Spaces:
Runtime error
Runtime error
Upload 2 files
Browse files- app.py +441 -0
- requirements.txt +15 -0
app.py
ADDED
@@ -0,0 +1,441 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) 2024 NVIDIA CORPORATION.
|
2 |
+
# Licensed under the MIT license.
|
3 |
+
|
4 |
+
import spaces
|
5 |
+
import gradio as gr
|
6 |
+
import pandas as pd
|
7 |
+
import torch
|
8 |
+
import os
|
9 |
+
import sys
|
10 |
+
|
11 |
+
# to import modules from parent_dir
|
12 |
+
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
|
13 |
+
sys.path.append(parent_dir)
|
14 |
+
|
15 |
+
from meldataset import get_mel_spectrogram, MAX_WAV_VALUE
|
16 |
+
from bigvgan import BigVGAN
|
17 |
+
import librosa
|
18 |
+
import numpy as np
|
19 |
+
from utils import plot_spectrogram
|
20 |
+
import PIL
|
21 |
+
|
22 |
+
if torch.cuda.is_available():
|
23 |
+
device = torch.device("cuda")
|
24 |
+
torch.backends.cudnn.benchmark = False
|
25 |
+
print(f"using GPU")
|
26 |
+
else:
|
27 |
+
device = torch.device("cpu")
|
28 |
+
print(f"using CPU")
|
29 |
+
|
30 |
+
|
31 |
+
def inference_gradio(input, model_choice): # Input is audio waveform in [T, channel]
|
32 |
+
sr, audio = input # Unpack input to sampling rate and audio itself
|
33 |
+
audio = np.transpose(audio) # Transpose to [channel, T] for librosa
|
34 |
+
audio = audio / MAX_WAV_VALUE # Convert int16 to float range used by BigVGAN
|
35 |
+
|
36 |
+
model = dict_model[model_choice]
|
37 |
+
|
38 |
+
if sr != model.h.sampling_rate: # Convert audio to model's sampling rate
|
39 |
+
audio = librosa.resample(audio, orig_sr=sr, target_sr=model.h.sampling_rate)
|
40 |
+
if len(audio.shape) == 2: # Stereo
|
41 |
+
audio = librosa.to_mono(audio) # Convert to mono if stereo
|
42 |
+
audio = librosa.util.normalize(audio) * 0.95
|
43 |
+
|
44 |
+
output, spec_gen = inference_model(
|
45 |
+
audio, model
|
46 |
+
) # Output is generated audio in ndarray, int16
|
47 |
+
|
48 |
+
spec_plot_gen = plot_spectrogram(spec_gen)
|
49 |
+
|
50 |
+
output_audio = (model.h.sampling_rate, output) # Tuple for gr.Audio output
|
51 |
+
|
52 |
+
buffer = spec_plot_gen.canvas.buffer_rgba()
|
53 |
+
output_image = PIL.Image.frombuffer(
|
54 |
+
"RGBA", spec_plot_gen.canvas.get_width_height(), buffer, "raw", "RGBA", 0, 1
|
55 |
+
)
|
56 |
+
|
57 |
+
return output_audio, output_image
|
58 |
+
|
59 |
+
|
60 |
+
@spaces.GPU(duration=120)
|
61 |
+
def inference_model(audio_input, model):
|
62 |
+
# Load model to device
|
63 |
+
model.to(device)
|
64 |
+
|
65 |
+
with torch.inference_mode():
|
66 |
+
wav = torch.FloatTensor(audio_input)
|
67 |
+
# Compute mel spectrogram from the ground truth audio
|
68 |
+
spec_gt = get_mel_spectrogram(wav.unsqueeze(0), model.h).to(device)
|
69 |
+
|
70 |
+
y_g_hat = model(spec_gt)
|
71 |
+
|
72 |
+
audio_gen = y_g_hat.squeeze().cpu()
|
73 |
+
spec_gen = get_mel_spectrogram(audio_gen.unsqueeze(0), model.h)
|
74 |
+
audio_gen = audio_gen.numpy() # [T], float [-1, 1]
|
75 |
+
audio_gen = (audio_gen * MAX_WAV_VALUE).astype("int16") # [T], int16
|
76 |
+
spec_gen = spec_gen.squeeze().numpy() # [C, T_frame]
|
77 |
+
|
78 |
+
# Unload to CPU
|
79 |
+
model.to("cpu")
|
80 |
+
# Delete GPU tensor
|
81 |
+
del spec_gt, y_g_hat
|
82 |
+
|
83 |
+
return audio_gen, spec_gen
|
84 |
+
|
85 |
+
|
86 |
+
css = """
|
87 |
