initial commit

LICENSE

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+MIT License
+
+Copyright (c) 2021 Jaehyeon Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# soft-vc-singingvc
+

app.py

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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 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 = librosa.pyin(audio,
+                fmin=librosa.note_to_hz('C0'),
+                fmax=librosa.note_to_hz('C7'),
+                frame_length=1780)[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()