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| import gradio as gr | |
| import os | |
| os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..') | |
| import json | |
| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.utils.data import DataLoader | |
| import commons | |
| import utils | |
| from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence | |
| from scipy.io.wavfile import write | |
| def get_text(text, hps): | |
| text_norm = text_to_sequence(text, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def load_model(model_path, hps): | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(model_path, net_g, None) | |
| return net_g | |
| hps = utils.get_hparams_from_file("configs/vctk_base.json") | |
| # Define a dictionary to store the model paths | |
| model_paths = { | |
| "Model 1": "fr_wa_finetuned_pho/G_125000.pth", | |
| "Model 2": "fr_wa_finetuned/G_198000.pth", | |
| "Model 3": "path_to_model_3_checkpoint.pth", | |
| "Model 4": "path_to_model_4_checkpoint.pth" | |
| } | |
| # Load the initial model | |
| net_g = load_model(model_paths["Model 1"], hps) | |
| def tts(text, speaker_id, model_choice): | |
| global net_g | |
| net_g = load_model(model_paths[model_choice], hps) | |
| if len(text) > 2000: | |
| return "Error: Text is too long", None | |
| sid = torch.LongTensor([speaker_id]) # speaker identity | |
| stn_tst = get_text(text, hps) | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][ | |
| 0, 0].data.float().numpy() | |
| return "Success", (hps.data.sampling_rate, audio) | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tabs(): | |
| for tab_name in ["Phonemes_finetuned", "Graphemes_finetuned", "Phonemes", "Graphemes"]: | |
| with gr.TabItem(tab_name): | |
| tts_input1 = gr.TextArea(label="Text in Walloon in phonemes IPA (2000 words limitation)", value="") | |
| tts_input2 = gr.Dropdown(label="Speaker", choices=["Male", "Female"], type="index", value="Male") | |
| model_choice = gr.Dropdown(label="Model", choices=list(model_paths.keys()), value="Model 1") | |
| tts_submit = gr.Button("Generate", variant="primary") | |
| tts_output1 = gr.Textbox(label="Message") | |
| tts_output2 = gr.Audio(label="Output") | |
| tts_submit.click(tts, [tts_input1, tts_input2, model_choice], [tts_output1, tts_output2]) | |
| app.launch() | |