+
a {
|
88 |
+
color: inherit;
|
89 |
+
text-decoration: underline;
|
90 |
+
}
|
91 |
+
.gradio-container {
|
92 |
+
font-family: 'IBM Plex Sans', sans-serif;
|
93 |
+
}
|
94 |
+
.gr-button {
|
95 |
+
color: white;
|
96 |
+
border-color: #000000;
|
97 |
+
background: #000000;
|
98 |
+
}
|
99 |
+
input[type='range'] {
|
100 |
+
accent-color: #000000;
|
101 |
+
}
|
102 |
+
.dark input[type='range'] {
|
103 |
+
accent-color: #dfdfdf;
|
104 |
+
}
|
105 |
+
.container {
|
106 |
+
max-width: 730px;
|
107 |
+
margin: auto;
|
108 |
+
padding-top: 1.5rem;
|
109 |
+
}
|
110 |
+
#gallery {
|
111 |
+
min-height: 22rem;
|
112 |
+
margin-bottom: 15px;
|
113 |
+
margin-left: auto;
|
114 |
+
margin-right: auto;
|
115 |
+
border-bottom-right-radius: .5rem !important;
|
116 |
+
border-bottom-left-radius: .5rem !important;
|
117 |
+
}
|
118 |
+
#gallery>div>.h-full {
|
119 |
+
min-height: 20rem;
|
120 |
+
}
|
121 |
+
.details:hover {
|
122 |
+
text-decoration: underline;
|
123 |
+
}
|
124 |
+
.gr-button {
|
125 |
+
white-space: nowrap;
|
126 |
+
}
|
127 |
+
.gr-button:focus {
|
128 |
+
border-color: rgb(147 197 253 / var(--tw-border-opacity));
|
129 |
+
outline: none;
|
130 |
+
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
|
131 |
+
--tw-border-opacity: 1;
|
132 |
+
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
|
133 |
+
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
|
134 |
+
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
|
135 |
+
--tw-ring-opacity: .5;
|
136 |
+
}
|
137 |
+
#advanced-btn {
|
138 |
+
font-size: .7rem !important;
|
139 |
+
line-height: 19px;
|
140 |
+
margin-top: 12px;
|
141 |
+
margin-bottom: 12px;
|
142 |
+
padding: 2px 8px;
|
143 |
+
border-radius: 14px !important;
|
144 |
+
}
|
145 |
+
#advanced-options {
|
146 |
+
margin-bottom: 20px;
|
147 |
+
}
|
148 |
+
.footer {
|
149 |
+
margin-bottom: 45px;
|
150 |
+
margin-top: 35px;
|
151 |
+
text-align: center;
|
152 |
+
border-bottom: 1px solid #e5e5e5;
|
153 |
+
}
|
154 |
+
.footer>p {
|
155 |
+
font-size: .8rem;
|
156 |
+
display: inline-block;
|
157 |
+
padding: 0 10px;
|
158 |
+
transform: translateY(10px);
|
159 |
+
background: white;
|
160 |
+
}
|
161 |
+
.dark .footer {
|
162 |
+
border-color: #303030;
|
163 |
+
}
|
164 |
+
.dark .footer>p {
|
165 |
+
background: #0b0f19;
|
166 |
+
}
|
167 |
+
.acknowledgments h4{
|
168 |
+
margin: 1.25em 0 .25em 0;
|
169 |
+
font-weight: bold;
|
170 |
+
font-size: 115%;
|
171 |
+
}
|
172 |
+
#container-advanced-btns{
|
173 |
+
display: flex;
|
174 |
+
flex-wrap: wrap;
|
175 |
+
justify-content: space-between;
|
176 |
+
align-items: center;
|
177 |
+
}
|
178 |
+
.animate-spin {
|
179 |
+
animation: spin 1s linear infinite;
|
180 |
+
}
|
181 |
+
@keyframes spin {
|
182 |
+
from {
|
183 |
+
transform: rotate(0deg);
|
184 |
+
}
|
185 |
+
to {
|
186 |
+
transform: rotate(360deg);
|
187 |
+
}
|
188 |
+
}
|
189 |
+
#share-btn-container {
|
190 |
+
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
|
191 |
+
margin-top: 10px;
|
192 |
+
margin-left: auto;
|
193 |
+
}
|
194 |
+
#share-btn {
|
195 |
+
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
|
196 |
+
}
|
197 |
+
#share-btn * {
|
198 |
+
all: unset;
|
199 |
+
}
|
200 |
+
#share-btn-container div:nth-child(-n+2){
|
201 |
+
width: auto !important;
|
202 |
