File size: 18,827 Bytes
891bd38
 
eb463f4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7499aaa
eb463f4
 
 
 
 
 
 
 
891bd38
 
 
 
 
eb463f4
 
891bd38
 
 
aebc6db
891bd38
45b6a61
891bd38
 
 
 
eb463f4
891bd38
 
eb463f4
891bd38
 
 
 
 
 
5cc9b3a
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
 
 
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
 
891bd38
 
 
 
 
 
5cc9b3a
 
 
 
 
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb463f4
891bd38
 
 
 
eb463f4
891bd38
eb463f4
 
 
 
891bd38
eb463f4
891bd38
eb463f4
 
891bd38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
print("Starting up. Please be patient...")

import os
import glob
import json
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import yt_dlp
import ffmpeg
import subprocess
import sys
import io
import wave
from datetime import datetime
from fairseq import checkpoint_utils
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
from config import Config
from edgetts_db import tts_order_voice

#fuck intel
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces"
#limitation=True
language_dict = tts_order_voice

authors = ["dacoolkid44", "Hijack", "Maki Ligon", "megaaziib", "Kit Lemonfoot", "yeey5", "Sui", "MahdeenSky"]

f0method_mode = []
if limitation is True:
    f0method_info = "PM is better for testing, RMVPE is better for finalized generations. (Default: PM)"
    f0method_mode = ["pm", "rmvpe"]
else:
    f0method_info = "PM is fast but low quality, crepe and harvest are slow but good quality, RMVPE is the best of both worlds. (Default: PM)"
    f0method_mode = ["pm", "crepe", "harvest", "rmvpe"]

#Eagerload VCs
print("Preloading VCs...")
vcArr=[]
vcArr.append(VC(32000, config))
vcArr.append(VC(40000, config))
vcArr.append(VC(48000, config))

def infer(name, path, index, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect):
    try:
        #Setup audio
        audio=None
        #Determine audio mode
        #TTS takes priority over uploads.
        #Uploads takes priority over paths.
        vc_audio_mode = ""
        #Edge-TTS
        if(tts_text):
            vc_audio_mode = "ETTS"
            if len(tts_text) > 250 and limitation:
                return "Text is too long.", None
            if tts_text is None or tts_voice is None or tts_text=="":
                return "You need to enter text and select a voice.", None
            voice = language_dict[tts_voice]
            try:
                asyncio.run(edge_tts.Communicate(tts_text, voice).save("tts.mp3"))
            except:
                print("Failed to get E-TTS handle. A restart may be needed soon.")
                return "ERROR: Failed to communicate with Edge-TTS. The Edge-TTS service may be down or cannot communicate. Please try another method or try again later.", None
            try:
                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
            except:
                return "ERROR: Invalid characters for the chosen TTS speaker. (Change your TTS speaker to one that supports your language!)", None
            duration = audio.shape[0] / sr
            if duration > 30 and limitation:
                return "Your text generated an audio that was too long.", None
            vc_input = "tts.mp3"
        #File upload
        elif(vc_upload):
            vc_audio_mode = "Upload"
            sampling_rate, audio = vc_upload
            duration = audio.shape[0] / sampling_rate
            if duration > 60 and limitation:
                return "Too long! Please upload an audio file that is less than 1 minute.", 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)
            tts_text = "Uploaded Audio"
        #YouTube or path
        elif(vc_input):
            audio, sr = librosa.load(vc_input, sr=16000, mono=True)
            vc_audio_mode = "YouTube"
            tts_text = "YouTube Audio"
        else:
            return "Please upload or choose some type of audio.", None  

        if audio is None:
            if vc_audio_mode == "ETTS":
                print("Failed to get E-TTS handle. A restart may be needed soon.")
                return "ERROR: Failed to obtain a correct response from Edge-TTS. The Edge-TTS service may be down or unable to communicate. Please try another method or try again later.", None
            return "ERROR: Unknown audio error. Please try again.", None
            
        times = [0, 0, 0]
        f0_up_key = int(f0_up_key)

