File size: 28,418 Bytes
ac403c1
 
 
 
4ac3fe7
ac403c1
4ac3fe7
 
ac403c1
4ac3fe7
ac403c1
 
4ac3fe7
5605d36
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b8a71c5
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91078a0
ac403c1
 
 
 
 
b67aeec
ac403c1
 
 
 
 
 
 
 
 
 
 
 
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
06bbc1f
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ee08064
6ca7e3b
 
 
 
06bbc1f
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4ac3fe7
 
 
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
da1b313
ac403c1
da1b313
ac403c1
 
 
4ac3fe7
ac403c1
4ac3fe7
ac403c1
 
 
 
 
 
 
 
 
 
 
608c140
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d57fb24
ac403c1
 
8d644e2
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6ca7e3b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
06bbc1f
6ca7e3b
 
 
 
 
 
 
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
4ac3fe7
 
ac403c1
 
 
 
 
4ac3fe7
 
ac403c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b67aeec
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
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
from cProfile import label
import dataclasses
from distutils.command.check import check
from doctest import Example
import gradio as gr
import os
import sys
import numpy as np
import logging
import torch
import pytorch_seed
import time


import math
import tempfile
from typing import Optional, Tuple, Union

import matplotlib.pyplot as plt
from loguru import logger
from PIL import Image
from torch import Tensor
from torchaudio.backend.common import AudioMetaData

from df import config
from df.enhance import enhance, init_df, load_audio, save_audio
from df.io import resample

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
map_location=torch.device('cpu')
model, df, _ = init_df("./DeepFilterNet2", config_allow_defaults=True)
model = model.to(device=device).eval()

fig_noisy: plt.Figure
fig_enh: plt.Figure
ax_noisy: plt.Axes
ax_enh: plt.Axes
fig_noisy, ax_noisy = plt.subplots(figsize=(15.2, 4))
fig_noisy.set_tight_layout(True)
fig_enh, ax_enh = plt.subplots(figsize=(15.2, 4))
fig_enh.set_tight_layout(True)

NOISES = {
    "None": None,
    "Kitchen": "samples/dkitchen.wav",
    "Living Room": "samples/dliving.wav",
    "River": "samples/nriver.wav",
    "Cafe": "samples/scafe.wav",
}


from xml.sax import saxutils
from bark.api import generate_with_settings
from bark.api import save_as_prompt
from util.settings import Settings
#import nltk

from bark import SAMPLE_RATE
from cloning.clonevoice import clone_voice
from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode
from scipy.io.wavfile import write as write_wav
from util.parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
from datetime import datetime
from tqdm.auto import tqdm
from util.helper import create_filename, add_id3_tag
from swap_voice import swap_voice_from_audio
from training.training_prepare import prepare_semantics_from_text, prepare_wavs_from_semantics
from training.train import training_prepare_files, train


# Denoise

def mix_at_snr(clean, noise, snr, eps=1e-10):
    """Mix clean and noise signal at a given SNR.
    Args:
        clean: 1D Tensor with the clean signal to mix.
        noise: 1D Tensor of shape.
        snr: Signal to noise ratio.
    Returns:
        clean: 1D Tensor with gain changed according to the snr.
        noise: 1D Tensor with the combined noise channels.
        mix: 1D Tensor with added clean and noise signals.
    """
    clean = torch.as_tensor(clean).mean(0, keepdim=True)
    noise = torch.as_tensor(noise).mean(0, keepdim=True)
    if noise.shape[1] < clean.shape[1]:
        noise = noise.repeat((1, int(math.ceil(clean.shape[1] / noise.shape[1]))))
    max_start = int(noise.shape[1] - clean.shape[1])
    start = torch.randint(0, max_start, ()).item() if max_start > 0 else 0
    logger.debug(f"start: {start}, {clean.shape}")
    noise = noise[:, start : start + clean.shape[1]]
    E_speech = torch.mean(clean.pow(2)) + eps
    E_noise = torch.mean(noise.pow(2))
    K = torch.sqrt((E_noise / E_speech) * 10 ** (snr / 10) + eps)
    noise = noise / K
    mixture = clean + noise
    logger.debug("mixture: {mixture.shape}")
    assert torch.isfinite(mixture).all()
    max_m = mixture.abs().max()
    if max_m > 1:
        logger.warning(f"Clipping detected during mixing. Reducing gain by {1/max_m}")
        clean, noise, mixture = clean / max_m, noise / max_m, mixture / max_m
    return clean, noise, mixture