+
min-height: 0px !important;
|
203 |
+
}
|
204 |
+
#share-btn-container .wrap {
|
205 |
+
display: none !important;
|
206 |
+
}
|
207 |
+
.gr-form{
|
208 |
+
flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
|
209 |
+
}
|
210 |
+
#prompt-container{
|
211 |
+
gap: 0;
|
212 |
+
}
|
213 |
+
#generated_id{
|
214 |
+
min-height: 700px
|
215 |
+
}
|
216 |
+
#setting_id{
|
217 |
+
margin-bottom: 12px;
|
218 |
+
text-align: center;
|
219 |
+
font-weight: 900;
|
220 |
+
}
|
221 |
+
"""
|
222 |
+
|
223 |
+
# Script for loading the models
|
224 |
+
|
225 |
+
LIST_MODEL_ID = [
|
226 |
+
"bigvgan_24khz_100band",
|
227 |
+
"bigvgan_base_24khz_100band",
|
228 |
+
"bigvgan_22khz_80band",
|
229 |
+
"bigvgan_base_22khz_80band",
|
230 |
+
"bigvgan_v2_22khz_80band_256x",
|
231 |
+
"bigvgan_v2_22khz_80band_fmax8k_256x",
|
232 |
+
"bigvgan_v2_24khz_100band_256x",
|
233 |
+
"bigvgan_v2_44khz_128band_256x",
|
234 |
+
"bigvgan_v2_44khz_128band_512x",
|
235 |
+
]
|
236 |
+
|
237 |
+
dict_model = {}
|
238 |
+
dict_config = {}
|
239 |
+
|
240 |
+
for model_name in LIST_MODEL_ID:
|
241 |
+
|
242 |
+
generator = BigVGAN.from_pretrained("nvidia/" + model_name)
|
243 |
+
generator.remove_weight_norm()
|
244 |
+
generator.eval()
|
245 |
+
|
246 |
+
dict_model[model_name] = generator
|
247 |
+
dict_config[model_name] = generator.h
|
248 |
+
|
249 |
+
# Script for Gradio UI
|
250 |
+
|
251 |
+
iface = gr.Blocks(css=css, title="BigVGAN - Demo")
|
252 |
+
|
253 |
+
with iface:
|
254 |
+
gr.HTML(
|
255 |
+
"""
|
256 |
+
<div style="text-align: center; max-width: 900px; margin: 0 auto;">
|
257 |
+
<div
|
258 |
+
style="
|
259 |
+
display: inline-flex;
|
260 |
+
align-items: center;
|
261 |
+
gap: 0.8rem;
|
262 |
+
font-size: 1.5rem;
|
263 |
+
"
|
264 |
+
>
|
265 |
+
<h1 style="font-weight: 700; margin-bottom: 7px; line-height: normal;">
|
266 |
+
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
|
267 |
+
</h1>
|
268 |
+
</div>
|
269 |
+
<p style="margin-bottom: 10px; font-size: 125%">
|
270 |
+
<a href="https://arxiv.org/abs/2206.04658">[Paper]</a> <a href="https://github.com/NVIDIA/BigVGAN">[Code]</a> <a href="https://bigvgan-demo.github.io/">[Demo]</a> <a href="https://research.nvidia.com/labs/adlr/projects/bigvgan/">[Project page]</a>
|
271 |
+
</p>
|
272 |
+
</div>
|
273 |
+
"""
|
274 |
+
)
|
275 |
+
gr.HTML(
|
276 |
+
"""
|
277 |
+
<div>
|
278 |
+
<h3>News</h3>
|
279 |
+
<p>[Jul 2024] We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:</p>
|
280 |
+
<ul>
|
281 |
+
<li>Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.</li>
|
282 |
+
<li>Improved discriminator and loss: BigVGAN-v2 is trained using a <a href="https://arxiv.org/abs/2311.14957" target="_blank">multi-scale sub-band CQT discriminator</a> and a <a href="https://arxiv.org/abs/2306.06546" target="_blank">multi-scale mel spectrogram loss</a>.</li>
|
283 |
+
<li>Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.</li>
|
284 |
+
<li>We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio. See the table below for the link.</li>
|
285 |
+
</ul>
|
286 |
+
</div>
|
287 |
+
"""
|
288 |
+
)
|
289 |
+
gr.HTML(
|
290 |
+
"""
|
291 |
+
<div>
|
292 |
+
<h3>Model Overview</h3>
|
293 |
+
BigVGAN is a universal neural vocoder model that generates audio waveforms using mel spectrogram as inputs.
