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| # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb. | |
| # %% auto 0 | |
| __all__ = ['data', 'audios', 'metadata', 'to_consider', 'processed_metadata', 'repo_id', 'learner', 'categories', 'title', | |
| 'description', 'mic', 'label', 'examples', 'intf', 'process_audio_exists', 'load_x', 'load_label_tfm', | |
| 'classify_audio'] | |
| # %% app.ipynb 1 | |
| import torch | |
| import gradio as gr | |
| from gradio import CSVLogger | |
| from fastai.vision.all import * | |
| import torchaudio | |
| import torchaudio.transforms as T | |
| import warnings | |
| from huggingface_hub import from_pretrained_fastai | |
| # %% app.ipynb 2 | |
| warnings.filterwarnings("ignore") | |
| # %% app.ipynb 3 | |
| def process_audio_exists(audio): | |
| slice_name = audio.name | |
| # check if slice name exists in new metadata file | |
| row = processed_metadata.loc[processed_metadata['slice_file_name'] == slice_name].index.any() | |
| return row | |
| # %% app.ipynb 4 | |
| data = Path('examples') | |
| audios = get_files(data, extensions='.wav') | |
| metadata = pd.read_csv('UrbanSound8K.csv') | |
| to_consider = ['siren', 'street_music', 'children_playing', 'dog_bark', 'car_horn'] | |
| processed_metadata = metadata.loc[metadata['class'].isin(to_consider)] | |
| processed_metadata.loc[processed_metadata['class'] == 'siren', 'classID'] = 4 | |
| processed_metadata.loc[processed_metadata['class'] == 'street_music', 'classID'] = 0 | |
| # %% app.ipynb 5 | |
| class load_x(Transform): | |
| def __init__(self): | |
| self.sr = 44100 | |
| self.max_ms = 4000 | |
| self.channels = 2 | |
| # self.transform = transform | |
| def rechannel(self, waveform, sr): | |
| if (waveform.shape[0] == self.channels): | |
| # no rechanneling needed | |
| return waveform, sr | |
| if (self.channels==1): | |
| # converting stereo to mono | |
| # by selecting the first channel | |
| new_waveform = waveform[:1,:] | |
| elif (self.channels==2): | |
| # converting mono to stereo | |
| # by duplicating the first channel | |
| new_waveform = torch.cat([waveform, waveform]) | |
| return new_waveform, sr | |
| def resample(self, waveform, sr): | |
| if (sr==self.sr): | |
| # no resampling needed | |
| return waveform, sr | |
| num_channels = waveform.shape[0] | |
| # resample first channel | |
| new_waveform = torchaudio.transforms.Resample(sr, self.sr)(waveform[:1,:]) | |
| if (num_channels) > 1: | |
| # resample second channel and merge the two | |
| re_two = torchaudio.transforms.Resample(sr, self.sr)(waveform[1:,:]) | |
| new_waveform = torch.cat([new_waveform, re_two]) | |
| return (new_waveform, self.sr) | |
| def pad_trunc(self, waveform, sr): | |
| num_channels, num_frames = waveform.shape | |
| max_len = sr//1000 * self.max_ms | |
| if (num_frames>max_len): | |
| # truncate signal to given length | |
| waveform = waveform[:,:max_len] | |
| else: | |
| # get padding lengths for beginning and end | |
| begin_ln = random.randint(0, max_len-num_frames) | |
| end_ln = max_len - num_frames - begin_ln | |
| # pad the audio with zeros | |
| pad_begin = torch.zeros((num_channels, begin_ln)) | |
| pad_end = torch.zeros((num_channels, end_ln)) | |
| waveform = torch.cat((pad_begin, waveform, pad_end), 1) | |
| return (waveform, sr) | |
| def mel_specgram(self, waveform, sr): | |
| mel_tfm = T.MelSpectrogram( | |
| sample_rate=sr, | |
| n_fft=1024, | |
| win_length=None, | |
| hop_length=512, | |
| center=True, | |
| pad_mode="reflect", | |
| power=2.0, | |
| norm="slaney", | |
| onesided=True, | |
| n_mels=128, | |
| mel_scale="htk") | |
| spec = mel_tfm(waveform) | |
| waveform = torchaudio.transforms.AmplitudeToDB(top_db=80)(spec) | |
| return waveform, sr | |
| def encodes(self, x): | |
| waveform, sr = torchaudio.load(x) | |
| waveform, sr = self.resample(waveform, sr) | |
| waveform, sr = self.pad_trunc(waveform, sr) | |
| waveform, sr = self.rechannel(waveform, sr) | |
| waveform, sr = self.mel_specgram(waveform, sr) | |
| return waveform | |
| class load_label_tfm(Transform): | |
| def __init__(self, metadata=processed_metadata): self.metadata = metadata | |
| def encodes(self, x): | |
| return self.metadata.loc[self.metadata['slice_file_name'] == x.name]['class'].item() | |
| # %% app.ipynb 6 | |
| repo_id = "Jimmie/urban8k" | |
| learner = from_pretrained_fastai(repo_id) | |
| # %% app.ipynb 14 | |
| categories = tuple(learner.dls.vocab) | |
| def classify_audio(audio): | |
| # use Path to open audio | |
| audio_path = Path(audio) | |
| pred,idx,probs = learner.predict(audio_path) | |
| return dict(zip(categories, map(float, probs))) | |
| # %% app.ipynb 16 | |
| title = "Environmental Sound Classification" | |
| description = """ | |
| This demo showcases how AI can be used to recognize environmental sounds. It focuses specifically on 5 classes: car_horn, children_playing, dog_bark, siren and street music | |
| When uploading audio, make sure it is in .wav format and is less than 4 seconds long. | |
| Enjoy! | |
| """ | |
| mic = gr.Audio(source='upload', type="filepath", label='Upload Audio File here') | |
| label = gr.outputs.Label() | |
| examples = list(data.ls()) | |
| intf = gr.Interface(fn=classify_audio, inputs=mic, outputs=label, examples=examples, | |
| title=title, description=description, cache_examples=False, | |
| auto_submit_duration=5) | |
| intf.launch(inline=False) | |