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from collections import OrderedDict
import tqdm
import json
import pathlib
import numpy as np
import torch
from typing import Dict
from basics.base_svs_infer import BaseSVSInfer
from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST
from modules.fastspeech.tts_modules import LengthRegulator
from modules.toplevel import DiffSingerAcoustic, ShallowDiffusionOutput
from modules.vocoders.registry import VOCODERS
from utils import load_ckpt
from utils.hparams import hparams
from utils.infer_utils import cross_fade, resample_align_curve, save_wav
from utils.phoneme_utils import build_phoneme_list
from utils.text_encoder import TokenTextEncoder
class DiffSingerAcousticInfer(BaseSVSInfer):
def __init__(self, device=None, load_model=True, load_vocoder=True, ckpt_steps=None):
super().__init__(device=device)
if load_model:
self.variance_checklist = []
self.variances_to_embed = set()
if hparams.get('use_energy_embed', False):
self.variances_to_embed.add('energy')
if hparams.get('use_breathiness_embed', False):
self.variances_to_embed.add('breathiness')
if hparams.get('use_voicing_embed', False):
self.variances_to_embed.add('voicing')
if hparams.get('use_tension_embed', False):
self.variances_to_embed.add('tension')
self.ph_encoder = TokenTextEncoder(vocab_list=build_phoneme_list())
if hparams['use_spk_id']:
with open(pathlib.Path(hparams['work_dir']) / 'spk_map.json', 'r', encoding='utf8') as f:
self.spk_map = json.load(f)
assert isinstance(self.spk_map, dict) and len(self.spk_map) > 0, 'Invalid or empty speaker map!'
assert len(self.spk_map) == len(set(self.spk_map.values())), 'Duplicate speaker id in speaker map!'
self.model = self.build_model(ckpt_steps=ckpt_steps)
self.lr = LengthRegulator().to(self.device)
if load_vocoder:
self.vocoder = self.build_vocoder()
def build_model(self, ckpt_steps=None):
model = DiffSingerAcoustic(
vocab_size=len(self.ph_encoder),
out_dims=hparams['audio_num_mel_bins']
).eval().to(self.device)
load_ckpt(model, hparams['work_dir'], ckpt_steps=ckpt_steps,
prefix_in_ckpt='model', strict=True, device=self.device)
return model
def build_vocoder(self):
if hparams['vocoder'] in VOCODERS:
vocoder = VOCODERS[hparams['vocoder']]()
else:
vocoder = VOCODERS[hparams['vocoder'].split('.')[-1]]()
vocoder.to_device(self.device)
return vocoder
def preprocess_input(self, param, idx=0):
"""
:param param: one segment in the .ds file
:param idx: index of the segment
:return: batch of the model inputs
"""
batch = {}
summary = OrderedDict()
txt_tokens = torch.LongTensor([self.ph_encoder.encode(param['ph_seq'])]).to(self.device) # => [B, T_txt]
batch['tokens'] = txt_tokens
ph_dur = torch.from_numpy(np.array(param['ph_dur'].split(), np.float32)).to(self.device)
ph_acc = torch.round(torch.cumsum(ph_dur, dim=0) / self.timestep + 0.5).long()
durations = torch.diff(ph_acc, dim=0, prepend=torch.LongTensor([0]).to(self.device))[None] # => [B=1, T_txt]
mel2ph = self.lr(durations, txt_tokens == 0) # => [B=1, T]
batch['mel2ph'] = mel2ph
length = mel2ph.size(1) # => T
summary['tokens'] = txt_tokens.size(1)
summary['frames'] = length
summary['seconds'] = '%.2f' % (length * self.timestep)
if hparams['use_spk_id']:
spk_mix_id, spk_mix_value = self.load_speaker_mix(
param_src=param, summary_dst=summary, mix_mode='frame', mix_length=length
)
batch['spk_mix_id'] = spk_mix_id
batch['spk_mix_value'] = spk_mix_value
batch['f0'] = torch.from_numpy(resample_align_curve(
np.array(param['f0_seq'].split(), np.float32),
original_timestep=float(param['f0_timestep']),
target_timestep=self.timestep,
align_length=length
)).to(self.device)[None]
for v_name in VARIANCE_CHECKLIST:
if v_name in self.variances_to_embed:
batch[v_name] = torch.from_numpy(resample_align_curve(
np.array(param[v_name].split(), np.float32),
original_timestep=float(param[f'{v_name}_timestep']),
target_timestep=self.timestep,
align_length=length
)).to(self.device)[None]
summary[v_name] = 'manual'
if hparams['use_key_shift_embed']:
shift_min, shift_max = hparams['augmentation_args']['random_pitch_shifting']['range']
gender = param.get('gender')
if gender is None:
gender = 0.
