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# Copyright 2024 LY Corporation
# LY Corporation licenses this file to you under the Apache License,
# version 2.0 (the "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at:
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from pathlib import Path
import hydra
import pandas as pd
import torch
import torchaudio
from hydra.utils import instantiate
from omegaconf import OmegaConf
from promptttspp.utils import seed_everything
from promptttspp.utils.model import remove_weight_norm_
from promptttspp.vocoders import F0AwareBigVGAN
from scipy import signal
from tqdm import tqdm
def lowpass_filter(x, fs=100, cutoff=20, N=5):
"""Lowpass filter
Args:
x (array): input signal
fs (int): sampling rate
cutoff (int): cutoff frequency
Returns:
array: filtered signal
"""
nyquist = fs // 2
norm_cutoff = cutoff / nyquist
Wn = [norm_cutoff]
x_len = x.shape[-1]
b, a = signal.butter(N, Wn, "lowpass")
if x_len <= max(len(a), len(b)) * (N // 2 + 1):
# NOTE: input signal is too short
return x
# NOTE: use zero-phase filter
if isinstance(x, torch.Tensor):
from torchaudio.functional import filtfilt
a = torch.from_numpy(a).float().to(x.device)
b = torch.from_numpy(b).float().to(x.device)
y = filtfilt(x, a, b, clamp=False)
else:
y = signal.filtfilt(b, a, x)
return y
def read_prompt_candidate(filepath):
df_style_prompt = pd.read_csv(
filepath, header=None, sep="|", names=["style_key", "prompt"]
)
style_prompt_dict = {}
for _, row in df_style_prompt.iterrows():
style_key, style_prompt = row.iloc[0], row.iloc[1]
assert isinstance(style_prompt, str)
style_prompt_dict[style_key] = list(
map(lambda s: s.lower().strip(), style_prompt.split(";"))
)
return style_prompt_dict
def read_spk_prompt_candidate(filepath):
df = pd.read_csv(filepath, sep="|", header=None, names=["spk", "words"])
df["words"] = df["words"].map(lambda x: x.split(","))
# dict(key: spk_id, value: words)
spk_prompt_cand_dict = df.set_index("spk")["words"].to_dict()
return spk_prompt_cand_dict
def add_spk_prompt(style_prompt, words):
spk_prompt = f"The speaker identity can be described as {words}."
prompt = f"{style_prompt}. {spk_prompt}"
return prompt
@hydra.main(version_base=None, config_path="conf/", config_name="synthesize")
def main(cfg):
data_root = Path(cfg.path.data_root)
output_dir = Path(cfg.output_dir)
seed_everything(cfg.train.seed)
prompt_candidate = read_prompt_candidate(cfg.path.prompt_candidate_file)
spk_prompt_candidate = read_spk_prompt_candidate(cfg.path.spk_prompt_candidate_file)
mel_stats = OmegaConf.load(f"{cfg.path.mel_dir}/stats.yaml")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = instantiate(cfg.model)
model.load_state_dict(torch.load(cfg.ckpt_path, map_location="cpu")["model"])
model = model.to(device).eval()
model.apply(remove_weight_norm_)
to_mel = instantiate(cfg.transforms).to(device).eval()
vocoder = instantiate(cfg.vocoder)
vocoder.load_state_dict(
torch.load(cfg.vocoder_ckpt_path, map_location="cpu")["generator"]
)
vocoder = vocoder.to(device).eval()
vocoder.apply(remove_weight_norm_)
use_col = [
"spk_id",
"item_name",
"gender",
"pitch",
"speaking_speed",
"energy",
"style_prompt",
"style_prompt_key",
"seq",
]
df = pd.read_csv(cfg.label_file, usecols=use_col)
data = df[use_col].values.tolist()
for row in tqdm(data, total=len(data)):
spk = row[0]
utt_id = row[1]
seq = row[-1]
style_prompt_key = row[-2]
style_prompt = prompt_candidate[style_prompt_key][0]
if spk in spk_prompt_candidate:
spk_prompt = spk_prompt_candidate[spk]
words = ", ".join(spk_prompt)
if cfg.use_spk_prompt:
prompt = add_spk_prompt(style_prompt, words)
else:
prompt = style_prompt
else:
prompt = style_prompt
spk_dir = output_dir / str(spk)
ref_dir = spk_dir / "ref"
ref_mel_dir = ref_dir / "mel"
ref_plot_dir = ref_dir / "plot"
ref_wav_dir = ref_dir / "wav"
prompt_dir = spk_dir / "prompt"
prompt_mel_dir = prompt_dir / "mel"
prompt_plot_dir = prompt_dir / "plot"
prompt_wav_dir = prompt_dir / "wav"
dirs = [
ref_mel_dir,
ref_plot_dir,
ref_wav_dir,
prompt_mel_dir,
prompt_plot_dir,
prompt_wav_dir,
]
[d.mkdir(parents=True, exist_ok=True) for d in dirs]
label = torch.LongTensor([int(s) for s in seq.split()])[None, :]
label = label.to(device)
wav, _ = torchaudio.load(data_root / f"{spk}/wav24k/{utt_id}.wav")
wav = wav.to(device)
mel = to_mel(wav)
mel = (mel - mel_stats["mean"]) / mel_stats["std"]
is_f0_aware_vocoder = isinstance(vocoder, F0AwareBigVGAN)
with torch.no_grad():
if is_f0_aware_vocoder:
dec, log_cf0, vuv = model.infer(
label, reference_mel=mel, return_f0=True
)
# NOTE: hard code for 10ms frame shift
modfs = int(1.0 / (10 * 0.001))
log_cf0 = lowpass_filter(log_cf0, modfs, cutoff=20)
f0 = log_cf0.exp()
f0[vuv < 0.5] = 0
dec = dec * mel_stats["std"] + mel_stats["mean"]
o_ref = vocoder(dec, f0).squeeze(1).cpu()
else:
dec = model.infer(label, reference_mel=mel)
dec = dec * mel_stats["std"] + mel_stats["mean"]
o_ref = vocoder(dec).squeeze(1).cpu()
torchaudio.save(ref_wav_dir / f"{utt_id}.wav", o_ref, to_mel.sample_rate)
with torch.no_grad():
style_prompt = [prompt]
if is_f0_aware_vocoder:
dec, log_cf0, vuv = model.infer(
label, style_prompt=style_prompt, return_f0=True
)
# NOTE: hard code for 10ms frame shift
modfs = int(1.0 / (10 * 0.001))
log_cf0 = lowpass_filter(log_cf0, modfs, cutoff=20)
f0 = log_cf0.exp()
f0[vuv < 0.5] = 0
dec = dec * mel_stats["std"] + mel_stats["mean"]
o_prompt = vocoder(dec, f0).squeeze(1).cpu()
else:
dec = model.infer(label, style_prompt=style_prompt)
dec = dec * mel_stats["std"] + mel_stats["mean"]
o_prompt = vocoder(dec).squeeze(1).cpu()
torchaudio.save(prompt_wav_dir / f"{utt_id}.wav", o_prompt, to_mel.sample_rate)
with open(output_dir / "finish", "w") as f:
f.write("finish")
if __name__ == "__main__":
main()
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