import json import os import re import librosa import numpy as np import torch from torch import no_grad, LongTensor import commons import utils import gradio as gr from models import SynthesizerTrn from text import text_to_sequence, _clean_text from mel_processing import spectrogram_torch limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces def get_text(text, hps, is_phoneme): text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme else hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm def create_tts_fn(model, hps, speaker_ids): def tts_fn(text, speaker, speed, is_phoneme): if limitation: text_len = len(text) max_len = 60 if is_phoneme: max_len *= 3 else: if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners": text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text)) if text_len > max_len: return "Error: Text is too long", None speaker_id = speaker_ids[speaker] stn_tst = get_text(text, hps, is_phoneme) with no_grad(): x_tst = stn_tst.unsqueeze(0) x_tst_lengths = LongTensor([stn_tst.size(0)]) sid = LongTensor([speaker_id]) audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() del stn_tst, x_tst, x_tst_lengths, sid return "Success", (hps.data.sampling_rate, audio) return tts_fn def create_vc_fn(model, hps, speaker_ids): def vc_fn(original_speaker, target_speaker, input_audio): if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = input_audio duration = audio.shape[0] / sampling_rate if limitation and duration > 15: return "Error: Audio is too long", None original_speaker_id = speaker_ids[original_speaker] target_speaker_id = speaker_ids[target_speaker] audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) if sampling_rate != hps.data.sampling_rate: audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) with no_grad(): y = torch.FloatTensor(audio) y = y.unsqueeze(0) spec = spectrogram_torch(y, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, center=False) spec_lengths = LongTensor([spec.size(-1)]) sid_src = LongTensor([original_speaker_id]) sid_tgt = LongTensor([target_speaker_id]) audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ 0, 0].data.cpu().float().numpy() del y, spec, spec_lengths, sid_src, sid_tgt return "Success", (hps.data.sampling_rate, audio) return vc_fn def create_soft_vc_fn(model, hps, speaker_ids): def soft_vc_fn(target_speaker, input_audio1, input_audio2): input_audio = input_audio1 if input_audio is None: input_audio = input_audio2 if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = input_audio duration = audio.shape[0] / sampling_rate if limitation and duration > 15: return "Error: Audio is too long", None target_speaker_id = speaker_ids[target_speaker] audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) if sampling_rate != 16000: audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) with torch.inference_mode(): units = hubert.units(torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0)) with no_grad(): unit_lengths = LongTensor([units.size(1)]) sid = LongTensor([target_speaker_id]) audio = model.infer(units, unit_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8)[0][0, 0].data.cpu().float().numpy() del units, unit_lengths, sid return "Success", (hps.data.sampling_rate, audio) return soft_vc_fn def create_to_phoneme_fn(hps): def to_phoneme_fn(text): return _clean_text(text, hps.data.text_cleaners) if text != "" else "" return to_phoneme_fn css = """ #advanced-btn { color: white; border-color: black; background: black; font-size: .7rem !important; line-height: 19px; margin-top: 24px; margin-bottom: 12px; padding: 2px 8px; border-radius: 14px !important; } #advanced-options { display: none; margin-bottom: 20px; } """ if __name__ == '__main__': models_tts = [] models_vc = [] models_soft_vc = [] with open("saved_model/info.json", "r", encoding="utf-8") as f: models_info = json.load(f) for i, info in models_info.items(): name = info["title"] lang = info["lang"] example = info["example"] config_path = f"saved_model/{i}/config.json" model_path = f"saved_model/{i}/model.pth" cover_path = f"saved_model/{i}/cover.jpg" hps = utils.get_hparams_from_file(config_path) model = SynthesizerTrn( len(hps.symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model) utils.load_checkpoint(model_path, model, None) model.eval() speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"] speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"] t = info["type"] if t == "vits": models_tts.append((name, cover_path, speakers, lang, example, hps.symbols, create_tts_fn(model, hps, speaker_ids), create_to_phoneme_fn(hps))) models_vc.append((name, cover_path, speakers, create_vc_fn(model, hps, speaker_ids))) elif t == "soft-vits-vc": models_soft_vc.append((name, cover_path, speakers, create_soft_vc_fn(model, hps, speaker_ids))) hubert = torch.hub.load("bshall/hubert:main", "hubert_soft") app = gr.Blocks(css=css) with app: gr.Markdown("# Moe TTS And Voice Conversion Using VITS Model\n\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.moegoe)\n\n" "unofficial demo for \n\n" "- [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)\n" "- [https://github.com/Francis-Komizu/VITS](https://github.com/Francis-Komizu/VITS)\n" "- [https://github.com/luoyily/MoeTTS](https://github.com/luoyily/MoeTTS)\n" "- [https://github.com/Francis-Komizu/Sovits](https://github.com/Francis-Komizu/Sovits)" ) with gr.Tabs(): with gr.TabItem("TTS"): with gr.Tabs(): for i, (name, cover_path, speakers, lang, example, symbols, tts_fn, to_phoneme_fn) in enumerate(models_tts): with gr.TabItem(f"model{i}"): with gr.Column(): gr.Markdown(f"## {name}\n\n" f"![cover](file/{cover_path})\n\n" f"lang: {lang}") tts_input1 = gr.TextArea(label="Text (60 words limitation)", value=example, elem_id=f"tts-input{i}") tts_input2 = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[0]) tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1) with gr.Accordion(label="Advanced Options", open=False): phoneme_input = gr.Checkbox(value=False, label="Phoneme input") to_phoneme_btn = gr.Button("Covert text to phoneme") phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1], samples=[[x] for x in symbols]) phoneme_list_json = gr.Json(value=symbols, visible=False) tts_submit = gr.Button("Generate", variant="primary") tts_output1 = gr.Textbox(label="Output Message") tts_output2 = gr.Audio(label="Output Audio") tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input], [tts_output1, tts_output2]) to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1]) phoneme_list.click(None, [phoneme_list, phoneme_list_json], [], _js=f""" (i,phonemes) => {{ let text_input = document.querySelector("body > gradio-app"); if (text_input.shadowRoot != null) text_input = text_input.shadowRoot; text_input = text_input.querySelector("#tts-input{i}").querySelector("textarea"); let startPos = text_input.selectionStart; let endPos = text_input.selectionEnd; let oldTxt = text_input.value; let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos); text_input.value = result; text_input.focus() text_input.selectionStart = startPos + phonemes[i].length; text_input.selectionEnd = startPos + phonemes[i].length; text_input.blur() }}""") with gr.TabItem("Voice Conversion"): with gr.Tabs(): for i, (name, cover_path, speakers, vc_fn) in enumerate(models_vc): with gr.TabItem(f"model{i}"): gr.Markdown(f"## {name}\n\n" f"![cover](file/{cover_path})") vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index", value=speakers[0]) vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index", value=speakers[1]) vc_input3 = gr.Audio(label="Input Audio (15s limitation)") vc_submit = gr.Button("Convert", variant="primary") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio") vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2]) with gr.TabItem("Soft Voice Conversion"): with gr.Tabs(): for i, (name, cover_path, speakers, soft_vc_fn) in enumerate(models_soft_vc): with gr.TabItem(f"model{i}"): gr.Markdown(f"## {name}\n\n" f"![cover](file/{cover_path})") vc_input1 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index", value=speakers[0]) source_tabs = gr.Tabs() with source_tabs: with gr.TabItem("microphone"): vc_input2 = gr.Audio(label="Input Audio (15s limitation)", source="microphone") with gr.TabItem("upload"): vc_input3 = gr.Audio(label="Input Audio (15s limitation)", source="upload") vc_submit = gr.Button("Convert", variant="primary") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio") # clear inputs source_tabs.set_event_trigger("change", None, [], [vc_input2, vc_input3], js="()=>[null,null]") vc_submit.click(soft_vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2]) app.queue(concurrency_count=3).launch(show_api=False)