init app

app.py

@@ -0,0 +1,81 @@
+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 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")
+
+net_g = SynthesizerTrn(
+    len(symbols),
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model)
+import numpy as np
+
+hubert = torch.hub.load("bshall/hubert:main", "hubert_soft")
+
+_ = utils.load_checkpoint("G_88000.pth", net_g, None)
+
+def vc_fn(input_audio):
+    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)
+    source = torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0)
+    print(source.shape)
+    with torch.inference_mode():
+        units = hubert.units(source)
+
+    stn_tst = torch.FloatTensor(units.squeeze(0))
+    with torch.no_grad():
+        x_tst = stn_tst.unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+        audio = net_g.infer(x_tst, x_tst_lengths, 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_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_output1, vc_output2])
+
+    app.launch()