def load_audio_gradio(
    audio_or_file: Union[None, str, Tuple[int, np.ndarray]], sr: int
) -> Optional[Tuple[Tensor, AudioMetaData]]:
    if audio_or_file is None:
        return None
    if isinstance(audio_or_file, str):
        if audio_or_file.lower() == "none":
            return None
        # First try default format
        audio, meta = load_audio(audio_or_file, sr)
    else:
        meta = AudioMetaData(-1, -1, -1, -1, "")
        assert isinstance(audio_or_file, (tuple, list))
        meta.sample_rate, audio_np = audio_or_file
        # Gradio documentation says, the shape is [samples, 2], but apparently sometimes its not.
        audio_np = audio_np.reshape(audio_np.shape[0], -1).T
        if audio_np.dtype == np.int16:
            audio_np = (audio_np / (1 << 15)).astype(np.float32)
        elif audio_np.dtype == np.int32:
            audio_np = (audio_np / (1 << 31)).astype(np.float32)
        audio = resample(torch.from_numpy(audio_np), meta.sample_rate, sr)
    return audio, meta


def demo_fn(speech_upl: str, noise_type: str, snr: int, mic_input: str):
    if mic_input:
        speech_upl = mic_input
    sr = config("sr", 48000, int, section="df")
    logger.info(f"Got parameters speech_upl: {speech_upl}, noise: {noise_type}, snr: {snr}")
    snr = int(snr)
    noise_fn = NOISES[noise_type]
    meta = AudioMetaData(-1, -1, -1, -1, "")
    max_s = 1000  # limit to 10 seconds
    if speech_upl is not None:
        sample, meta = load_audio(speech_upl, sr)
        max_len = max_s * sr
        if sample.shape[-1] > max_len:
            start = torch.randint(0, sample.shape[-1] - max_len, ()).item()
            sample = sample[..., start : start + max_len]
    else:
        sample, meta = load_audio("samples/p232_013_clean.wav", sr)
        sample = sample[..., : max_s * sr]
    if sample.dim() > 1 and sample.shape[0] > 1:
        assert (
            sample.shape[1] > sample.shape[0]
        ), f"Expecting channels first, but got {sample.shape}"
        sample = sample.mean(dim=0, keepdim=True)
    logger.info(f"Loaded sample with shape {sample.shape}")
    if noise_fn is not None:
        noise, _ = load_audio(noise_fn, sr)  # type: ignore
        logger.info(f"Loaded noise with shape {noise.shape}")
        _, _, sample = mix_at_snr(sample, noise, snr)
    logger.info("Start denoising audio")
    enhanced = enhance(model, df, sample)
    logger.info("Denoising finished")
    lim = torch.linspace(0.0, 1.0, int(sr * 0.15)).unsqueeze(0)
    lim = torch.cat((lim, torch.ones(1, enhanced.shape[1] - lim.shape[1])), dim=1)
    enhanced = enhanced * lim
    if meta.sample_rate != sr:
        enhanced = resample(enhanced, sr, meta.sample_rate)
        sample = resample(sample, sr, meta.sample_rate)
        sr = meta.sample_rate
    enhanced_wav = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
    save_audio(enhanced_wav, enhanced, sr)
    logger.info(f"saved audios: {enhanced_wav}")
    ax_noisy.clear()
    ax_enh.clear()
    # noisy_wav = gr.make_waveform(noisy_fn, bar_count=200)
    # enh_wav = gr.make_waveform(enhanced_fn, bar_count=200)
    return enhanced_wav


