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import argparse
import json
import os
import re
import tempfile
from pathlib import Path
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 json
import gradio.processing_utils as gr_processing_utils
from models import SynthesizerTrn
from text import text_to_sequence, _clean_text
# from mel_processing import spectrogram_torch
# import sounddevice as sd
# from scipy.io.wavfile import write
# import scikits.audiolab
# import soundfile as sf
import scipy.io.wavfile as wf
import base64
limitation = False
device = torch.device('cpu')
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();
}}
"""
# download = gr.Button("Download Audio")
tts_input1 = gr.TextArea(label="inputText", value="あなたと一緒にいると、とても興奮します", elem_id=f"tts-input{0}")
tts_output2 = gr.Audio(label="outputAudio", elem_id=f"tts-audio{0}")
def get_text(text, hps, is_symbol):
text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = 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
def main(input):
models_tts = []
models_vc = []
models_soft_vc = []
device = torch.device("cpu")
global result
with open("saved_model/info.json", "r", encoding="utf-8") as f:
models_info = json.load(f)
for i, info in models_info.items():
if int(i) == 0:
name = info["title"]
author = info["author"]
lang = info["lang"]
example = info["example"]
config_path = f"saved_model/{i}/config.json"
model_path = f"saved_model/{i}/model.pth"
cover = info["cover"]
cover_path = f"saved_model/{i}/{cover}" if cover else None
hps = utils.get_hparams_from_file(config_path)
model = SynthesizerTrn(
len(hps.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**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"]
# input_text = get_text("ヨスガノソラ", hps, True)
print(speaker_ids[0])
vtts = create_tts_fn(model, hps, speaker_ids)
symbol = create_to_symbol_fn(hps)
result = vtts(input, speaker_ids[0], 1, False)
# wf.write('anime_girl3.wav', result[1][0], result[1][1])
# print(type(result[1][0]), result[1][0])
# download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{0}"))
# return result[1][0], result[1][1]
wf.write(os.path.join(os.path.dirname(__file__), 'audio/animegirl.wav'), result[1][0], result[1][1])
return """
<audio src={}></audio>
""".format(os.path.join(os.path.dirname(__file__), 'audio/animegirl.wav'))
# return str(result[1][1])
# base64.b64encode(open("animegirl.wav").read())
# return str(json.dumps(result[1][1]))
# result[1][1]
print(models_tts)
demo = gr.Interface(fn=main, inputs="text", outputs="html")
# outputs=gr.outputs.Textbox(label="outputAudio"))
if __name__ == "__main__":
demo.launch(debug=True)
# main(input = "あなたと一緒にいると、とても興奮します")
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