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# import gradio as gr
# gr.Interface.load("models/ulysses115/pmvoice").launch()
import argparse
import json
import os
import re
import tempfile
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
import gradio.utils as gr_utils
import gradio.processing_utils as gr_processing_utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence, _clean_text
from mel_processing import spectrogram_torch
limitation = False#os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
def audio_postprocess(self, y):
if y is None:
return None
self.temp_dir = "./"
if gr_utils.validate_url(y):
file = gr_processing_utils.download_to_file(y, dir=self.temp_dir)
elif isinstance(y, tuple):
sample_rate, data = y
file = tempfile.NamedTemporaryFile(
suffix=".wav", dir=self.temp_dir, delete=False
)
gr_processing_utils.audio_to_file(sample_rate, data, file.name)
else:
file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir)
return gr_processing_utils.encode_url_or_file_to_base64(file.name)
gr.Audio.postprocess = audio_postprocess
def get_text(text, hps, is_symbol):
text_norm = text_to_sequence(text, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
def create_tts_fn(model, hps, speaker_ids):
def tts_fn(text, speaker, speed, is_symbol):
if limitation:
text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
max_len = 150
if is_symbol:
max_len *= 3
if text_len > max_len:
return "Error: Text is too long", None
speaker_id = speaker_ids[speaker]
stn_tst = get_text(text, hps, is_symbol)
with no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
sid = LongTensor([speaker_id]).to(device)
audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
del stn_tst, x_tst, x_tst_lengths, sid
return "Success", (hps.data.sampling_rate, audio)
return tts_fn
def create_to_symbol_fn(hps):
def to_symbol_fn(is_symbol_input, input_text, temp_text):
return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
else (temp_text, temp_text)
return to_symbol_fn
download_audio_js = """
() =>{{
let root = document.querySelector("body > gradio-app");
if (root.shadowRoot != null)
root = root.shadowRoot;
let audio = root.querySelector("#{audio_id}").querySelector("audio");
if (audio == undefined)
return;
audio = audio.src;
let oA = document.createElement("a");
oA.download = Math.floor(Math.random()*100000000)+'.wav';
oA.href = audio;
document.body.appendChild(oA);
oA.click();
oA.remove();
}}
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
device = torch.device(args.device)
models_tts = []
with open("save_model/info.json", "r", encoding="utf-8") as f:
models_info = json.load(f)
for i, info in models_info.items():
name = info["title"]
author = info["author"]
lang = info["lang"]
example = info["example"]
config_path = f"config.json"
model_path = f"G_1434000.pth"
cover = info["cover"]
cover_path = cover
hps = utils.get_hparams_from_file(config_path)
model = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model)
utils.load_checkpoint(model_path, model, None)
model.eval().to(device)
speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
t = info["type"]
if t == "vits":
models_tts.append((name, author, cover_path, speakers, lang, example,
symbols, create_tts_fn(model, hps, speaker_ids),
create_to_symbol_fn(hps)))
app = gr.Blocks()
with app:
for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn,
to_symbol_fn) in enumerate(models_tts):
with gr.TabItem(f"model{i}"):
with gr.Column():
tts_input1 = gr.TextArea(label="Text", value="你好,旅行者!我是派蒙~有什么可以帮助你的吗?",
elem_id=f"tts-input{i}")
tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
type="index", value=speakers[0])
tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1)
with gr.Accordion(label="Advanced Options", open=False):
temp_text_var = gr.Variable()
symbol_input = gr.Checkbox(value=False, label="Symbol input")
symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1],
samples=[[x] for x in symbols],
elem_id=f"symbol-list{i}")
symbol_list_json = gr.Json(value=symbols, visible=False)
tts_submit = gr.Button("Generate", variant="primary")
tts_output1 = gr.Textbox(label="Output Message")
tts_output2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio{i}")
download = gr.Button("Download Audio")
download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}"))
tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input],
[tts_output1, tts_output2])
symbol_input.change(to_symbol_fn,
[symbol_input, tts_input1, temp_text_var],
[tts_input1, temp_text_var])
symbol_list.click(None, [symbol_list, symbol_list_json], [],
_js=f"""
(i,symbols) => {{
let root = document.querySelector("body > gradio-app");
if (root.shadowRoot != null)
root = root.shadowRoot;
let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
let startPos = text_input.selectionStart;
let endPos = text_input.selectionEnd;
let oldTxt = text_input.value;
let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
text_input.value = result;
let x = window.scrollX, y = window.scrollY;
text_input.focus();
text_input.selectionStart = startPos + symbols[i].length;
text_input.selectionEnd = startPos + symbols[i].length;
text_input.blur();
window.scrollTo(x, y);
return [];
}}""")
app.queue(concurrency_count=3).launch(show_api=True, share=args.share)