Spaces:
Runtime error
Runtime error
File size: 5,554 Bytes
4a14f7f 64db264 4a14f7f 64db264 4a14f7f ac4f0f1 4a14f7f 64db264 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
import os
os.system("pip install gradio==2.9b24")
import gradio as gr
vocoder_url = 'https://bj.bcebos.com/v1/ai-studio-online/e46d52315a504f1fa520528582a8422b6fa7006463844b84b8a2c3d21cc314db?/Vocoder.zip'
models_url = 'https://bj.bcebos.com/v1/ai-studio-online/6c081f29caad483ebd4cded087ee6ddbfc8dca8fb89d4ab69d44253ce5525e32?/Models.zip'
from io import BytesIO
from zipfile import ZipFile
from urllib.request import urlopen
if not (os.path.isdir('Vocoder') and os.path.isdir('Models')):
for url in [vocoder_url, models_url]:
resp = urlopen(url)
zipfile = ZipFile(BytesIO(resp.read()))
zipfile.extractall()
import random
import yaml
from munch import Munch
import numpy as np
import paddle
from paddle import nn
import paddle.nn.functional as F
import paddleaudio
import librosa
from starganv2vc_paddle.Utils.JDC.model import JDCNet
from starganv2vc_paddle.models import Generator, MappingNetwork, StyleEncoder
speakers = [225,228,229,230,231,233,236,239,240,244,226,227,232,243,254,256,258,259,270,273]
to_mel = paddleaudio.features.MelSpectrogram(
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
to_mel.fbank_matrix[:] = paddle.load('starganv2vc_paddle/fbank_matrix.pd')['fbank_matrix']
mean, std = -4, 4
def preprocess(wave):
wave_tensor = paddle.to_tensor(wave).astype(paddle.float32)
mel_tensor = to_mel(wave_tensor)
mel_tensor = (paddle.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
return mel_tensor
def build_model(model_params={}):
args = Munch(model_params)
generator = Generator(args.dim_in, args.style_dim, args.max_conv_dim, w_hpf=args.w_hpf, F0_channel=args.F0_channel)
mapping_network = MappingNetwork(args.latent_dim, args.style_dim, args.num_domains, hidden_dim=args.max_conv_dim)
style_encoder = StyleEncoder(args.dim_in, args.style_dim, args.num_domains, args.max_conv_dim)
nets_ema = Munch(generator=generator,
mapping_network=mapping_network,
style_encoder=style_encoder)
return nets_ema
def compute_style(speaker_dicts):
reference_embeddings = {}
for key, (path, speaker) in speaker_dicts.items():
if path == "":
label = paddle.to_tensor([speaker], dtype=paddle.int64)
latent_dim = starganv2.mapping_network.shared[0].weight.shape[0]
ref = starganv2.mapping_network(paddle.randn([1, latent_dim]), label)
else:
wave, sr = librosa.load(path, sr=24000)
audio, index = librosa.effects.trim(wave, top_db=30)
if sr != 24000:
wave = librosa.resample(wave, sr, 24000)
mel_tensor = preprocess(wave)
with paddle.no_grad():
label = paddle.to_tensor([speaker], dtype=paddle.int64)
ref = starganv2.style_encoder(mel_tensor.unsqueeze(1), label)
reference_embeddings[key] = (ref, label)
return reference_embeddings
F0_model = JDCNet(num_class=1, seq_len=192)
params = paddle.load("Models/bst.pd")['net']
F0_model.set_state_dict(params)
_ = F0_model.eval()
import yaml
import paddle
from yacs.config import CfgNode
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
with open('Vocoder/config.yml') as f:
voc_config = CfgNode(yaml.safe_load(f))
voc_config["generator_params"].pop("upsample_net")
voc_config["generator_params"]["upsample_scales"] = voc_config["generator_params"].pop("upsample_params")["upsample_scales"]
vocoder = PWGGenerator(**voc_config["generator_params"])
vocoder.remove_weight_norm()
vocoder.eval()
vocoder.set_state_dict(paddle.load('Vocoder/checkpoint-400000steps.pd'))
model_path = 'Models/vc_ema.pd'
with open('Models/config.yml') as f:
starganv2_config = yaml.safe_load(f)
starganv2 = build_model(model_params=starganv2_config["model_params"])
params = paddle.load(model_path)
params = params['model_ema']
_ = [starganv2[key].set_state_dict(params[key]) for key in starganv2]
_ = [starganv2[key].eval() for key in starganv2]
starganv2.style_encoder = starganv2.style_encoder
starganv2.mapping_network = starganv2.mapping_network
starganv2.generator = starganv2.generator
# Compute speakers' styles under the Demo directory
speaker_dicts = {}
selected_speakers = [273, 259, 258, 243, 254, 244, 236, 233, 230, 228]
for s in selected_speakers:
k = s
speaker_dicts['p' + str(s)] = ('Demo/VCTK-corpus/p' + str(k) + '/p' + str(k) + '_023.wav', speakers.index(s))
reference_embeddings = compute_style(speaker_dicts)
examples = [['Demo/VCTK-corpus/p254/p254_023.wav', 'p254'], ['Demo/VCTK-corpus/p236/p236_023.wav', 'p243']]
def app(wav_path, speaker_id):
audio, _ = librosa.load(wav_path, sr=24000)
audio = audio / np.max(np.abs(audio))
audio.dtype = np.float32
source = preprocess(audio)
ref = reference_embeddings[speaker_id][0]
with paddle.no_grad():
f0_feat = F0_model.get_feature_GAN(source.unsqueeze(1))
out = starganv2.generator(source.unsqueeze(1), ref, F0=f0_feat)
c = out.transpose([0,1,3,2]).squeeze()
y_out = vocoder.inference(c)
y_out = y_out.reshape([-1])
return (24000, y_out.numpy())
title="StarGANv2 Voice Conversion"
description="Gradio Demo for voice conversion using paddlepaddle. "
iface = gr.Interface(app, [gr.inputs.Audio(source="microphone", type="filepath"),
gr.inputs.Radio(list(speaker_dicts.keys()), type="value", default='p228', label='speaker id')],
"audio", title=title, description=description, examples=examples)
iface.launch()
|