|
294 |
+
<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800" style="margin-top: 20px; border-radius: 15px;"></center>
|
295 |
+
</div>
|
296 |
+
"""
|
297 |
+
)
|
298 |
+
with gr.Accordion("Input"):
|
299 |
+
|
300 |
+
model_choice = gr.Dropdown(
|
301 |
+
label="Select the model to use",
|
302 |
+
info="The default model is bigvgan_v2_24khz_100band_256x",
|
303 |
+
value="bigvgan_v2_24khz_100band_256x",
|
304 |
+
choices=[m for m in LIST_MODEL_ID],
|
305 |
+
interactive=True,
|
306 |
+
)
|
307 |
+
|
308 |
+
audio_input = gr.Audio(
|
309 |
+
label="Input Audio", elem_id="input-audio", interactive=True
|
310 |
+
)
|
311 |
+
|
312 |
+
button = gr.Button("Submit")
|
313 |
+
|
314 |
+
with gr.Accordion("Output"):
|
315 |
+
with gr.Column():
|
316 |
+
output_audio = gr.Audio(label="Output Audio", elem_id="output-audio")
|
317 |
+
output_image = gr.Image(
|
318 |
+
label="Output Mel Spectrogram", elem_id="output-image-gen"
|
319 |
+
)
|
320 |
+
|
321 |
+
button.click(
|
322 |
+
inference_gradio,
|
323 |
+
inputs=[audio_input, model_choice],
|
324 |
+
outputs=[output_audio, output_image],
|
325 |
+
concurrency_limit=10,
|
326 |
+
)
|
327 |
+
|
328 |
+
gr.Examples(
|
329 |
+
[
|
330 |
+
[
|
331 |
+
os.path.join(os.path.dirname(__file__), "examples/jensen_24k.wav"),
|
332 |
+
"bigvgan_v2_24khz_100band_256x",
|
333 |
+
],
|
334 |
+
[
|
335 |
+
os.path.join(os.path.dirname(__file__), "examples/libritts_24k.wav"),
|
336 |
+
"bigvgan_v2_24khz_100band_256x",
|
337 |
+
],
|
338 |
+
[
|
339 |
+
os.path.join(os.path.dirname(__file__), "examples/queen_24k.wav"),
|
340 |
+
"bigvgan_v2_24khz_100band_256x",
|
341 |
+
],
|
342 |
+
[
|
343 |
+
os.path.join(os.path.dirname(__file__), "examples/dance_24k.wav"),
|
344 |
+
"bigvgan_v2_24khz_100band_256x",
|
345 |
+
],
|
346 |
+
[
|
347 |
+
os.path.join(os.path.dirname(__file__), "examples/megalovania_24k.wav"),
|
348 |
+
"bigvgan_v2_24khz_100band_256x",
|
349 |
+
],
|
350 |
+
[
|
351 |
+
os.path.join(os.path.dirname(__file__), "examples/hifitts_44k.wav"),
|
352 |
+
"bigvgan_v2_44khz_128band_256x",
|
353 |
+
],
|
354 |
+
[
|
355 |
+
os.path.join(os.path.dirname(__file__), "examples/musdbhq_44k.wav"),
|
356 |
+
"bigvgan_v2_44khz_128band_256x",
|
357 |
+
],
|
358 |
+
[
|
359 |
+
os.path.join(os.path.dirname(__file__), "examples/musiccaps1_44k.wav"),
|
360 |
+
"bigvgan_v2_44khz_128band_256x",
|
361 |
+
],
|
362 |
+
[
|
363 |
+
os.path.join(os.path.dirname(__file__), "examples/musiccaps2_44k.wav"),
|
364 |
+
"bigvgan_v2_44khz_128band_256x",
|
365 |
+
],
|
366 |
+
],
|
367 |
+
fn=inference_gradio,
|
368 |
+
inputs=[audio_input, model_choice],
|
369 |
+
outputs=[output_audio, output_image],
