
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@click.option(
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'--out', type=click.Path(
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file_okay=False, dir_okay=True, path_type=pathlib.Path
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),
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required=False,
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help='Path of the output folder'
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)
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@click.option(
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'--title', type=click.STRING,
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required=False,
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help='Title of output file'
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)
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@click.option(
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'--num', type=click.IntRange(min=1),
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required=False, default=1,
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help='Number of runs'
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)
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@click.option(
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'--key', type=click.INT,
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required=False, default=0,
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help='Key transition of pitch'
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)
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@click.option(
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'--expr', type=click.FloatRange(min=0, max=1),
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required=False, help='Static expressiveness control'
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)
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@click.option(
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'--seed', type=click.INT,
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required=False, default=-1,
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help='Random seed of the inference'
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)
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@click.option(
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'--steps', type=click.IntRange(min=1),
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required=False,
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help='Diffusion sampling steps'
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)
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def variance(
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proj: pathlib.Path,
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exp: str,
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ckpt: int,
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spk: str,
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lang: str,
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predict: Tuple[str],
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out: pathlib.Path,
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title: str,
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num: int,
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key: int,
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expr: float,
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seed: int,
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steps: int
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):
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name = proj.stem if not title else title
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if out is None:
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out = proj.parent
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if (not out or out.resolve() == proj.parent.resolve()) and not title:
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name += '_variance'
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with open(proj, 'r', encoding='utf-8') as f:
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params = json.load(f)
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if not isinstance(params, list):
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params = [params]
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params = [OrderedDict(p) for p in params]
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if len(params) == 0:
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print('The input file is empty.')
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exit()
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from utils.infer_utils import trans_key, parse_commandline_spk_mix
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if key != 0:
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params = trans_key(params, key)
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key_suffix = '%+dkey' % key
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if not title:
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name += key_suffix
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print(f'| key transition: {key:+d}')
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sys.argv = [
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sys.argv[0],
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'--exp_name',
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exp,
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'--infer'
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]
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from utils.hparams import set_hparams, hparams
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set_hparams()
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# For compatibility:
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# migrate timesteps, K_step, K_step_infer, diff_speedup to time_scale_factor, T_start, T_start_infer, sampling_steps
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if 'diff_speedup' not in hparams and 'pndm_speedup' in hparams:
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hparams['diff_speedup'] = hparams['pndm_speedup']
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if 'sampling_steps' not in hparams:
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hparams['sampling_steps'] = hparams['timesteps'] // hparams['diff_speedup']
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if 'time_scale_factor' not in hparams:
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hparams['time_scale_factor'] = hparams['timesteps']
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if steps is not None:
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if 'timesteps' in hparams:
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hparams['diff_speedup'] = round(hparams['timesteps'] / steps)
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hparams['sampling_steps'] = steps
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spk_mix = parse_commandline_spk_mix(spk) if hparams['use_spk_id'] and spk is not None else None
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for param in params:
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if expr is not None:
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param['expr'] = expr
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if spk_mix is not None:
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param['ph_spk_mix_backup'] = param.get('ph_spk_mix')
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param['spk_mix_backup'] = param.get('spk_mix')
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param['ph_spk_mix'] = param['spk_mix'] = spk_mix
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if lang is not None:
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param['lang'] = lang
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from inference.ds_variance import DiffSingerVarianceInfer
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infer_ins = DiffSingerVarianceInfer(ckpt_steps=ckpt, predictions=set(predict))
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print(f'| Model: {type(infer_ins.model)}')
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try:
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infer_ins.run_inference(
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params, out_dir=out, title=name,
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num_runs=num, seed=seed
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)
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except KeyboardInterrupt:
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exit(-1)
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if __name__ == '__main__':
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main()
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Reference in New Issue
Block a user