Plachta
91 lines
3.7 KiB
Python
91 lines
3.7 KiB
Python
import os
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import json
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import math
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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 librosa
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from torch.nn import functional as F
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import argparse
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from mel_processing import spectrogram_torch
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import commons
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import utils
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from models_infer import SynthesizerTrn
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from text import text_to_sequence
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import gradio as gr
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import torchaudio
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def get_text(text, hps):
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text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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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):
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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 = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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if sampling_rate != hps.data.sampling_rate:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
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with no_grad():
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y = torch.FloatTensor(audio)
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y = y.unsqueeze(0)
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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)
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spec_lengths = LongTensor([spec.size(-1)])
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sid_src = LongTensor([original_speaker_id])
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sid_tgt = LongTensor([target_speaker_id])
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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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args = parser.parse_args()
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hps = utils.get_hparams_from_file("./configs/finetune_speaker.json")
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device = "cpu"
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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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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],
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outputs=[message_box, converted_audio])
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app.launch()
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