Spaces:
Runtime error
Runtime error
File size: 3,871 Bytes
ae8e1dd |
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 |
import gradio as gr
import argparse
import json
import datetime as dt
import numpy as np
from scipy.io.wavfile import write
import gradio as gr
import torch
from pydub import AudioSegment
from model.classifier import SpecClassifier
from torch.utils.data import DataLoader
from text import text_to_sequence, cmudict
from text.symbols import symbols
import utils_data as utils
from utils import load_checkpoint_no_logger
from kaldiio import WriteHelper
import os
from tqdm import tqdm
from text import text_to_sequence, convert_text
import sys
from model import GradTTSXvector, GradTTSWithEmo
import IPython.display as ipd
device = ('cuda' if torch.cuda.is_available() else 'cpu')
device
hps, args = utils.get_hparams_decode_two_mixture()
gradtts_uncond_model = GradTTSWithEmo
gradtts_uncond_model = gradtts_uncond_model(**hps.model).to(device)
model = SpecClassifier(
in_dim=hps.data.n_mel_channels,
d_decoder=hps.model.d_decoder,
h_decoder=hps.model.h_decoder,
l_decoder=hps.model.l_decoder,
k_decoder=hps.model.k_decoder,
decoder_dropout=hps.model.decoder_dropout,
n_class=hps.model.n_emos,
cond_dim=hps.data.n_mel_channels,
model_type=getattr(hps.model, "classifier_type", "CNN-with-time")
)
ckpt = './cnnwt_SGD_1959.pt'
ckpt_tts = './grad_uncond_cnn_001.pt'
utils.load_checkpoints_no_logger(ckpt_tts, gradtts_uncond_model, None)
utils.load_checkpoints_no_logger(ckpt, model, None)
_ = model.to(device).eval()
HIFIGAN_CONFIG = './config.json'
HIFIGAN_CHECKPT = './g_01720000'
from models import Generator as HiFiGAN
from env import AttrDict
print('Initializing HiFi-GAN...')
with open(HIFIGAN_CONFIG) as f:
h = AttrDict(json.load(f))
vocoder = HiFiGAN(h)
vocoder.load_state_dict(torch.load(HIFIGAN_CHECKPT, map_location=lambda loc, storage: loc)['generator'])
_ = vocoder.to(device).eval()
vocoder.remove_weight_norm()
def generate_audio(text, quantity, speaker, emotion_1, emotion_2):
x, x_lengths = convert_text(text)
emo_1, emo_2 = emotion_1, emotion_2
emo1 = torch.LongTensor([emo_1]).to(device)
emo2 = torch.LongTensor([emo_2]).to(device)
sid = torch.LongTensor([spekears.index(speaker)]).to(device)
intensity = quantity / 100
y_enc, y_dec, attn = gradtts_uncond_model.classifier_guidance_decode_two_mixture(
x, x_lengths,
n_timesteps=10,
temperature=2.0,
stoc=args.stoc,
spk=sid,
emo1=emo1,
emo2=emo2,
emo1_weight=intensity,
length_scale=1.,
classifier_func=model.forward,
guidance=300,
classifier_type=model.model_type
)
y_dec = y_dec.detach()
# y_dec = torch.nan_to_num(y_dec)
res = y_dec.squeeze().cpu().numpy()
x = torch.from_numpy(res).cuda().unsqueeze(0)
y_g_hat = vocoder(x)
audio = y_g_hat.squeeze()
audio = audio * 32768.0
audio = audio.detach().cpu().numpy().astype('int16')
sr = 22050
return sr, audio
# def sentence_builder(quantity, emotion_1, emotion_2):
# return f"""The {quantity} {emotion_1}s from {" and ".join(emotion_2)}"""
emotions = sorted(["angry", "surprise", "fear", "happy", "neutral", "sad"])
spekears = ['Madi', 'Marzhan', 'Akzhol']
demo = gr.Interface(
generate_audio,
[
gr.Slider(0, 100, value=0, step=10, label="Count", info="Choose between 0 and 100"),
gr.Dropdown(spekears, value=spekears[1], label="Narrator", info="Select a narrator."
),
gr.Dropdown(emotions, label="Emotion 1", info="Select first emotion"),
gr.Dropdown(emotions, value=emotions[3], label="Emotion 2", info="Select second emotion."
),
],
"audio"
)
demo.launch() |