def specshow(
    spec,
    ax=None,
    title=None,
    xlabel=None,
    ylabel=None,
    sr=48000,
    n_fft=None,
    hop=None,
    t=None,
    f=None,
    vmin=-100,
    vmax=0,
    xlim=None,
    ylim=None,
    cmap="inferno",
):
    """Plots a spectrogram of shape [F, T]"""
    spec_np = spec.cpu().numpy() if isinstance(spec, torch.Tensor) else spec
    if ax is not None:
        set_title = ax.set_title
        set_xlabel = ax.set_xlabel
        set_ylabel = ax.set_ylabel
        set_xlim = ax.set_xlim
        set_ylim = ax.set_ylim
    else:
        ax = plt
        set_title = plt.title
        set_xlabel = plt.xlabel
        set_ylabel = plt.ylabel
        set_xlim = plt.xlim
        set_ylim = plt.ylim
    if n_fft is None:
        if spec.shape[0] % 2 == 0:
            n_fft = spec.shape[0] * 2
        else:
            n_fft = (spec.shape[0] - 1) * 2
    hop = hop or n_fft // 4
    if t is None:
        t = np.arange(0, spec_np.shape[-1]) * hop / sr
    if f is None:
        f = np.arange(0, spec_np.shape[0]) * sr // 2 / (n_fft // 2) / 1000
    im = ax.pcolormesh(
        t, f, spec_np, rasterized=True, shading="auto", vmin=vmin, vmax=vmax, cmap=cmap
    )
    if title is not None:
        set_title(title)
    if xlabel is not None:
        set_xlabel(xlabel)
    if ylabel is not None:
        set_ylabel(ylabel)
    if xlim is not None:
        set_xlim(xlim)
    if ylim is not None:
        set_ylim(ylim)
    return im


def spec_im(
    audio: torch.Tensor,
    figsize=(15, 5),
    colorbar=False,
    colorbar_format=None,
    figure=None,
    labels=True,
    **kwargs,
) -> Image:
    audio = torch.as_tensor(audio)
    if labels:
        kwargs.setdefault("xlabel", "Time [s]")
        kwargs.setdefault("ylabel", "Frequency [Hz]")
    n_fft = kwargs.setdefault("n_fft", 1024)
    hop = kwargs.setdefault("hop", 512)
    w = torch.hann_window(n_fft, device=audio.device)
    spec = torch.stft(audio, n_fft, hop, window=w, return_complex=False)
    spec = spec.div_(w.pow(2).sum())
    spec = torch.view_as_complex(spec).abs().clamp_min(1e-12).log10().mul(10)
    kwargs.setdefault("vmax", max(0.0, spec.max().item()))

    if figure is None:
        figure = plt.figure(figsize=figsize)
        figure.set_tight_layout(True)
    if spec.dim() > 2:
        spec = spec.squeeze(0)
    im = specshow(spec, **kwargs)
    if colorbar:
        ckwargs = {}
        if "ax" in kwargs:
            if colorbar_format is None:
                if kwargs.get("vmin", None) is not None or kwargs.get("vmax", None) is not None:
                    colorbar_format = "%+2.0f dB"
            ckwargs = {"ax": kwargs["ax"]}
        plt.colorbar(im, format=colorbar_format, **ckwargs)
    figure.canvas.draw()
    return Image.frombytes("RGB", figure.canvas.get_width_height(), figure.canvas.tostring_rgb())


def toggle(choice):
    if choice == "mic":
        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
    else:
        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)

# Bark

settings = Settings('config.yaml')

def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)):
    # Chunk the text into smaller pieces then combine the generated audio

    # generation settings
    if selected_speaker == 'None':
        selected_speaker = None

    voice_name = selected_speaker

    if text == None or len(text) < 1:
       if selected_speaker == None:
            raise gr.Error('No text entered!')

