Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import librosa | |
| import tensorflow as tf | |
| from huggingface_hub import from_pretrained_keras | |
| 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 | |
| return wave | |
| 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 = 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() |