        #Setup model
        cpt = torch.load(f"{path}", 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()
        vcIdx = int((tgt_sr/8000)-4)

        #Gen audio
        audio_opt = vcArr[vcIdx].pipeline(
            hubert_model,
            net_g,
            0,
            audio,
            vc_input,
            times,
            f0_up_key,
            f0_method,
            index,
            # file_big_npy,
            index_rate,
            if_f0,
            filter_radius,
            tgt_sr,
            resample_sr,
            rms_mix_rate,
            version,
            protect,
            f0_file=None,
        )
        info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
        print(f"Successful inference with model {name} | {tts_text} | {info}")
        del net_g, cpt
        return info, (tgt_sr, audio_opt)
    except:
        info = traceback.format_exc()
        print(info)
        return info, (None, None)

def load_model():
    categories = []
    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
        folder_info = json.load(f)
    for category_name, category_info in folder_info.items():
        if not category_info['enable']:
            continue
        category_title = category_info['title']
        category_folder = category_info['folder_path']
        models = []
        print(f"Creating category {category_title}...")
        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
            models_info = json.load(f)
        for character_name, info in models_info.items():
            if not info['enable']:
                continue
            model_title = info['title']
            model_name = info['model_path']
            model_author = info.get("author", None)
            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
            if info['feature_retrieval_library'] == "None":
                model_index = None
            if model_index:
               assert os.path.exists(model_index), f"Model {model_title} failed to load index."
            if not (model_author in authors or "/" in model_author or "&" in model_author):
                authors.append(model_author)
            model_path =  f"weights/{category_folder}/{character_name}/{model_name}"
            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
            model_version = cpt.get("version", "v1")
            print(f"Indexed model {model_title} by {model_author} ({model_version})")
            models.append((character_name, model_title, model_author, model_cover, model_version, model_path, model_index))
            del cpt
        categories.append([category_title, category_folder, models])
    return categories

def cut_vocal_and_inst(url, audio_provider, split_model):
    if url != "":
        if not os.path.exists("dl_audio"):
            os.mkdir("dl_audio")
        if audio_provider == "Youtube":
            ydl_opts = {
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'dl_audio/youtube_audio',
            }
            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                ydl.download([url])
            audio_path = "dl_audio/youtube_audio.wav"
        else:
            # Spotify doesnt work.
            # Need to find other solution soon.
            ''' 
            command = f"spotdl download {url} --output dl_audio/.wav"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            audio_path = "dl_audio/spotify_audio.wav"
            '''
        if split_model == "htdemucs":
            command = f"demucs --two-stems=vocals {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
        else:
            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
    else:
        raise gr.Error("URL Required!")
        return None, None, None, None

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()

if __name__ == '__main__':
    load_hubert()
    categories = load_model()
    voices = list(language_dict.keys())

    # Gradio preloading
    # Input and Upload
    vc_upload = gr.Audio(label="Upload or record an audio file", interactive=True)
    # Youtube
    vc_input = gr.Textbox(label="Input audio path", visible=False)
    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, value="Youtube", info="Select provider (Default: Youtube)")
    vc_link = gr.Textbox(label="Youtube URL", info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
    vc_split = gr.Button("Split Audio", variant="primary")
    vc_vocal_preview = gr.Audio(label="Vocal Preview")
    vc_inst_preview = gr.Audio(label="Instrumental Preview")
    vc_audio_preview = gr.Audio(label="Audio Preview")
    # TTS
    tts_text = gr.Textbox(label="TTS text", info="Text to speech input (There is a limit of 250 characters)", interactive=True)
    tts_voice = gr.Dropdown(label="Edge-TTS speaker", choices=voices, allow_custom_value=False, value="English-Ana (Female)", interactive=True)
    # Other settings
    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
    f0method0 = gr.Radio(
        label="Pitch extraction algorithm",
        info=f0method_info,
        choices=f0method_mode,
        value="pm",
        interactive=True
    )
    index_rate1 = gr.Slider(
        minimum=0,
        maximum=1,
        label="Retrieval feature ratio",
        info="Accent control. Too high will usually sound too robotic. (Default: 0.4)",
        value=0.4,
        interactive=True,
    )
    filter_radius0 = gr.Slider(
        minimum=0,
        maximum=7,
        label="Apply Median Filtering",
        info="The value represents the filter radius and can reduce breathiness.",
        value=1,
        step=1,
        interactive=True,
    )
    resample_sr0 = gr.Slider(
        minimum=0,
        maximum=48000,
        label="Resample the output audio",
        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling.",
        value=0,
        step=1,
        interactive=True,
    )
    rms_mix_rate0 = gr.Slider(
        minimum=0,
        maximum=1,
        label="Volume Envelope",
        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
        value=1,
        interactive=True,
    )
    protect0 = gr.Slider(
        minimum=0,
        maximum=0.5,
        label="Voice Protection",
        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
        value=0.23,
        step=0.01,
        interactive=True,
    )