       # Extract audio data from speaker if no text and speaker selected
       voicedata = _load_history_prompt(voice_name)
       audio_arr = codec_decode(voicedata["fine_prompt"])
       result = create_filename(settings.output_folder_path, "None", "extract",".wav")
       save_wav(audio_arr, result)
       return result

    if batchcount < 1:
        batchcount = 1


    silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16)  # quarter second of silence
    silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # half a second of silence
    use_last_generation_as_history = "Use last generation as history" in complete_settings
    save_last_generation = "Save generation as Voice" in complete_settings
    for l in range(batchcount):
        currentseed = seed
        if seed != None and seed > 2**32 - 1:
            logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random")
            currentseed = None
        if currentseed == None or currentseed <= 0:
            currentseed = np.random.default_rng().integers(1, 2**32 - 1)
        assert(0 < currentseed and currentseed < 2**32)

        progress(0, desc="Generating")

        full_generation = None

        all_parts = []
        complete_text = ""
        text = text.lstrip()
        if is_ssml(text):
            list_speak = create_clips_from_ssml(text)
            prev_speaker = None
            for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
                selected_speaker = clip[0]
                # Add pause break between speakers
                if i > 0 and selected_speaker != prev_speaker:
                    all_parts += [silencelong.copy()]
                prev_speaker = selected_speaker
                text = clip[1]
                text = saxutils.unescape(text)
                if selected_speaker == "None":
                    selected_speaker = None

                print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
                complete_text += text
                with pytorch_seed.SavedRNG(currentseed):
                    audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
                    currentseed = torch.random.initial_seed()
                if len(list_speak) > 1:
                    filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
                    save_wav(audio_array, filename)
                    add_id3_tag(filename, text, selected_speaker, currentseed)

                all_parts += [audio_array]
        else:
            texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)
            for i, text in tqdm(enumerate(texts), total=len(texts)):
                print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
                complete_text += text
                if quick_generation == True:
                    with pytorch_seed.SavedRNG(currentseed):
                        audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
                        currentseed = torch.random.initial_seed()
                else:
                    full_output = use_last_generation_as_history or save_last_generation
                    if full_output:
                        full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)
                    else:
                        audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)

                # Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format  
                # audio_array = (audio_array * 32767).astype(np.int16)

                if len(texts) > 1:
                    filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
                    save_wav(audio_array, filename)
                    add_id3_tag(filename, text, selected_speaker, currentseed)

                if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):
                    # save to npz
                    voice_name = create_filename(settings.output_folder_path, seed, "audioclip", ".npz")
                    save_as_prompt(voice_name, full_generation)
                    if use_last_generation_as_history:
                        selected_speaker = voice_name

                all_parts += [audio_array]
                # Add short pause between sentences
                if text[-1] in "!?.\n" and i > 1:
                    all_parts += [silenceshort.copy()]

        # save & play audio
        result = create_filename(settings.output_folder_path, currentseed, "final",".wav")
        save_wav(np.concatenate(all_parts), result)
        # write id3 tag with text truncated to 60 chars, as a precaution...
        add_id3_tag(result, complete_text, selected_speaker, currentseed)

    return result



def save_wav(audio_array, filename):
    write_wav(filename, SAMPLE_RATE, audio_array)

def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
    np.savez_compressed(
        filename,
        semantic_prompt=semantic_prompt,
        coarse_prompt=coarse_prompt,
        fine_prompt=fine_prompt
    )
    

def on_quick_gen_changed(checkbox):
    if checkbox == False:
        return gr.CheckboxGroup.update(visible=True)
    return gr.CheckboxGroup.update(visible=False)

def delete_output_files(checkbox_state):
    if checkbox_state:
        outputs_folder = os.path.join(os.getcwd(), settings.output_folder_path)
        if os.path.exists(outputs_folder):
            purgedir(outputs_folder)
    return False


