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import gradio as gr |
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import os |
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os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..') |
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import logging |
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numba_logger = logging.getLogger('numba') |
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numba_logger.setLevel(logging.WARNING) |
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import librosa |
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import torch |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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def resize2d(source, target_len): |
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source[source<0.001] = np.nan |
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target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source) |
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return np.nan_to_num(target) |
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def convert_wav_22050_to_f0(audio): |
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tmp = torchcrepe.predict(audio=audio, fmin=50, fmax=550, |
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sample_rate=22050, model='full', |
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batch_size=2048).numpy()[0] |
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f0 = np.zeros_like(tmp) |
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f0[tmp > 0] = tmp[tmp > 0] |
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return f0 |
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def get_text(text, hps): |
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text_norm = text_to_sequence(text, hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = torch.LongTensor(text_norm) |
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print(text_norm.shape) |
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return text_norm |
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hps = utils.get_hparams_from_file("configs/ljs_base.json") |
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hps_ms = utils.get_hparams_from_file("configs/vctk_base.json") |
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net_g_ms = SynthesizerTrn( |
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len(symbols), |
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hps_ms.data.filter_length // 2 + 1, |
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hps_ms.train.segment_size // hps.data.hop_length, |
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n_speakers=hps_ms.data.n_speakers, |
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**hps_ms.model) |
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import numpy as np |
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hubert = torch.hub.load("bshall/hubert:main", "hubert_soft") |
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_ = utils.load_checkpoint("G_312000.pth", net_g_ms, None) |
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def vc_fn(input_audio,vc_transform): |
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if input_audio is None: |
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return "You need to upload an audio", None |
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sampling_rate, audio = input_audio |
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duration = audio.shape[0] / sampling_rate |
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if duration > 30: |
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return "Error: Audio is too long", None |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != 16000: |
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) |
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audio22050 = librosa.resample(audio, orig_sr=16000, target_sr=22050) |
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f0 = convert_wav_22050_to_f0(audio22050) |
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source = torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0) |
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print(source.shape) |
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with torch.inference_mode(): |
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units = hubert.units(source) |
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soft = units.squeeze(0).numpy() |
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print(sampling_rate) |
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f0 = resize2d(f0, len(soft[:, 0])) * vc_transform |
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soft[:, 0] = f0 / 10 |
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sid = torch.LongTensor([0]) |
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stn_tst = torch.FloatTensor(soft) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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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][ |
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0, 0].data.float().numpy() |
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return "Success", (hps.data.sampling_rate, audio) |
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app = gr.Blocks() |
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with app: |
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with gr.Tabs(): |
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with gr.TabItem("Basic"): |
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vc_input3 = gr.Audio(label="Input Audio (30s limitation)") |
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vc_transform = gr.Number(label="transform",value=1.0) |
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vc_submit = gr.Button("Convert", variant="primary") |
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vc_output1 = gr.Textbox(label="Output Message") |
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vc_output2 = gr.Audio(label="Output Audio") |
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vc_submit.click(vc_fn, [ vc_input3,vc_transform], [vc_output1, vc_output2]) |
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app.launch() |