import argparse import json import os import re import tempfile import librosa import numpy as np import torch from torch import no_grad, LongTensor import commons import utils import gradio as gr import gradio.utils as gr_utils import gradio.processing_utils as gr_processing_utils 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 audio_postprocess(self, y): if y is None: return None if gr_utils.validate_url(y): file = gr_processing_utils.download_to_file(y, dir=self.temp_dir) elif isinstance(y, tuple): sample_rate, data = y file = tempfile.NamedTemporaryFile( suffix=".wav", dir=self.temp_dir, delete=False ) gr_processing_utils.audio_to_file(sample_rate, data, file.name) else: file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir) return gr_processing_utils.encode_url_or_file_to_base64(file.name) gr.Audio.postprocess = audio_postprocess def get_text(text, hps, is_symbol): text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol 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_symbol): if limitation: text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) max_len = 150 if is_symbol: max_len *= 3 if text_len > max_len: return "Error: Text is too long", None speaker_id = speaker_ids[speaker] stn_tst = get_text(text, hps, is_symbol) with no_grad(): x_tst = stn_tst.unsqueeze(0).to(device) x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) sid = LongTensor([speaker_id]).to(device) 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 > 30: 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).to(device) spec_lengths = LongTensor([spec.size(-1)]).to(device) sid_src = LongTensor([original_speaker_id]).to(device) sid_tgt = LongTensor([target_speaker_id]).to(device) 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 > 30: 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).to(device)) with no_grad(): unit_lengths = LongTensor([units.size(1)]).to(device) sid = LongTensor([target_speaker_id]).to(device) 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_symbol_fn(hps): def to_symbol_fn(is_symbol_input, input_text, temp_text): return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ else (temp_text, temp_text) return to_symbol_fn download_audio_js = """ () =>{{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let audio = root.querySelector("#{audio_id}").querySelector("audio"); if (audio == undefined) return; audio = audio.src; let oA = document.createElement("a"); oA.download = Math.floor(Math.random()*100000000)+'.wav'; oA.href = audio; document.body.appendChild(oA); oA.click(); oA.remove(); }} """ if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') parser.add_argument("--share", action="store_true", default=False, help="share gradio app") args = parser.parse_args() device = torch.device(args.device) 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"] author = info["author"] lang = info["lang"] example = info["example"] config_path = f"saved_model/{i}/config.json" model_path = f"saved_model/{i}/model.pth" cover = info["cover"] cover_path = f"saved_model/{i}/{cover}" if cover else None 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().to(device) 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, author, cover_path, speakers, lang, example, hps.symbols, create_tts_fn(model, hps, speaker_ids), create_to_symbol_fn(hps))) models_vc.append((name, author, cover_path, speakers, create_vc_fn(model, hps, speaker_ids))) elif t == "soft-vits-vc": models_soft_vc.append((name, author, cover_path, speakers, create_soft_vc_fn(model, hps, speaker_ids))) hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).to(device) app = gr.Blocks() 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" "[Open In Colab]" "(https://colab.research.google.com/drive/14Pb8lpmwZL-JI5Ub6jpG4sz2-8KS0kbS?usp=sharing)" " without queue and length limitation.\n\n" "Feel free to [open discussion](https://huggingface.co/spaces/skytnt/moe-tts/discussions/new) " "if you want to add your model to this app.") with gr.Tabs(): with gr.TabItem("TTS"): with gr.Tabs(): for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(models_tts): with gr.TabItem(f"model{i}"): with gr.Column(): cover_markdown = f"![cover](file/{cover_path})\n\n" if cover_path else "" gr.Markdown(f"## {name}\n\n" f"{cover_markdown}" f"model author: {author}\n\n" f"language: {lang}") tts_input1 = gr.TextArea(label="Text (150 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): temp_text_var = gr.Variable() symbol_input = gr.Checkbox(value=False, label="Symbol input") symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1], samples=[[x] for x in symbols], elem_id=f"symbol-list{i}") symbol_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", elem_id=f"tts-audio{i}") download = gr.Button("Download Audio") download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}")) tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input], [tts_output1, tts_output2]) symbol_input.change(to_symbol_fn, [symbol_input, tts_input1, temp_text_var], [tts_input1, temp_text_var]) symbol_list.click(None, [symbol_list, symbol_list_json], [], _js=f""" (i,symbols) => {{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let text_input = root.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) + symbols[i] + oldTxt.substring(endPos); text_input.value = result; let x = window.scrollX, y = window.scrollY; text_input.focus(); text_input.selectionStart = startPos + symbols[i].length; text_input.selectionEnd = startPos + symbols[i].length; text_input.blur(); window.scrollTo(x, y); return []; }}""") with gr.TabItem("Voice Conversion"): with gr.Tabs(): for i, (name, author, cover_path, speakers, vc_fn) in enumerate(models_vc): with gr.TabItem(f"model{i}"): cover_markdown = f"![cover](file/{cover_path})\n\n" if cover_path else "" gr.Markdown(f"## {name}\n\n" f"{cover_markdown}" f"model author: {author}") 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[min(len(speakers) - 1, 1)]) vc_input3 = gr.Audio(label="Input Audio (30s limitation)") vc_submit = gr.Button("Convert", variant="primary") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio", elem_id=f"vc-audio{i}") download = gr.Button("Download Audio") download.click(None, [], [], _js=download_audio_js.format(audio_id=f"vc-audio{i}")) 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, author, cover_path, speakers, soft_vc_fn) in enumerate(models_soft_vc): with gr.TabItem(f"model{i}"): cover_markdown = f"![cover](file/{cover_path})\n\n" if cover_path else "" gr.Markdown(f"## {name}\n\n" f"{cover_markdown}" f"model author: {author}") 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 (30s limitation)", source="microphone") with gr.TabItem("upload"): vc_input3 = gr.Audio(label="Input Audio (30s limitation)", source="upload") vc_submit = gr.Button("Convert", variant="primary") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio", elem_id=f"svc-audio{i}") download = gr.Button("Download Audio") download.click(None, [], [], _js=download_audio_js.format(audio_id=f"svc-audio{i}")) # 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]) gr.Markdown( "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)" ) app.queue(concurrency_count=3).launch(show_api=False, share=args.share)