from typing import Union from argparse import ArgumentParser import asyncio import json import hashlib from os import path, getenv import gradio as gr import torch import numpy as np import librosa import edge_tts import config import util from fairseq import checkpoint_utils from infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from vc_infer_pipeline import VC from config import Config config = Config() force_support = None if config.unsupported is False: if config.device == "mps" or config.device == "cpu": force_support = False else: force_support = True # Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa in_hf_space = getenv('SYSTEM') == 'spaces' # Argument parsing arg_parser = ArgumentParser() arg_parser.add_argument( '--hubert', default=getenv('RVC_HUBERT', 'hubert_base.pt'), help='path to hubert base model (default: hubert_base.pt)' ) arg_parser.add_argument( '--config', default=getenv('RVC_MULTI_CFG', 'multi_config.json'), help='path to config file (default: multi_config.json)' ) arg_parser.add_argument( '--api', action='store_true', help='enable api endpoint' ) arg_parser.add_argument( '--cache-examples', action='store_true', help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa ) args = arg_parser.parse_args() app_css = ''' #model_info img { max-width: 100px; max-height: 100px; float: right; } #model_info p { margin: unset; } ''' app = gr.Blocks( theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"), css=app_css, analytics_enabled=False ) # Load 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() # Load models multi_cfg = json.load(open(args.config, 'r')) loaded_models = [] for model_name in multi_cfg.get('models'): print(f'Loading model: {model_name}') # Load model info model_info = json.load( open(path.join('model', model_name, 'config.json'), 'r') ) # Load RVC checkpoint cpt = torch.load( path.join('model', model_name, model_info['model']), 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) version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) model_version = "V1" elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) model_version = "V2" 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) loaded_models.append(dict( name=model_name, metadata=model_info, vc=vc, net_g=net_g, if_f0=if_f0, target_sr=tgt_sr, test=model_version )) print(f'Models loaded: {len(loaded_models)}') # Edge TTS speakers tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa # https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa def vc_func( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): if input_audio is None: return (None, 'Please provide input audio.') if model_index is None: return (None, 'Please select a model.') model = loaded_models[model_index] # Reference: so-vits (audio_samp, audio_npy) = input_audio # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49 # Can be change well, we will see if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space: return (None, 'Input audio is longer than 60 secs.') # Bloody hell: https://stackoverflow.com/questions/26921836/ if audio_npy.dtype != np.float32: # :thonk: audio_npy = ( audio_npy / np.iinfo(audio_npy.dtype).max ).astype(np.float32) if len(audio_npy.shape) > 1: audio_npy = librosa.to_mono(audio_npy.transpose(1, 0)) if audio_samp != 16000: audio_npy = librosa.resample( audio_npy, orig_sr=audio_samp, target_sr=16000 ) pitch_int = int(pitch_adjust) resample = ( 0 if resample_option == 'Disable resampling' else int(resample_option) ) times = [0, 0, 0] checksum = hashlib.sha512() checksum.update(audio_npy.tobytes()) print(model['test']) output_audio = model['vc'].pipeline( hubert_model, model['net_g'], model['metadata'].get('speaker_id', 0), audio_npy, checksum.hexdigest(), times, pitch_int, f0_method, path.join('model', model['name'], model['metadata']['feat_index']), feat_ratio, model['if_f0'], filter_radius, model['target_sr'], resample, rms_mix_rate, model['test'], 0.5 ) out_sr = ( resample if resample >= 16000 and model['target_sr'] != resample else model['target_sr'] ) print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s') return ((out_sr, output_audio), 'Success') async def edge_tts_vc_func( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): if input_text is None: return (None, 'Please provide TTS text.') if tts_speaker is None: return (None, 'Please select TTS speaker.') if model_index is None: return (None, 'Please select