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()