# https://stackoverflow.com/a/54494779
def purgedir(parent):
    for root, dirs, files in os.walk(parent):                                      
        for item in files:
            # Delete subordinate files                                                 
            filespec = os.path.join(root, item)
            os.unlink(filespec)
        for item in dirs:
            # Recursively perform this operation for subordinate directories   
            purgedir(os.path.join(root, item))

def convert_text_to_ssml(text, selected_speaker):
    return build_ssml(text, selected_speaker)


def training_prepare(selected_step, num_text_generations, progress=gr.Progress(track_tqdm=True)):
    if selected_step == prepare_training_list[0]:
        prepare_semantics_from_text()
    else:
        prepare_wavs_from_semantics()
    return None


def start_training(save_model_epoch, max_epochs, progress=gr.Progress(track_tqdm=True)):
    training_prepare_files("./training/data/", "./training/data/checkpoint/hubert_base_ls960.pt")
    train("./training/data/", save_model_epoch, max_epochs)
    return None



def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):
    settings.selected_theme = themes
    settings.server_name = input_server_name
    settings.server_port = input_server_port
    settings.server_share = input_server_public
    settings.input_text_desired_length = input_desired_len
    settings.input_text_max_length = input_max_len
    settings.silence_sentence = input_silence_break
    settings.silence_speaker = input_silence_speaker
    settings.save()

def restart():
    global restart_server
    restart_server = True


def create_version_html():
    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
    versions_html = f"""
python: <span title="{sys.version}">{python_version}</span>
โ€‚โ€ขโ€‚
torch: {getattr(torch, '__long_version__',torch.__version__)}
โ€‚โ€ขโ€‚
gradio: {gr.__version__}
"""
    return versions_html

    

logger = logging.getLogger(__name__)
APPTITLE = "Bark Voice Cloning UI"


autolaunch = False

if len(sys.argv) > 1:
    autolaunch = "-autolaunch" in sys.argv

if torch.cuda.is_available() == False:
    os.environ['BARK_FORCE_CPU'] = 'True'
    logger.warning("No CUDA detected, fallback to CPU!")

print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
print(f'autolaunch={autolaunch}\n\n')

#print("Updating nltk\n")
#nltk.download('punkt')

print("Preloading Models\n")
preload_models()

available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]
tokenizer_language_list = ["de","en", "pl"]
prepare_training_list = ["Step 1: Semantics from Text","Step 2: WAV from Semantics"]

seed = -1
server_name = settings.server_name
if len(server_name) < 1:
    server_name = None
server_port = settings.server_port
if server_port <= 0:
    server_port = None
global run_server
global restart_server

run_server = True

while run_server:
    # Collect all existing speakers/voices in dir
    speakers_list = []

    for root, dirs, files in os.walk("./bark/assets/prompts"):
        for file in files:
            if file.endswith(".npz"):
                pathpart = root.replace("./bark/assets/prompts", "")
                name = os.path.join(pathpart, file[:-4])
                if name.startswith("/") or name.startswith("\\"):
                     name = name[1:]
                speakers_list.append(name)

    speakers_list = sorted(speakers_list, key=lambda x: x.lower())
    speakers_list.insert(0, 'None')

    print(f'Launching {APPTITLE} Server')

    # Create Gradio Blocks

    with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui:
        gr.Markdown("# <center>๐Ÿถ๐ŸŽถโญ - Bark Voice Cloning</center>")
        gr.Markdown("## <center>๐Ÿค— - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>")
        gr.Markdown("### <center>๐ŸŽก - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)</center>")
        gr.Markdown(f""" You can duplicate and use it with a GPU: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>
                         or open in [Colab](https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning.ipynb) for quick start ๐ŸŒŸ P.S. Voice cloning needs a GPU, but TTS doesn't ๐Ÿ˜„
                    """)

        with gr.Tab("๐ŸŽ™๏ธ - Clone Voice"):
            with gr.Row():
                input_audio_filename = gr.Audio(label="Input audio.wav", source="upload", type="filepath")
            #transcription_text = gr.Textbox(label="Transcription Text", lines=1, placeholder="Enter Text of your Audio Sample here...")
            with gr.Row():
                with gr.Column():
                    initialname = "/home/user/app/bark/assets/prompts/file"
                    output_voice = gr.Textbox(label="Filename of trained Voice (do not change the initial name)", lines=1, placeholder=initialname, value=initialname, visible=False)
                with gr.Column():
                    tokenizerlang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1], visible=False)
            with gr.Row():
                clone_voice_button = gr.Button("Create Voice", variant="primary")
            with gr.Row():
                dummy = gr.Text(label="Progress")
                npz_file = gr.File(label=".npz file")
            speakers_list.insert(0, npz_file) # add prompt

