upload files

2023-02-14 17:51:36 +08:00
parent 3467cb495a
commit 5424334701
5 changed files with 648 additions and 5 deletions
@@ -0,0 +1,90 @@
+import os
+import json
+import math
+import numpy as np
+import torch
+from torch import no_grad, LongTensor
+import librosa
+from torch.nn import functional as F
+import argparse
+from mel_processing import spectrogram_torch
+import commons
+import utils
+from models_infer import SynthesizerTrn
+from text import text_to_sequence
+import gradio as gr
+import torchaudio
+
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+def create_vc_fn(model, hps, speaker_ids):
+    def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
+        input_audio = record_audio if record_audio is not None else upload_audio
+        if input_audio is None:
+            return "You need to record or upload an audio", None
+        sampling_rate, audio = input_audio
+        original_speaker_id = speaker_ids[original_speaker]
+        target_speaker_id = speaker_ids[target_speaker]
+
+        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 != hps.data.sampling_rate:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
+        with no_grad():
+            y = torch.FloatTensor(audio)
+            y = y.unsqueeze(0)
+            spec = spectrogram_torch(y, hps.data.filter_length,
+                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
+                                     center=False)
+            spec_lengths = LongTensor([spec.size(-1)])
+            sid_src = LongTensor([original_speaker_id])
+            sid_tgt = LongTensor([target_speaker_id])
+            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
+                0, 0].data.cpu().float().numpy()
+        del y, spec, spec_lengths, sid_src, sid_tgt
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return vc_fn
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
+
+    args = parser.parse_args()
+    hps = utils.get_hparams_from_file("./configs/finetune_speaker.json")
+    device = "cpu"
+
+    net_g = SynthesizerTrn(
+        len(hps.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)
+    _ = net_g.eval()
+
+    _ = utils.load_checkpoint(args.model_dir, net_g, None)
+    speaker_ids = hps.speakers
+    speakers = list(hps.speakers.keys())
+    vc_fn = create_vc_fn(net_g, hps, speaker_ids)
+    app = gr.Blocks()
+    with app:
+        gr.Markdown("""
+                        录制或上传声音，并选择要转换的音色。User代表的音色是你自己。
+        """)
+        with gr.Column():
+            record_audio = gr.Audio(label="record your voice", source="microphone")
+            upload_audio = gr.Audio(label="or upload audio here", source="upload")
+            source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker")
+            target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
+        with gr.Column():
+            message_box = gr.Textbox(label="Message")
+            converted_audio = gr.Audio(label='converted audio')
+        btn = gr.Button("Convert!")
+        btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
+                  outputs=[message_box, converted_audio])
+    app.launch()