|
370 |
+
)
|
371 |
+
|
372 |
+
# Define the data for the table
|
373 |
+
data = {
|
374 |
+
"Model Name": [
|
375 |
+
"bigvgan_v2_44khz_128band_512x",
|
376 |
+
"bigvgan_v2_44khz_128band_256x",
|
377 |
+
"bigvgan_v2_24khz_100band_256x",
|
378 |
+
"bigvgan_v2_22khz_80band_256x",
|
379 |
+
"bigvgan_v2_22khz_80band_fmax8k_256x",
|
380 |
+
"bigvgan_24khz_100band",
|
381 |
+
"bigvgan_base_24khz_100band",
|
382 |
+
"bigvgan_22khz_80band",
|
383 |
+
"bigvgan_base_22khz_80band",
|
384 |
+
],
|
385 |
+
"Sampling Rate": [
|
386 |
+
"44 kHz",
|
387 |
+
"44 kHz",
|
388 |
+
"24 kHz",
|
389 |
+
"22 kHz",
|
390 |
+
"22 kHz",
|
391 |
+
"24 kHz",
|
392 |
+
"24 kHz",
|
393 |
+
"22 kHz",
|
394 |
+
"22 kHz",
|
395 |
+
],
|
396 |
+
"Mel band": [128, 128, 100, 80, 80, 100, 100, 80, 80],
|
397 |
+
"fmax": [22050, 22050, 12000, 11025, 8000, 12000, 12000, 8000, 8000],
|
398 |
+
"Upsampling Ratio": [512, 256, 256, 256, 256, 256, 256, 256, 256],
|
399 |
+
"Parameters": [
|
400 |
+
"122M",
|
401 |
+
"112M",
|
402 |
+
"112M",
|
403 |
+
"112M",
|
404 |
+
"112M",
|
405 |
+
"112M",
|
406 |
+
"14M",
|
407 |
+
"112M",
|
408 |
+
"14M",
|
409 |
+
],
|
410 |
+
"Dataset": [
|
411 |
+
"Large-scale Compilation",
|
412 |
+
"Large-scale Compilation",
|
413 |
+
"Large-scale Compilation",
|
414 |
+
"Large-scale Compilation",
|
415 |
+
"Large-scale Compilation",
|
416 |
+
"LibriTTS",
|
417 |
+
"LibriTTS",
|
418 |
+
"LibriTTS + VCTK + LJSpeech",
|
419 |
+
"LibriTTS + VCTK + LJSpeech",
|
420 |
+
],
|
421 |
+
"Fine-Tuned": ["No", "No", "No", "No", "No", "No", "No", "No", "No"],
|
422 |
+
}
|
423 |
+
|
424 |
+
base_url = "https://huggingface.co/nvidia/"
|
425 |
+
|
426 |
+
df = pd.DataFrame(data)
|
427 |
+
df["Model Name"] = df["Model Name"].apply(
|
428 |
+
lambda x: f'<a href="{base_url}{x}">{x}</a>'
|
429 |
+
)
|
430 |
+
|
431 |
+
html_table = gr.HTML(
|
432 |
+
f"""
|
433 |
+
<div style="text-align: center;">
|
434 |
+
{df.to_html(index=False, escape=False, classes='border="1" cellspacing="0" cellpadding="5" style="margin-left: auto; margin-right: auto;')}
|
435 |
+
<p><b>NOTE: The v1 models are trained using speech audio datasets ONLY! (24kHz models: LibriTTS, 22kHz models: LibriTTS + VCTK + LJSpeech).</b></p>
|
436 |
+
</div>
|
437 |
+
"""
|
438 |
+
)
|
439 |
+
|
440 |
+
iface.queue()
|
441 |
+
iface.launch()
|
requirements.txt
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch
|
2 |
+
numpy
|
3 |
+
librosa>=0.8.1
|
4 |
+
scipy
|
5 |
+
tensorboard
|
6 |
+
soundfile
|
7 |
+
matplotlib
|
8 |
+
pesq
|
9 |
+
auraloss
|
10 |
+
tqdm
|
11 |
+
nnAudio
|
12 |
+
ninja
|
13 |
+
huggingface_hub>=0.23.4
|
14 |
+
gradio>=4.38.1
|
15 |
+
spaces>=0.28.3
|