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import gradio as gr
import librosa
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
from itertools import groupby
import numpy as np
model = from_pretrained_keras("CXDJY/snore_ai")
def load_audio_to_tensor(filename):
audio, sampling_rate = librosa.load(filename, sr=None, mono=True) # load audio and convert to mono
wave = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) # resample to 16KHz
rms = librosa.feature.rms(y=audio)[0] # get root mean square of audio
volume = np.mean(rms) # get volume of audio
return wave, volume
def preprocess_mp3(sample, index):
sample = sample[0]
sample = tf.cast(sample, tf.float32)
zero_padding = tf.zeros([16000] - tf.shape(sample), dtype=tf.float32)
wave = tf.concat([zero_padding, sample], 0)
spectrogram = tf.signal.stft(wave, frame_length=320, frame_step=32)
spectrogram = tf.abs(spectrogram)
spectrogram = tf.expand_dims(spectrogram, axis=2)
return spectrogram
def greet(name):
wave, volume = load_audio_to_tensor(name)
# power = sum(wave * 2) / len(wave) # audio signal power
# SNR = 3.5 # signal-to-noise ratio
# SNR_linear = 10 ** (SNR / 10) # convert SNR to linear scale
# noise_power = power / SNR_linear # noise power
# # add noise to audio to simulate environment
# noise = np.random.normal(0, noise_power ** 0.5, wave.shape) # generate noise
# wave = (wave + noise) * 32768.0 # add noise to the audio signal
# tensor_wave = tf.convert_to_tensor(wave, dtype=tf.float32) # convert to tensor
# min_wave = min(wave)
if len(wave) > 16000:
sequence_stride = 16000
else:
sequence_stride = 16000-1
# create audio slices
audio_slices = tf.keras.utils.timeseries_dataset_from_array(wave, wave, sequence_length=16000, sequence_stride=sequence_stride, batch_size=1)
samples, index = audio_slices.as_numpy_iterator().next()
audio_slices = audio_slices.map(preprocess_mp3)
audio_slices = audio_slices.batch(64)
# model = from_pretrained_keras("CXDJY/snore_ai")
yhat = model.predict(audio_slices)
yhat = [1 if prediction > 0.99 else 0 for prediction in yhat]
yhat1 = [key for key, group in groupby(yhat)]
return yhat1
iface = gr.Interface(fn=greet, inputs="file", outputs="text")
# iface = gr.Interface(fn=greet, inputs="audio", outputs="text")
iface.launch() |