Plachta
93 lines
4.3 KiB
Python
93 lines
4.3 KiB
Python
import os
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import numpy as np
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import torch
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from torch import no_grad, LongTensor
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import argparse
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from models_infer import spectrogram_torch
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import utils
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from models_infer import SynthesizerTrn
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import gradio as gr
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import torchaudio
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import webbrowser
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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def create_vc_fn(model, hps, speaker_ids):
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def vc_fn(original_speaker, target_speaker, record_audio, upload_audio, denoise):
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input_audio = record_audio if record_audio is not None else upload_audio
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if input_audio is None:
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return "You need to record or upload an audio", None
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sampling_rate, audio = input_audio
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original_speaker_id = speaker_ids[original_speaker]
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target_speaker_id = speaker_ids[target_speaker]
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audio = torch.tensor(audio).type(torch.float32)
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audio = audio.squeeze().unsqueeze(0)
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audio = audio / max(-audio.min(), audio.max()) / 0.99
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if sampling_rate != hps.data.sampling_rate:
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audio = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=22050)(audio)
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with no_grad():
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y = torch.FloatTensor(audio)
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y = y / max(-y.min(), y.max()) / 0.99
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if denoise:
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torchaudio.save("infer.wav", y.cpu(), 22050, channels_first=True)
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os.system(f"demucs --two-stems=vocals infer.wav")
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y, sr = torchaudio.load(f"./separated/htdemucs/infer/vocals.wav", frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
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y = y.mean(dim=0).unsqueeze(0)
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if sr != 22050:
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y = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(y)
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y = y.to(device)
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spec = spectrogram_torch(y, hps.data.filter_length,
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
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center=False).to(device)
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spec_lengths = LongTensor([spec.size(-1)]).to(device)
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sid_src = LongTensor([original_speaker_id]).to(device)
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sid_tgt = LongTensor([target_speaker_id]).to(device)
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audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
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0, 0].data.cpu().float().numpy()
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del y, spec, spec_lengths, sid_src, sid_tgt
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return "Success", (hps.data.sampling_rate, audio)
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return vc_fn
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
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parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
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parser.add_argument("--share", default=False, help="make link public (used in colab)")
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args = parser.parse_args()
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hps = utils.get_hparams_from_file(args.config_dir)
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net_g = SynthesizerTrn(
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len(hps.symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model).to(device)
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_ = net_g.eval()
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_ = utils.load_checkpoint(args.model_dir, net_g, None)
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speaker_ids = hps.speakers
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speakers = list(hps.speakers.keys())
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vc_fn = create_vc_fn(net_g, hps, speaker_ids)
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app = gr.Blocks()
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with app:
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gr.Markdown("""
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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。
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""")
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with gr.Column():
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record_audio = gr.Audio(label="record your voice", source="microphone")
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upload_audio = gr.Audio(label="or upload audio here", source="upload")
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source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker")
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target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
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denoise_checkbox = gr.Checkbox(label="denoise using demucs", value=False)
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with gr.Column():
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message_box = gr.Textbox(label="Message")
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converted_audio = gr.Audio(label='converted audio')
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btn = gr.Button("Convert!")
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btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio, denoise_checkbox],
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outputs=[message_box, converted_audio])
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webbrowser.open("http://127.0.0.1:7860")
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app.launch(share=args.share)
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