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import json
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
from models import SynthesizerTrn
from text import text_to_sequence
from mel_processing import spectrogram_torch
def get_text(text, hps):
text_norm = text_to_sequence(text, hps.symbols, 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):
if len(text) > 150:
return "Error: Text is too long", None
speaker_id = speaker_ids[speaker]
stn_tst = get_text(text, hps)
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()
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, input_audio):
if input_audio is None:
return "You need to upload an audio", None
sampling_rate, audio = input_audio
duration = audio.shape[0] / sampling_rate
if duration > 30:
return "Error: Audio is too long", None
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)
y = torch.FloatTensor(audio)
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)
spec_lengths = LongTensor([spec.size(-1)])
sid_src = LongTensor([original_speaker_id])
sid_tgt = LongTensor([target_speaker_id])
with no_grad():
audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
0, 0].data.cpu().float().numpy()
return "Success", (hps.data.sampling_rate, audio)
return vc_fn
if __name__ == '__main__':
models = []
with open("saved_model/names.json", "r", encoding="utf-8") as f:
models_names = json.load(f)
for i, models_name in models_names.items():
config_path = f"saved_model/{i}/config.json"
model_path = f"saved_model/{i}/model.pth"
cover_path = f"saved_model/{i}/cover.jpg"
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()
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"]
models.append((models_name, cover_path, speakers,
create_tts_fn(model, hps, speaker_ids), create_vc_fn(model, hps, speaker_ids)))
app = gr.Blocks()
with app:
gr.Markdown("# Moe Japanese TTS And Voice Conversion Using VITS Model\n\n"
"\n\n"
"unofficial demo for \n\n"
"- [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)\n"
"- [https://github.com/Francis-Komizu/VITS](https://github.com/Francis-Komizu/VITS)"
)
with gr.Tabs():
with gr.TabItem("TTS"):
with gr.Tabs():
for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models):
with gr.TabItem(f"model{i}"):
with gr.Column():
gr.Markdown(f"## {models_name}\n\n"
f"")
tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="γγγ«γ‘γ―γ")
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)
tts_submit = gr.Button("Generate", variant="primary")
tts_output1 = gr.Textbox(label="Output Message")
tts_output2 = gr.Audio(label="Output Audio")
tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3],
[tts_output1, tts_output2])
with gr.TabItem("Voice Conversion"):
with gr.Tabs():
for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models):
with gr.TabItem(f"model{i}"):
gr.Markdown(f"## {models_name}\n\n"
f"")
vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index",
value=speakers[0])
vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index",
value=speakers[1])
vc_input3 = gr.Audio(label="Input Audio (30s limitation)")
vc_submit = gr.Button("Convert", variant="primary")
vc_output1 = gr.Textbox(label="Output Message")
vc_output2 = gr.Audio(label="Output Audio")
vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2])
app.launch()
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