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