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import os
import torch
import librosa
import gradio as gr
from scipy.io.wavfile import write
from transformers import WavLMModel
import utils
from models import SynthesizerTrn
from mel_processing import mel_spectrogram_torch
from speaker_encoder.voice_encoder import SpeakerEncoder
'''
def get_wavlm():
os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
shutil.move('WavLM-Large.pt', 'wavlm')
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Loading FreeVC...")
hps = utils.get_hparams_from_file("configs/freevc.json")
freevc = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model).to(device)
_ = freevc.eval()
_ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None)
smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
print("Loading FreeVC-s...")
hps = utils.get_hparams_from_file("configs/freevc-s.json")
freevc_s = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model).to(device)
_ = freevc_s.eval()
_ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None)
print("Loading WavLM for content...")
cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
def convert(model, src, tgt):
with torch.no_grad():
# tgt
wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
if model == "FreeVC":
g_tgt = smodel.embed_utterance(wav_tgt)
g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
else:
wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
mel_tgt = mel_spectrogram_torch(
wav_tgt,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
# src
wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
# infer
if model == "FreeVC":
audio = freevc.infer(c, g=g_tgt)
else:
audio = freevc_s.infer(c, mel=mel_tgt)
audio = audio[0][0].data.cpu().float().numpy()
write("out.wav", hps.data.sampling_rate, audio)
out = "out.wav"
return out
model = gr.Dropdown(choices=["FreeVC", "FreeVC-s"], value="FreeVC",type="value", label="Model")
audio1 = gr.inputs.Audio(label="Source Audio", type='filepath')
audio2 = gr.inputs.Audio(label="Reference Audio", type='filepath')
inputs = [model, audio1, audio2]
outputs = gr.outputs.Audio(label="Output Audio", type='filepath')
title = "FreeVC"
description = "Gradio Demo for FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion. To use it, simply upload your audio, or click the example to load. Read more at the links below. Note: It seems that the WavLM checkpoint in HuggingFace is a little different from the one used to train FreeVC, which may degrade the performance a bit."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.15418' target='_blank'>Paper</a> | <a href='https://github.com/OlaWod/FreeVC' target='_blank'>Github Repo</a></p>"
examples=[["FreeVC", 'p225_001.wav', 'p226_002.wav'], ["FreeVC-s", 'p226_002.wav', 'p225_001.wav']]
gr.Interface(convert, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
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