if isinstance(gender, (int, float, bool)): # static gender value
summary['gender'] = f'static({gender:.3f})'
key_shift_value = gender * shift_max if gender >= 0 else gender * abs(shift_min)
batch['key_shift'] = torch.FloatTensor([key_shift_value]).to(self.device)[:, None] # => [B=1, T=1]
else:
summary['gender'] = 'dynamic'
gender_seq = resample_align_curve(
np.array(gender.split(), np.float32),
original_timestep=float(param['gender_timestep']),
target_timestep=self.timestep,
align_length=length
)
gender_mask = gender_seq >= 0
key_shift_seq = gender_seq * (gender_mask * shift_max + (1 - gender_mask) * abs(shift_min))
batch['key_shift'] = torch.clip(
torch.from_numpy(key_shift_seq.astype(np.float32)).to(self.device)[None], # => [B=1, T]
min=shift_min, max=shift_max
)
if hparams['use_speed_embed']:
if param.get('velocity') is None:
summary['velocity'] = 'default'
batch['speed'] = torch.FloatTensor([1.]).to(self.device)[:, None] # => [B=1, T=1]
else:
summary['velocity'] = 'manual'
speed_min, speed_max = hparams['augmentation_args']['random_time_stretching']['range']
speed_seq = resample_align_curve(
np.array(param['velocity'].split(), np.float32),
original_timestep=float(param['velocity_timestep']),
target_timestep=self.timestep,
align_length=length
)
batch['speed'] = torch.clip(
torch.from_numpy(speed_seq.astype(np.float32)).to(self.device)[None], # => [B=1, T]
min=speed_min, max=speed_max
)
print(f'[{idx}]\t' + ', '.join(f'{k}: {v}' for k, v in summary.items()))
return batch
@torch.no_grad()
def forward_model(self, sample):
txt_tokens = sample['tokens']
variances = {
v_name: sample.get(v_name)
for v_name in self.variances_to_embed
}
if hparams['use_spk_id']:
spk_mix_id = sample['spk_mix_id']
spk_mix_value = sample['spk_mix_value']
# perform mixing on spk embed
spk_mix_embed = torch.sum(
self.model.fs2.spk_embed(spk_mix_id) * spk_mix_value.unsqueeze(3), # => [B, T, N, H]
dim=2, keepdim=False
) # => [B, T, H]
else:
spk_mix_embed = None
mel_pred: ShallowDiffusionOutput = self.model(
txt_tokens, mel2ph=sample['mel2ph'], f0=sample['f0'], **variances,
key_shift=sample.get('key_shift'), speed=sample.get('speed'),
spk_mix_embed=spk_mix_embed, infer=True
)
return mel_pred.diff_out
@torch.no_grad()
def run_vocoder(self, spec, **kwargs):
y = self.vocoder.spec2wav_torch(spec, **kwargs)
return y[None]
def run_inference(
self, params,
out_dir: pathlib.Path = None,
title: str = None,
num_runs: int = 1,
spk_mix: Dict[str, float] = None,
seed: int = -1,
save_mel: bool = False
):
batches = [self.preprocess_input(param, idx=i) for i, param in enumerate(params)]
out_dir.mkdir(parents=True, exist_ok=True)
suffix = '.wav' if not save_mel else '.mel.pt'
for i in range(num_runs):
if save_mel:
result = []
else:
result = np.zeros(0)
current_length = 0
for param, batch in tqdm.tqdm(
zip(params, batches), desc='infer segments', total=len(params)
):
if 'seed' in param:
torch.manual_seed(param["seed"] & 0xffff_ffff)
torch.cuda.manual_seed_all(param["seed"] & 0xffff_ffff)
elif seed >= 0:
torch.manual_seed(seed & 0xffff_ffff)
torch.cuda.manual_seed_all(seed & 0xffff_ffff)
mel_pred = self.forward_model(batch)
if save_mel:
result.append({
'offset': param.get('offset', 0.),
'mel': mel_pred.cpu(),
'f0': batch['f0'].cpu()
})
else:
waveform_pred = self.run_vocoder(mel_pred, f0=batch['f0'])[0].cpu().numpy()
silent_length = round(param.get('offset', 0) * hparams['audio_sample_rate']) - current_length
if silent_length >= 0:
result = np.append(result, np.zeros(silent_length))
result = np.append(result, waveform_pred)
else:
result = cross_fade(result, waveform_pred, current_length + silent_length)
current_length = current_length + silent_length + waveform_pred.shape[0]
if num_runs > 1:
filename = f'{title}-{str(i).zfill(3)}{suffix}'
else:
filename = title + suffix
save_path = out_dir / filename
if save_mel:
print(f'| save mel: {save_path}')
torch.save(result, save_path)
else:
print(f'| save audio: {save_path}')
save_wav(result, save_path, hparams['audio_sample_rate'])
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