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| import os | |
| import json | |
| import traceback | |
| import logging | |
| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| import torch | |
| import asyncio | |
| import edge_tts | |
| from datetime import datetime | |
| from fairseq import checkpoint_utils | |
| from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono | |
| from vc_infer_pipeline import VC | |
| from config import Config | |
| config = Config() | |
| logging.getLogger("numba").setLevel(logging.WARNING) | |
| limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces | |
| def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index): | |
| def vc_fn( | |
| input_audio, | |
| f0_up_key, | |
| f0_method, | |
| index_rate, | |
| tts_mode, | |
| tts_text, | |
| tts_voice | |
| ): | |
| try: | |
| if tts_mode: | |
| if len(tts_text) > 100 and limitation: | |
| return "Text is too long", None | |
| if tts_text is None or tts_voice is None: | |
| return "You need to enter text and select a voice", None | |
| asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3")) | |
| audio, sr = librosa.load("tts.mp3", sr=16000, mono=True) | |
| else: | |
| 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 duration > 20 and limitation: | |
| return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None | |
| 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) | |
| times = [0, 0, 0] | |
| f0_up_key = int(f0_up_key) | |
| audio_opt = vc.pipeline( | |
| hubert_model, | |
| net_g, | |
| 0, | |
| audio, | |
| times, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| index_rate, | |
| if_f0, | |
| f0_file=None, | |
| ) | |
| print( | |
| f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s" | |
| ) | |
| return (tgt_sr, audio_opt) | |
| except BaseException: | |
| info = traceback.format_exc() | |
| print(info) | |
| return info, (None, None) | |
| return vc_fn | |
| def load_hubert(): | |
| global hubert_model | |
| models, _, _ = checkpoint_utils.load_model_ensemble_and_task( | |
| ["hubert_base.pt"], | |
| suffix="", | |
| ) | |
| hubert_model = models[0] | |
| hubert_model = hubert_model.to(config.device) | |
| if config.is_half: | |
| hubert_model = hubert_model.half() | |
| else: | |
| hubert_model = hubert_model.float() | |
| hubert_model.eval() | |
| def change_to_tts_mode(tts_mode): | |
| if tts_mode: | |
| return gr.Audio.update(visible=False), gr.Checkbox.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True) | |
| else: | |
| return gr.Audio.update(visible=True), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) | |
| def change_to_microphone_mode(microphone_mode): | |
| if microphone_mode: | |
| return gr.Audio.update(source="microphone") | |
| else: | |
| return gr.Audio.update(source="upload") | |
| if __name__ == '__main__': | |
| load_hubert() | |
| tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) | |
| voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] | |
| models = [] | |
| with open("weights/model_info.json", "r", encoding="utf-8") as f: | |
| models_info = json.load(f) | |
| for model_name, info in models_info.items(): | |
| if not info['enable']: | |
| continue | |
| model_title = info['title'] | |
| model_author = info.get("author", None) | |
| model_cover = f"weights/{model_name}/{info['cover']}" | |
| model_index = f"weights/{model_name}/{info['feature_retrieval_library']}" | |
| cpt = torch.load(f"weights/{model_name}/{model_name}.pth", map_location="cpu") | |
| tgt_sr = cpt["config"][-1] | |
| cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk | |
| if_f0 = cpt.get("f0", 1) | |
| if if_f0 == 1: | |
| net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) | |
| else: | |
| net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) | |
| del net_g.enc_q | |
| print(net_g.load_state_dict(cpt["weight"], strict=False)) | |
| net_g.eval().to(config.device) | |
| if config.is_half: | |
| net_g = net_g.half() | |
| else: | |
| net_g = net_g.float() | |
| vc = VC(tgt_sr, config) | |
| print(f"Model loaded: {model_name}") | |
| models.append((model_name, model_title, model_author, model_cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index))) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| "# <center> RVC Mochinoa\n" | |
| "望月のあ Mochizuki Noa\n\n" | |
| "YouTube: https://www.youtube.com/@MochizukiNoa \n\n" | |
| "Twitter: https://twitter.com/_noach \n\n" | |
| "このプロジェクトに含まれるRVCモデル `mochinoa`及びこのモデルを利用して作成された音声を利用する場合、`mochinoa`モデルの[ライセンス](https://huggingface.co/spaces/hrdtbs/rvc-mochinoa/blob/main/weights/mochinoa/LICENCE.md)に従ってください。このモデルを除き、このプロジェクトはMITライセンスで公開されています。\n\n" | |
| ) | |
| with gr.Tabs(): | |
| for (name, title, author, cover, vc_fn) in models: | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| gr.Markdown( | |
| '<div align="center">' | |
| f'<div>{title}</div>\n'+ | |
| (f'<div>Model author: {author}</div>' if author else "")+ | |
| (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+ | |
| '</div>' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| vc_input = gr.Audio(source="upload", label="Input audio"+' (less than 20 seconds)' if limitation else '') | |
| vc_microphone_mode = gr.Checkbox(label="use microphone", value=False) | |
| vc_transpose = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice') | |
| vc_f0method = gr.Radio( | |
| label="Pitch extraction algorithm", | |
| choices=["pm", "harvest"], | |
| value="pm", | |
| interactive=True, | |
| info="PM is fast but Harvest is better for low frequencies. (Default: PM)" | |
| ) | |
| vc_index_ratio = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| label="Retrieval feature ratio", | |
| value=0.6, | |
| interactive=True, | |
| info="(Default: 0.6)" | |
| ) | |
| tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False) | |
| tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text") | |
| tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") | |
| vc_submit = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| vc_output = gr.Audio(label="Output Audio") | |
| vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output]) | |
| tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, vc_microphone_mode, tts_text, tts_voice]) | |
| vc_microphone_mode.change(change_to_microphone_mode, [vc_microphone_mode], [vc_input]) | |
| app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab) |