Spaces:
Paused
Paused
File size: 6,521 Bytes
02768a2 de475ce 1f3a9b6 02768a2 de475ce 02768a2 1f3a9b6 02768a2 1f3a9b6 02768a2 731f5de e0a167e 731f5de e0a167e 1f3a9b6 731f5de 02768a2 e0a167e 1f3a9b6 731f5de 9b0f71a 731f5de e0a167e 02768a2 db8dccd 02768a2 674a3ea 02768a2 1f3a9b6 02768a2 1f3a9b6 02768a2 1f3a9b6 02768a2 1f3a9b6 1de0133 1f3a9b6 02768a2 1f3a9b6 02768a2 |
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 |
import re
import gradio as gr
import librosa
import numpy as np
from transformers import AutoTokenizer,ViTImageProcessor
from unidecode import unidecode
from models import *
tok = AutoTokenizer.from_pretrained("readerbench/RoBERT-base")
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
def preprocess(x):
"""Preprocess input string x"""
s = unidecode(x)
s = str.lower(s)
s = re.sub(r"\[[a-z]+\]","", s)
s = re.sub(r"\*","", s)
s = re.sub(r"[^a-zA-Z0-9]+"," ",s)
s = re.sub(r" +"," ",s)
s = re.sub(r"(.)\1+",r"\1",s)
return s
label_names = ["ABUSE", "INSULT", "OTHER", "PROFANITY"]
audio_label_names = ["Laughter", "Sigh", "Cough", "Throat clearing", "Sneeze", "Sniff"]
def ssl_predict(in_text, model_type):
"""main predict function"""
preprocessed = preprocess(in_text)
toks = tok(
preprocessed,
padding="max_length",
max_length=96,
truncation=True,
return_tensors="tf"
)
preds = None
if model_type == "fixmatch":
model = FixMatchTune(encoder_name="readerbench/RoBERT-base")
model.load_weights("./checkpoints/fixmatch_tune")
preds, _ = model([toks["input_ids"],toks["attention_mask"]], training=False)
elif model_type == "freematch":
model = FixMatchTune(encoder_name="andrei-saceleanu/ro-offense-freematch")
model.cls_head.load_weights("./checkpoints/freematch_tune")
preds, _ = model([toks["input_ids"],toks["attention_mask"]], training=False)
elif model_type == "mixmatch":
model = MixMatch(bert_model="andrei-saceleanu/ro-offense-mixmatch")
model.cls_head.load_weights("./checkpoints/mixmatch")
preds = model([toks["input_ids"],toks["attention_mask"]], training=False)
elif model_type == "contrastive_reg":
model = FixMatchTune(encoder_name="readerbench/RoBERT-base")
model.cls_head.load_weights("./checkpoints/contrastive")
preds, _ = model([toks["input_ids"],toks["attention_mask"]], training=False)
elif model_type == "label_propagation":
model = LPModel()
model.cls_head.load_weights("./checkpoints/label_prop")
preds = model([toks["input_ids"],toks["attention_mask"]], training=False)
probs = list(preds[0].numpy())
d = {}
for k, v in zip(label_names, probs):
d[k] = float(v)
return d
def ssl_predict2(audio_file, model_type):
"""main predict function"""
signal, sr = librosa.load(audio_file.name, sr=16000)
length = 5 * 16000
if len(signal) < length:
signal = np.pad(signal,(0,length-len(signal)),'constant')
else:
signal = signal[:length]
spectrogram = librosa.feature.melspectrogram(y=signal, sr=sr, n_mels=128)
spectrogram = librosa.power_to_db(S=spectrogram, ref=np.max)
spectrogram_min, spectrogram_max = spectrogram.min(), spectrogram.max()
spectrogram = (spectrogram - spectrogram_min) / (spectrogram_max - spectrogram_min)
spectrogram = spectrogram.astype("float32")
inputs = processor.preprocess(
np.repeat(spectrogram[np.newaxis,:,:,np.newaxis],3,-1),
image_mean=(-3.05,-3.05,-3.05),
image_std=(2.33,2.33,2.33),
return_tensors="tf"
)
preds = None
if model_type == "fixmatch":
model = AudioFixMatch(encoder_name="andrei-saceleanu/vit-base-fixmatch")
model.cls_head.load_weights("./checkpoints/audio_fixmatch")
preds, _ = model(inputs["pixel_values"], training=False)
elif model_type == "freematch":
model = AudioFixMatch(encoder_name="andrei-saceleanu/vit-base-freematch")
model.cls_head.load_weights("./checkpoints/audio_freematch")
preds, _ = model(inputs["pixel_values"], training=False)
elif model_type == "mixmatch":
model = AudioMixMatch(bert_model="andrei-saceleanu/vit-base-mixmatch")
model.cls_head.load_weights("./checkpoints/audio_mixmatch")
preds = model(inputs["pixel_values"], training=False)
probs = list(preds[0].numpy())
d = {}
for k, v in zip(audio_label_names, probs):
d[k] = float(v)
return d
with gr.Blocks() as ssl_interface:
with gr.Tab("Text (RO-Offense)"):
with gr.Row():
with gr.Column():
in_text = gr.Textbox(label="Input text")
model_list = gr.Dropdown(
choices=["fixmatch", "freematch", "mixmatch", "contrastive_reg", "label_propagation"],
max_choices=1,
label="Training method",
allow_custom_value=False,
info="Select trained model according to different SSL techniques from paper",
)
with gr.Row():
clear_btn = gr.Button(value="Clear")
submit_btn = gr.Button(value="Submit")
with gr.Column():
out_field = gr.Label(num_top_classes=4, label="Prediction")
submit_btn.click(
fn=ssl_predict,
inputs=[in_text, model_list],
outputs=[out_field]
)
clear_btn.click(
fn=lambda: [None for _ in range(2)],
inputs=None,
outputs=[in_text, out_field]
)
with gr.Tab("Audio (VocalSound)"):
with gr.Row():
with gr.Column():
audio_file = gr.File(
label="Input audio",
file_count="single",
file_types=["audio"]
)
model_list2 = gr.Dropdown(
choices=["fixmatch", "freematch", "mixmatch"],
max_choices=1,
label="Training method",
allow_custom_value=False,
info="Select trained model according to different SSL techniques from paper",
)
with gr.Row():
clear_btn2 = gr.Button(value="Clear")
submit_btn2 = gr.Button(value="Submit")
with gr.Column():
out_field2 = gr.Label(num_top_classes=6, label="Prediction")
submit_btn2.click(
fn=ssl_predict2,
inputs=[audio_file, model_list2],
outputs=[out_field2]
)
clear_btn2.click(
fn=lambda: [None for _ in range(2)],
inputs=None,
outputs=[audio_file, out_field2]
)
ssl_interface.launch(server_name="0.0.0.0", server_port=7860)
|