Spaces:
Runtime error
Runtime error
File size: 7,540 Bytes
df169bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import argparse
import json
import os
import re
import tempfile
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import numpy as np
import torch
from torch import no_grad, LongTensor
import argparse
import commons
from mel_processing import spectrogram_torch
import utils
from models import SynthesizerTrn
import gradio as gr
import gradio.utils as gr_utils
import gradio.processing_utils as gr_processing_utils
import models
import librosa
import webbrowser
from mel_processing import spectrogram_torch
import psutil
from datetime import datetime
from text import text_to_sequence, _clean_text
device = "cuda:0" if torch.cuda.is_available() else "cpu"
language_marks = {
"Japanese": "",
"日本語": "[JA]",
"简体中文": "[ZH]",
"English": "[EN]",
"Mix": "",
}
lang = ['日本語', '简体中文', 'English', 'Mix']
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, language, 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
if language is not None:
text = language_marks[language] + text + language_marks[language]
speaker_id = speaker_ids[speaker]
stn_tst = get_text(text, hps, is_symbol)
with no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = LongTensor([stn_tst.size(0)])
sid = LongTensor([speaker_id])
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_vc_fn(model, hps, speaker_ids):
def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
input_audio = record_audio if record_audio is not None else upload_audio
if input_audio is None:
return "You need to record or upload an audio", None
sampling_rate, audio = input_audio
original_speaker_id = speaker_ids[original_speaker]
target_speaker_id = speaker_ids[target_speaker]
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != hps.data.sampling_rate:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
with no_grad():
y = torch.FloatTensor(audio)
y = y / max(-y.min(), y.max()) / 0.99
y = y.to(device)
y = y.unsqueeze(0)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False).to(device)
spec_lengths = LongTensor([spec.size(-1)]).to(device)
sid_src = LongTensor([original_speaker_id]).to(device)
sid_tgt = LongTensor([target_speaker_id]).to(device)
audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
0, 0].data.cpu().float().numpy()
del y, spec, spec_lengths, sid_src, sid_tgt
return "Success", (hps.data.sampling_rate, audio)
return vc_fn
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_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
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
parser.add_argument("--share", default=False, help="share gradio app")
args = parser.parse_args()
hps = utils.get_hparams_from_file(args.config_dir)
net_g = 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).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(args.model_dir, net_g, None)
speaker_ids = hps.speakers
speakers = list(hps.speakers.keys())
tts_fn = create_tts_fn(net_g, hps, speaker_ids)
vc_fn = create_vc_fn(net_g, hps, speaker_ids)
app = gr.Blocks()
with app:
with gr.Tab("Text-to-Speech"):
with gr.Row():
with gr.Column():
textbox = gr.TextArea(label="Text",
placeholder="Type your sentence here",
value="こんにちわ。", elem_id=f"tts-input")
# select character
char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
label='速度 Speed')
with gr.Column():
text_output = gr.Textbox(label="Message")
audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
btn = gr.Button("Generate!")
btn.click(tts_fn,
inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
outputs=[text_output, audio_output])
with gr.Tab("Voice Conversion"):
gr.Markdown("""
录制或上传声音，并选择要转换的音色。
""")
with gr.Column():
record_audio = gr.Audio(label="record your voice", source="microphone")
upload_audio = gr.Audio(label="or upload audio here", source="upload")
source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker")
target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
with gr.Column():
message_box = gr.Textbox(label="Message")
converted_audio = gr.Audio(label='converted audio')
btn = gr.Button("Convert!")
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
outputs=[message_box, converted_audio])
webbrowser.open("http://127.0.0.1:7860")
app.launch(share=args.share)
|