Upload app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import sys
+from vinorm import TTSnorm
+from utils_audio import convert_to_wav
+
+sys.path.append("vits")
+import commons
+import utils
+from models import SynthesizerTrn
+from text.symbols import symbols
+from text import text_to_sequence
+from scipy.io.wavfile import write
+import logging
+
+numba_logger = logging.getLogger("numba")
+numba_logger.setLevel(logging.WARNING)
+
+
+from resemblyzer import preprocess_wav, VoiceEncoder
+
+
+device = "cpu"
+
+
+def get_text(texts, hps):
+    text_norm_list = []
+    for text in texts.split(","):
+        chunk_strings = []
+        chunk_len = 30
+        for i in range(0, len(text.split()), chunk_len):
+            chunk = " ".join(text.split()[i : i + chunk_len])
+            chunk_strings.append(chunk)
+        for chunk_string in chunk_strings:
+            text_norm = text_to_sequence(chunk_string, hps.data.text_cleaners)
+            if hps.data.add_blank:
+                text_norm = commons.intersperse(text_norm, 0)
+            text_norm_list.append(torch.LongTensor(text_norm))
+    return text_norm_list
+
+
+def get_speaker_embedding(path):
+    encoder = VoiceEncoder(device="cpu")
+    path = convert_to_wav(path)
+    wav = preprocess_wav(path)
+    embed = encoder.embed_utterance(wav)
+    return embed
+
+
+class VoiceClone:
+    def __init__(self, checkpoint_path):
+        hps = utils.get_hparams_from_file("./vits/configs/vivos.json")
+        self.net_g = SynthesizerTrn(
+            len(symbols),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model
+        ).to(device)
+        _ = self.net_g.eval()
+
+        _ = utils.load_checkpoint(checkpoint_path, self.net_g, None)
+
+        self.hps = hps
+
+    def infer(self, text, ref_audio):
+        text_norm = TTSnorm(text)
+        stn_tst_list = get_text(text_norm, self.hps)
+        with torch.no_grad():
+            audios = []
+            for stn_tst in stn_tst_list:
+                x_tst = stn_tst.to(device).unsqueeze(0)
+                x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
+
+                speaker_embedding = get_speaker_embedding(ref_audio)
+                speaker_embedding = (
+                    torch.FloatTensor(torch.from_numpy(speaker_embedding))
+                    .unsqueeze(0)
+                    .to(device)
+                )
+
+                audio = self.net_g.infer(
+                    x_tst,
+                    x_tst_lengths,
+                    speaker_embedding=speaker_embedding,
+                    noise_scale=0.667,
+                    noise_scale_w=0.8,
+                    length_scale=1,
+                )
+
+                audio = audio[0][0, 0].data.cpu().float().numpy()
+                audios.append(audio)
+                print(audio.shape)
+
+            audios = np.concatenate(audios, axis=0)
+            write(ref_audio.replace(".wav", "_clone.wav"), 22050, audios)
+        return ref_audio.replace(".wav", "_clone.wav"), text_norm
+
+
+# object = VoiceClone("vits/logs/vivos/G_7700000.pth")
+object = VoiceClone("vits/logs/vivos/G_150000.pth")
+
+
+def clonevoice(text: str, speaker_wav, file_upload, language: str):
+    speaker_source = ""
+    if speaker_wav is not None:
+        speaker_source = speaker_wav
+    elif file_upload is not None:
+        speaker_source = file_upload
+    else:
+        speaker_source = "vits/audio/sontung.wav"
+
+    print(speaker_source)
+
+    outfile, text_norm = object.infer(text, speaker_source)
+
+    return [outfile, text_norm]
+
+
+inputs = [
+    gr.Textbox(
+        label="Input",
+        value="muốn ngồi ở một vị trí không ai ngồi được thì phải chịu cảm giác không ai chịu được",
+        max_lines=3,
+    ),
+    gr.Audio(label="Speaker Wav", source="microphone", type="filepath"),
+    gr.Audio(label="Speaker Wav", source="upload", type="filepath"),
+    gr.Radio(label="Language", choices=["Vietnamese"], value="en"),
+]
+outputs = [gr.Audio(label="Output"), gr.TextArea()]
+
+demo = gr.Interface(fn=clonevoice, inputs=inputs, outputs=outputs)
+
+demo.launch(debug=True)