Hugging Face's logo

Spaces:
JacobLinCool
/
MP-SENet
Running on Zero

App Files Community
MP-SENet
File size: 4,924 Bytes 
45b4605
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import os

os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"

import time
import librosa
import spaces
from librosa.display import specshow
import numpy as np
from accelerate import Accelerator
import matplotlib.pyplot as plt
import gradio as gr
from typing import Tuple
from MPSENet import MPSENet

accelerator = Accelerator()
device = accelerator.device
print(f"Using device: {device}")

model = MPSENet.from_pretrained("JacobLinCool/MP-SENet-DNS").to(device)


def plot_spec(y: np.ndarray, title: str = "Spectrogram") -> plt.Figure:
    y[np.isnan(y)] = 0
    y[np.isinf(y)] = 0
    stft = librosa.stft(
        y, n_fft=model.h.n_fft, hop_length=model.h.hop_size, win_length=model.h.win_size
    )
    D = librosa.amplitude_to_db(np.abs(stft), ref=np.max)

    fig = plt.figure(figsize=(10, 4))
    specshow(
        D,
        sr=model.sampling_rate,
        n_fft=model.h.n_fft,
        hop_length=model.h.hop_size,
        win_length=model.h.win_size,
        y_axis="linear",
        x_axis="time",
        cmap="viridis",
    )
    plt.title(title)
    plt.tight_layout()

    return fig


def plot_input(input: str) -> plt.Figure:
    wav, _ = librosa.load(input, sr=model.sampling_rate)
    return plot_spec(wav, title="Original Spectrogram")


def plot_output(output: Tuple[int, np.ndarray]) -> plt.Figure:
    wav = output[1].astype(np.float32) / 32768.0
    return plot_spec(wav, title="Processed Spectrogram")


def process_audio(
    input: str,
    segment_size_seconds: int,
) -> Tuple[Tuple[int, np.ndarray], np.ndarray, np.ndarray, str]:
    # Load the audio
    start_time = time.time()
    noisy_wav, sr = librosa.load(input, sr=model.sampling_rate)
    print(f"{noisy_wav.shape=}, {sr=}")
    print(f"Loaded audio in {time.time() - start_time:.2f} seconds")

    # Process the audio
    start_time = time.time()
    processed_wav, sr, notation = model(
        noisy_wav, segment_size=segment_size_seconds * 16000
    )
    print(f"{processed_wav.shape=}, {sr=}, {notation=}")
    print(f"Inference in {time.time() - start_time:.2f} seconds")

    return ((sr, processed_wav), "Processed.")


@spaces.GPU()
def run(input: str, segment_size_seconds: int):
    return process_audio(input, segment_size_seconds)


@spaces.GPU(duration=60 * 2)
def run2x(input: str, segment_size_seconds: int):
    return process_audio(input, segment_size_seconds)


@spaces.GPU(duration=60 * 4)
def run4x(input: str, segment_size_seconds: int):
    return process_audio(input, segment_size_seconds)


with gr.Blocks() as app:
    gr.Markdown(
        "# MP-SENet Speech Enhancement\n\n[MP-SENet](https://github.com/yxlu-0102/MP-SENet) with ZeroGPU support.\n"
        "> Package is available at [JacobLinCool/MPSENet](https://github.com/JacobLinCool/MPSENet)"
    )

    with gr.Row():
        with gr.Column():
            input = gr.Audio(
                label="Upload an audio file", type="filepath", show_download_button=True
            )
        with gr.Column():
            original_spec = gr.Plot(label="Original Spectrogram")

    with gr.Row():
        btn = gr.Button(value="Process", variant="primary")
    with gr.Row():
        info = gr.Markdown("Press the button to process the audio.")

    with gr.Row():
        with gr.Column():
            output = gr.Audio(
                label="Processed Audio", show_download_button=True
            )
        with gr.Column():
            processed_spec = gr.Plot(label="Processed Spectrogram")

    with gr.Accordion("Advanced Settings", open=False):
        segment_size = gr.Slider(
            minimum=1,
            maximum=20,
            value=10,
            step=1,
            label="Segment Size (seconds)",
            info="The audio will be processed in segments of this size. Larger segments take more memory but may give more consistent results.",
        )

    input.change(
        fn=plot_input,
        inputs=[input],
        outputs=[original_spec],
    )
    output.change(
        fn=plot_output,
        inputs=[output],
        outputs=[processed_spec],
    )

    btn.click(
        fn=run,
        inputs=[input, segment_size],
        outputs=[output, info],
        api_name="run",
    )

    gr.Examples(
        examples=[
            ["examples/p226_007.wav", 2],
            ["examples/p226_016.wav", 2],
            ["examples/p230_005.wav", 8],
            ["examples/p232_032.wav", 2],
            ["examples/p232_232.wav", 2],
        ],
        inputs=[input, segment_size],
    )

    btn2x = gr.Button(value="Process", variant="primary", visible=False)
    btn2x.click(
        fn=run2x,
        inputs=[input, segment_size],
        outputs=[output, info],
        api_name="run2x",
    )

    btn4x = gr.Button(value="Process", variant="primary", visible=False)
    btn4x.click(
        fn=run4x,
        inputs=[input, segment_size],
        outputs=[output, info],
        api_name="run4x",
    )

    app.launch()