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import torch
import torchaudio
import voicebox.src.attacks.offline.perturbation.voicebox.voicebox as vb #To access VoiceBox class
#import voicebox.src.attacks.online.voicebox_streamer as streamer #To access VoiceBoxStreamer class
import numpy as np
from voicebox.src.constants import PPG_PRETRAINED_PATH
#Set voicebox default parameters
LOOKAHEAD = 5
voicebox_kwargs={'win_length': 256,
'ppg_encoder_hidden_size': 256,
'use_phoneme_encoder': True,
'use_pitch_encoder': True,
'use_loudness_encoder': True,
'spec_encoder_lookahead_frames': 0,
'spec_encoder_type': 'mel',
'spec_encoder_mlp_depth': 2,
'bottleneck_lookahead_frames': LOOKAHEAD,
'ppg_encoder_path': PPG_PRETRAINED_PATH,
'n_bands': 128,
'spec_encoder_hidden_size': 512,
'bottleneck_skip': True,
'bottleneck_hidden_size': 512,
'bottleneck_feedforward_size': 512,
'bottleneck_type': 'lstm',
'bottleneck_depth': 2,
'control_eps': 0.5,
'projection_norm': float('inf'),
'conditioning_dim': 512}
#Load pretrained model:
model = vb.VoiceBox(**voicebox_kwargs)
model.load_state_dict(torch.load('voicebox/pretrained/voicebox/voicebox_final.pt', map_location=torch.device('cpu')), strict=True)
model.eval()
#Define function to convert final audio format:
def float32_to_int16(waveform):
waveform = waveform / np.abs(waveform).max()
waveform = waveform * 32767
waveform = waveform.astype(np.int16)
waveform = waveform.ravel()
return waveform
#Define predict function:
def predict(inp):
#How to transform audio from string to tensor
waveform, sample_rate = torchaudio.load(inp)
#Resample to 16kHz
transform_to_16hz = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
waveform = transform_to_16hz(waveform)
sample_rate = 16000
#Run model without changing weights
with torch.no_grad():
waveform = model(waveform)
#Transform output audio into gradio-readable format
waveform = waveform.numpy()
waveform = float32_to_int16(waveform)
return sample_rate, waveform
#Set up gradio interface
import gradio as gr
interface = gr.Interface(
fn=predict,
inputs=gr.Audio(type="filepath"),
outputs=gr.Audio()
)
interface.launch() |