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demo_page.py
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demo_page.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.
import streamlit as st
import os, glob
import numpy as np
from yacs import config as CONFIG
import torch
import re
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from config.joint.config import Config
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_modified.simbert import StyleEncoder
from transformers import AutoTokenizer
import base64
from pathlib import Path
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MAX_WAV_VALUE = 32768.0
config = Config()
def create_download_link():
pdf_path = Path("EmotiVoice_UserAgreement_易魔声用户协议.pdf")
base64_pdf = base64.b64encode(pdf_path.read_bytes()).decode("utf-8") # val looks like b'...'
return f'<a href="data:application/octet-stream;base64,{base64_pdf}" download="EmotiVoice_UserAgreement_易魔声用户协议.pdf.pdf">EmotiVoice_UserAgreement_易魔声用户协议.pdf</a>'
html=create_download_link()
st.set_page_config(
page_title="demo page",
page_icon="📕",
)
st.write("# Text-To-Speech")
st.markdown(f"""
### How to use:
- Simply select a **Speaker ID**, type in the **text** you want to convert and the emotion **Prompt**, like a single word or even a sentence. Then click on the **Synthesize** button below to start voice synthesis.
- You can download the audio by clicking on the vertical three points next to the displayed audio widget.
- For more information on **'Speaker ID'**, please consult the [EmotiVoice voice wiki page](https://github.com/netease-youdao/EmotiVoice/tree/main/data/youdao/text)
- This interactive demo page is provided under the {html} file. The audio is synthesized by AI. 音频由AI合成，仅供参考。
""", unsafe_allow_html=True)
def scan_checkpoint(cp_dir, prefix, c=8):
pattern = os.path.join(cp_dir, prefix + '?'*c)
cp_list = glob.glob(pattern)
if len(cp_list) == 0:
return None
return sorted(cp_list)[-1]
@st.cache_resource
def get_models():
am_checkpoint_path = scan_checkpoint(f'{config.output_directory}/prompt_tts_open_source_joint/ckpt', 'g_')
style_encoder_checkpoint_path = scan_checkpoint(f'{config.output_directory}/style_encoder/ckpt', 'checkpoint_', 6)#f'{config.output_directory}/style_encoder/ckpt/checkpoint_163431'
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(style_encoder_checkpoint_path, 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(am_checkpoint_path, map_location=DEVICE)
generator.load_state_dict(model_CKPT['generator'])
generator.eval()
tokenizer = AutoTokenizer.from_pretrained(config.bert_path)
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())}
return (style_encoder, generator, tokenizer, token2id, speaker2id)
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 tts(name, text, prompt, content, speaker, models):
(style_encoder, generator, tokenizer, token2id, speaker2id)=models
style_embedding = get_style_embedding(prompt, tokenizer, style_encoder)
content_embedding = get_style_embedding(content, tokenizer, style_encoder)
speaker = speaker2id[speaker]
text_int = [token2id[ph] for ph in text.split()]
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')
return audio
speakers = config.speakers
models = get_models()
lexicon = read_lexicon(f"{ROOT_DIR}/lexicon/librispeech-lexicon.txt")
g2p = G2p()
def new_line(i):
col1, col2, col3, col4 = st.columns([1.5, 1.5, 3.5, 1.3])
with col1:
speaker=st.selectbox("Speaker ID (说话人)", speakers, key=f"{i}_speaker")
with col2:
prompt=st.text_input("Prompt (开心/悲伤)", "", key=f"{i}_prompt")
with col3:
content=st.text_input("Text to be synthesized into speech (合成文本)", "合成文本", key=f"{i}_text")
with col4:
lang=st.selectbox("Language (语言)", ["zh_us"], key=f"{i}_lang")
flag = st.button(f"Synthesize (合成)", key=f"{i}_button1")
if flag:
text = g2p_cn_en(content, g2p, lexicon)
path = tts(i, text, prompt, content, speaker, models)
st.audio(path, sample_rate=config.sampling_rate)
new_line(0)