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| import argparse | |
| import json | |
| import os | |
| import re | |
| import tempfile | |
| from pathlib import Path | |
| 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 | |
| audio_postprocess_ori = gr.Audio.postprocess | |
| def audio_postprocess(self, y): | |
| data = audio_postprocess_ori(self, y) | |
| if data is None: | |
| return None | |
| return gr_processing_utils.encode_url_or_file_to_base64(data["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" | |
| "\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"\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], api_name=f"tts-model{i}") | |
| 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"\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], api_name=f"vc-model{i}") | |
| 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"\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], api_name=f"svc-model{i}") | |
| 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(share=args.share) | |