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| import gradio as gr | |
| import os | |
| os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..') | |
| import logging | |
| numba_logger = logging.getLogger('numba') | |
| numba_logger.setLevel(logging.WARNING) | |
| import librosa | |
| import torch | |
| import torchcrepe | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence | |
| def resize2d(source, target_len): | |
| source[source<0.001] = np.nan | |
| target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source) | |
| return np.nan_to_num(target) | |
| def convert_wav_22050_to_f0(audio): | |
| tmp = torchcrepe.predict(audio=audio, fmin=50, fmax=550, | |
| sample_rate=22050, model='full', | |
| batch_size=2048).numpy()[0] | |
| f0 = np.zeros_like(tmp) | |
| f0[tmp > 0] = tmp[tmp > 0] | |
| return f0 | |
| def get_text(text, hps): | |
| 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) | |
| print(text_norm.shape) | |
| return text_norm | |
| hps = utils.get_hparams_from_file("configs/ljs_base.json") | |
| hps_ms = utils.get_hparams_from_file("configs/vctk_base.json") | |
| net_g_ms = SynthesizerTrn( | |
| len(symbols), | |
| hps_ms.data.filter_length // 2 + 1, | |
| hps_ms.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps_ms.data.n_speakers, | |
| **hps_ms.model) | |
| import numpy as np | |
| hubert = torch.hub.load("bshall/hubert:main", "hubert_soft") | |
| _ = utils.load_checkpoint("G_312000.pth", net_g_ms, None) | |
| def vc_fn(input_audio,vc_transform): | |
| if input_audio is None: | |
| return "You need to upload an audio", None | |
| sampling_rate, audio = input_audio | |
| # print(audio.shape,sampling_rate) | |
| duration = audio.shape[0] / sampling_rate | |
| if duration > 30: | |
| return "Error: Audio is too long", None | |
| 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 != 16000: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) | |
| audio22050 = librosa.resample(audio, orig_sr=16000, target_sr=22050) | |
| f0 = convert_wav_22050_to_f0(audio22050) | |
| source = torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0) | |
| print(source.shape) | |
| with torch.inference_mode(): | |
| units = hubert.units(source) | |
| soft = units.squeeze(0).numpy() | |
| print(sampling_rate) | |
| f0 = resize2d(f0, len(soft[:, 0])) * vc_transform | |
| soft[:, 0] = f0 / 10 | |
| sid = torch.LongTensor([0]) | |
| stn_tst = torch.FloatTensor(soft) | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| audio = net_g_ms.infer(x_tst, x_tst_lengths,sid=sid, noise_scale=0.1, noise_scale_w=0.1, length_scale=1)[0][ | |
| 0, 0].data.float().numpy() | |
| return "Success", (hps.data.sampling_rate, audio) | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tabs(): | |
| with gr.TabItem("Basic"): | |
| vc_input3 = gr.Audio(label="Input Audio (30s limitation)") | |
| vc_transform = gr.Number(label="transform",value=1.0) | |
| 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_input3,vc_transform], [vc_output1, vc_output2]) | |
| app.launch() |