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inference_am_vocoder_exp.py
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inference_am_vocoder_exp.py
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# Copyright 2023, YOUDAO
#
# Licensed 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
#
# http://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 models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transformers import AutoTokenizer
import os, sys, warnings, torch, glob, argparse
import numpy as np
from models.hifigan.get_vocoder import MAX_WAV_VALUE
import soundfile as sf
from yacs import config as CONFIG
from tqdm import tqdm
def get_style_embedding(prompt, tokenizer, style_encoder):
prompt = tokenizer([prompt], return_tensors="pt")
input_ids = prompt["input_ids"]
token_type_ids = prompt["token_type_ids"]
attention_mask = prompt["attention_mask"]
with torch.no_grad():
output = style_encoder(
input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
)
style_embedding = output["pooled_output"].cpu().squeeze().numpy()
return style_embedding
def main(args, config):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
root_path = os.path.join(config.output_directory)
ckpt_path = os.path.join(root_path, "ckpt")
files = os.listdir(ckpt_path)
for file in files:
if args.checkpoint:
if file != args.checkpoint:
continue
checkpoint_path = os.path.join(ckpt_path, file)
with open(config.model_config_path, 'r') as fin:
conf = CONFIG.load_cfg(fin)
conf.n_vocab = config.n_symbols
conf.n_speaker = config.speaker_n_labels
style_encoder = StyleEncoder(config)
model_CKPT = torch.load(config.style_encoder_ckpt, map_location="cpu")
model_ckpt = {}
for key, value in model_CKPT['model'].items():
new_key = key[7:]
model_ckpt[new_key] = value
style_encoder.load_state_dict(model_ckpt, strict=False)
generator = JETSGenerator(conf).to(device)
model_CKPT = torch.load(checkpoint_path, map_location=device)
generator.load_state_dict(model_CKPT['generator'])
generator.eval()
with open(config.token_list_path, 'r') as f:
token2id = {t.strip():idx for idx, t, in enumerate(f.readlines())}
with open(config.speaker2id_path, encoding='utf-8') as f:
speaker2id = {t.strip():idx for idx, t in enumerate(f.readlines())}
tokenizer = AutoTokenizer.from_pretrained(config.bert_path)
text_path = args.test_file
if os.path.exists(root_path + "/test_audio/audio/" +f"{file}/"):
r = glob.glob(root_path + "/test_audio/audio/" +f"{file}/*")
for j in r:
os.remove(j)
texts = []
prompts = []
speakers = []
contents = []
with open(text_path, "r") as f:
for line in f:
line = line.strip().split("|")
speakers.append(line[0])
prompts.append(line[1])
texts.append(line[2].split())
contents.append(line[3])
for i, (speaker, prompt, text, content) in enumerate(tqdm(zip(speakers, prompts, texts, contents))):
style_embedding = get_style_embedding(prompt, tokenizer, style_encoder)
content_embedding = get_style_embedding(content, tokenizer, style_encoder)
if speaker not in speaker2id:
continue
speaker = speaker2id[speaker]
text_int = [token2id[ph] for ph in text]
sequence = torch.from_numpy(np.array(text_int)).to(device).long().unsqueeze(0)
sequence_len = torch.from_numpy(np.array([len(text_int)])).to(device)
style_embedding = torch.from_numpy(style_embedding).to(device).unsqueeze(0)
content_embedding = torch.from_numpy(content_embedding).to(device).unsqueeze(0)
speaker = torch.from_numpy(np.array([speaker])).to(device)
with torch.no_grad():
infer_output = generator(
inputs_ling=sequence,
inputs_style_embedding=style_embedding,
input_lengths=sequence_len,
inputs_content_embedding=content_embedding,
inputs_speaker=speaker,
alpha=1.0
)
audio = infer_output["wav_predictions"].squeeze()* MAX_WAV_VALUE
audio = audio.cpu().numpy().astype('int16')
if not os.path.exists(root_path + "/test_audio/audio/" +f"{file}/"):
os.makedirs(root_path + "/test_audio/audio/" +f"{file}/", exist_ok=True)
sf.write(file=root_path + "/test_audio/audio/" +f"{file}/{i+1}.wav", data=audio, samplerate=config.sampling_rate) #h.sampling_rate
if __name__ == '__main__':
print("run!")
p = argparse.ArgumentParser()
p.add_argument("-c", "--config_folder", type=str, required=True)
p.add_argument("--checkpoint", type=str, required=False, default='', help='inference specific checkpoint, e.g --checkpoint checkpoint_230000')
p.add_argument('-t', '--test_file', type=str, required=True, help='the absolute path of test file that is going to inference')
args = p.parse_args()
##################################################
sys.path.append(os.path.dirname(os.path.abspath("__file__")) + "/" + args.config_folder)
from config import Config
config = Config()
##################################################
main(args, config)