    with gr.Blocks(theme=gr.themes.Base()) as app:
        gr.Markdown(
            "# <center> RVC Models\n"
            "### <center> Please credit the original model authors if you use this Space."
            "<center>Do no evil.\n\n"
            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19Eo2xO7EKcMqvJDc_yXrWmixuNA4NtEU)\n\n"
        )
        for (folder_title, folder, models) in categories:
            with gr.TabItem(folder_title):
                with gr.Tabs():
                    if not models:
                        gr.Markdown("# <center> No Model Loaded.")
                        gr.Markdown("## <center> Please add model or fix your model path.")
                        continue
                    for (name, title, author, cover, model_version, model_path, model_index) in models:
                        with gr.TabItem(name):
                            with gr.Row():
                                with gr.Column():
                                    gr.Markdown(
                                        '<div align="center">'
                                        f'<div>{title}</div>\n'+
                                        f'<div>RVC {model_version} Model</div>\n'+
                                        (f'<div>Model author: {author}</div>' if author else "")+
                                        (f'<img style="width:auto;height:300px;" src="file/{cover}"></img>' if cover else "")+
                                        '</div>'
                                    )
                                with gr.Column():
                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
                                    #This is a fucking stupid solution but Gradio refuses to pass in values unless I do this.
                                    vc_name = gr.Textbox(value=title, visible=False, interactive=False)
                                    vc_mp = gr.Textbox(value=model_path, visible=False, interactive=False)
                                    vc_mi = gr.Textbox(value=model_index, visible=False, interactive=False)
                                    vc_convert = gr.Button("Convert", variant="primary")

                                    vc_convert.click(
                                        fn=infer,
                                        inputs=[
                                            vc_name,
                                            vc_mp,
                                            vc_mi,
                                            vc_input, 
                                            vc_upload,
                                            tts_text,
                                            tts_voice,
                                            vc_transform0,
                                            f0method0,
                                            index_rate1,
                                            filter_radius0,
                                            resample_sr0,
                                            rms_mix_rate0,
                                            protect0
                                        ],
                                        outputs=[vc_log, vc_output]
                                    )

        with gr.Row():
            with gr.Column():
                with gr.Tab("Edge-TTS"):
                    tts_text.render()
                    tts_voice.render()
                with gr.Tab("Upload/Record"):
                    vc_input.render()
                    vc_upload.render()
                if(not limitation):
                    with gr.Tab("YouTube"):
                        vc_download_audio.render()
                        vc_link.render()
                        vc_split_model.render()
                        vc_split.render()
                        vc_vocal_preview.render()
                        vc_inst_preview.render()
                        vc_audio_preview.render()
            with gr.Column():
                vc_transform0.render()
                f0method0.render()
                index_rate1.render()
                with gr.Accordion("Advanced Options", open=False):
                    filter_radius0.render()
                    resample_sr0.render()
                    rms_mix_rate0.render()
                    protect0.render() 

        vc_split.click(
            fn=cut_vocal_and_inst, 
            inputs=[vc_link, vc_download_audio, vc_split_model], 
            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
        )
        
        authStr=", ".join(authors)
if limitation is True:
    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"])
else:
    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"], share=False)