a model.') speaker = tts_speakers_list[tts_speaker]['ShortName'] (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text) return vc_func( (tts_sr, tts_np), model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) def update_model_info(model_index): if model_index is None: return str( '### Model info\n' 'Please select a model from dropdown above.' ) model = loaded_models[model_index] model_icon = model['metadata'].get('icon', '') return str( '### Model info\n' '![model icon]({icon})' '**{name}**\n\n' 'Author: {author}\n\n' 'Source: {source}\n\n' '{note}' ).format( name=model['metadata'].get('name'), author=model['metadata'].get('author', 'Anonymous'), source=model['metadata'].get('source', 'Unknown'), note=model['metadata'].get('note', ''), icon=( model_icon if model_icon.startswith(('http://', 'https://')) else '/file/model/%s/%s' % (model['name'], model_icon) ) ) def _example_vc( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): (audio, message) = vc_func( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) return ( audio, message, update_model_info(model_index) ) async def _example_edge_tts( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): (audio, message) = await edge_tts_vc_func( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) return ( audio, message, update_model_info(model_index) ) with app: gr.Markdown( '## A simplistic Web interface\n' 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa ) with gr.Row(): with gr.Column(): with gr.Tab('Audio conversion'): input_audio = gr.Audio(label='Input audio') vc_convert_btn = gr.Button('Convert', variant='primary') with gr.Tab('TTS conversion'): tts_input = gr.TextArea( label='TTS input text' ) tts_speaker = gr.Dropdown( [ '%s (%s)' % ( s['FriendlyName'], s['Gender'] ) for s in tts_speakers_list ], label='TTS speaker', type='index' ) tts_convert_btn = gr.Button('Convert', variant='primary') pitch_adjust = gr.Slider( label='Pitch', minimum=-24, maximum=24, step=1, value=0 ) f0_method = gr.Radio( label='f0 methods', choices=['pm', 'harvest', 'crepe'], value='pm', interactive=True ) with gr.Accordion('Advanced options', open=False): feat_ratio = gr.Slider( label='Feature ratio', minimum=0, maximum=1, step=0.1, value=0.6 ) filter_radius = gr.Slider( label='Filter radius', minimum=0, maximum=7, step=1, value=3 ) rms_mix_rate = gr.Slider( label='Volume envelope mix rate', minimum=0, maximum=1, step=0.1, value=1 ) resample_rate = gr.Dropdown( [ 'Disable resampling', '16000', '22050', '44100', '48000' ], label='Resample rate', value='Disable resampling' ) with gr.Column(): # Model select model_index = gr.Dropdown( [ '%s - %s' % ( m['metadata'].get('source', 'Unknown'), m['metadata'].get('name') ) for m in loaded_models ], label='Model', type='index' ) # Model info with gr.Box(): model_info = gr.Markdown( '### Model info\n' 'Please select a model from dropdown above.', elem_id='model_info' ) output_audio = gr.Audio(label='Output audio') output_msg = gr.Textbox(label='Output message') multi_examples = multi_cfg.get('examples') if ( multi_examples and multi_examples.get('vc') and multi_examples.get('tts_vc') ): with gr.Accordion('Sweet sweet examples', open=False): with gr.Row(): # VC Example if multi_examples.get('vc'): gr.Examples( label='Audio conversion examples', examples=multi_examples.get('vc'), inputs=[ input_audio, model_index, pitch_adjust, f0_method, feat_ratio ], outputs=[output_audio, output_msg, model_info], fn=_example_vc, cache_examples=args.cache_examples, run_on_click=args.cache_examples ) # Edge TTS Example if multi_examples.get('tts_vc'): gr.Examples( label='TTS conversion examples', examples=multi_examples.get('tts_vc'), inputs=[ tts_input, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio ], outputs=[output_audio, output_msg, model_info], fn=_example_edge_tts, cache_examples=args.cache_examples, run_on_click=args.cache_examples ) vc_convert_btn.click( vc_func, [ input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_rate ], [output_audio, output_msg], api_name='audio_conversion' ) tts_convert_btn.click( edge_tts_vc_func, [ tts_input, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_rate ], [output_audio, output_msg], api_name='tts_conversion' ) model_index.change( update_model_info, inputs=[model_index], outputs=[model_info], show_progress=False, queue=False ) app.queue( concurrency_count=1, max_size=20, api_open=args.api ).launch()