azalea/VITS-fast-fine-tuning
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Plachta
2023-02-15 14:31:58 +08:00
parent 1418b66b28
commit 1a16349cb1
2 changed files with 10 additions and 9 deletions
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VC_inference.py
+10 -8
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@@ -2,13 +2,13 @@ import os
import numpy as np import numpy as np
import torch import torch
from torch import no_grad, LongTensor from torch import no_grad, LongTensor
import librosa
import argparse import argparse
from mel_processing import spectrogram_torch from mel_processing import spectrogram_torch
import utils import utils
from models_infer import SynthesizerTrn from models_infer import SynthesizerTrn
import gradio as gr import gradio as gr
import torchaudio import torchaudio
import webbrowser
device = "cuda:0" if torch.cuda.is_available() else "cpu" device = "cuda:0" if torch.cuda.is_available() else "cpu"
def create_vc_fn(model, hps, speaker_ids): def create_vc_fn(model, hps, speaker_ids):
@@ -20,14 +20,13 @@ def create_vc_fn(model, hps, speaker_ids):
original_speaker_id = speaker_ids[original_speaker] original_speaker_id = speaker_ids[original_speaker]
target_speaker_id = speaker_ids[target_speaker] target_speaker_id = speaker_ids[target_speaker]
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) audio = torch.tensor(audio).type(torch.float32)
if len(audio.shape) > 1: audio = audio.squeeze().unsqueeze(0)
audio = librosa.to_mono(audio.transpose(1, 0)) audio = audio / max(-audio.min(), audio.max()) / 0.99
if sampling_rate != hps.data.sampling_rate: if sampling_rate != hps.data.sampling_rate:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) audio = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=22050)(audio)
with no_grad(): with no_grad():
y = torch.FloatTensor(audio) y = torch.FloatTensor(audio)
y = y.unsqueeze(0)
y = y / max(-y.min(), y.max()) / 0.99 y = y / max(-y.min(), y.max()) / 0.99
if denoise: if denoise:
torchaudio.save("infer.wav", y.cpu(), 22050, channels_first=True) torchaudio.save("infer.wav", y.cpu(), 22050, channels_first=True)
@@ -52,10 +51,11 @@ def create_vc_fn(model, hps, speaker_ids):
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model") 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="make link public (used in colab)") parser.add_argument("--share", default=False, help="make link public (used in colab)")
args = parser.parse_args() args = parser.parse_args()
hps = utils.get_hparams_from_file("./configs/finetune_speaker.json") hps = utils.get_hparams_from_file(args.config_dir)
net_g = SynthesizerTrn( net_g = SynthesizerTrn(
@@ -80,11 +80,13 @@ if __name__ == "__main__":
upload_audio = gr.Audio(label="or upload audio here", source="upload") upload_audio = gr.Audio(label="or upload audio here", source="upload")
source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker") source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker")
target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker") target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
denoise_checkbox = gr.Checkbox(label="denoise using demucs", value=True) denoise_checkbox = gr.Checkbox(label="denoise using demucs", value=False)
with gr.Column(): with gr.Column():
message_box = gr.Textbox(label="Message") message_box = gr.Textbox(label="Message")
converted_audio = gr.Audio(label='converted audio') converted_audio = gr.Audio(label='converted audio')
btn = gr.Button("Convert!") btn = gr.Button("Convert!")
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio, denoise_checkbox], btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio, denoise_checkbox],
outputs=[message_box, converted_audio]) outputs=[message_box, converted_audio])
webbrowser.open("http://127.0.0.1:7860")
app.launch(share=args.share) app.launch(share=args.share)
requirements_infer.txt
-1
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@@ -3,7 +3,6 @@ librosa
numpy numpy
scipy scipy
torch torch
torchvision
torchaudio torchaudio
unidecode unidecode
protobuf protobuf