        with gr.Tab("๐ŸŽต - TTS"):
            with gr.Row():
                with gr.Column():
                    placeholder = "Enter text here."
                    input_text = gr.Textbox(label="Input Text", lines=4, placeholder=placeholder)
                    convert_to_ssml_button = gr.Button("Convert Input Text to SSML")
                with gr.Column():
                        seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
                        batchcount = gr.Number(label="Batch count", precision=0, value=1)
           
            with gr.Row():
                with gr.Column():
                    gr.Markdown("[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
                    speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose โ€œfileโ€ if you wanna use the custom voice)")
                    
                with gr.Column():
                    text_temp = gr.Slider(0.1, 1.0, value=0.6, label="Generation Temperature", info="1.0 more diverse, 0.1 more conservative")
                    waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")

            with gr.Row():
                with gr.Column():
                    quick_gen_checkbox = gr.Checkbox(label="Quick Generation", value=True)
                    settings_checkboxes = ["Use last generation as history", "Save generation as Voice"]
                    complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
                with gr.Column():
                    eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability")

            with gr.Row():
                with gr.Column():
                    tts_create_button = gr.Button("Generate", variant="primary")
                with gr.Column():
                    hidden_checkbox = gr.Checkbox(visible=False)
                    button_stop_generation = gr.Button("Stop generation")
            with gr.Row():
                output_audio = gr.Audio(label="Generated Audio", type="filepath")

            with gr.Row():
                with gr.Column():
                    radio = gr.Radio(
                        ["mic", "file"], value="file", label="How would you like to upload your audio?", visible=False
                    )
                    mic_input = gr.Mic(label="Input", type="filepath", visible=False)
                    audio_file = output_audio
                    inputs = [
                        audio_file,
                        gr.Dropdown(
                            label="Add background noise",
                            choices=list(NOISES.keys()),
                            value="None", visible =False,
                        ),
                        gr.Dropdown(
                            label="Noise Level (SNR)",
                            choices=["-5", "0", "10", "20"],
                            value="0", visible =False,
                        ),
                        mic_input,
                    ]
                    btn_denoise = gr.Button("Denoise", variant="primary")
                with gr.Column():
                    outputs = [
                        gr.Audio(type="filepath", label="Enhanced audio"),
                    ]
            btn_denoise.click(fn=demo_fn, inputs=inputs, outputs=outputs)
            radio.change(toggle, radio, [mic_input, audio_file])

        with gr.Tab("๐Ÿ”ฎ - Voice Conversion"):
            with gr.Row():
                 swap_audio_filename = gr.Audio(label="Input audio.wav to swap voice", source="upload", type="filepath")
            with gr.Row():
                 with gr.Column():
                     swap_tokenizer_lang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1])
                     swap_seed = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
                 with gr.Column():
                     speaker_swap = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose โ€œfileโ€ if you wanna use the custom voice)")
                     swap_batchcount = gr.Number(label="Batch count", precision=0, value=1)
            with gr.Row():
                swap_voice_button = gr.Button("Generate", variant="primary")
            with gr.Row():
                output_swap = gr.Audio(label="Generated Audio", type="filepath")

   
        quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
        gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio)
        button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])
        


        swap_voice_button.click(swap_voice_from_audio, inputs=[swap_audio_filename, speaker_swap, swap_tokenizer_lang, swap_seed, swap_batchcount], outputs=output_swap)
        clone_voice_button.click(clone_voice, inputs=[input_audio_filename, output_voice], outputs=[dummy, npz_file])


        restart_server = False
        try:
            barkgui.queue().launch(show_error=True)
        except:
            restart_server = True
            run_server = False
        try:
            while restart_server == False:
                time.sleep(1.0)
        except (KeyboardInterrupt, OSError):
            print("Keyboard interruption in main thread... closing server.")
            run_server = False
        barkgui.close()