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| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| API_KEY = "682d2362-894c-800c-af30-a4c56b7f074b" | |
| FRAME_LENGTH = 1024 | |
| HOP_LENGTH = FRAME_LENGTH//4 | |
| sr = 22050 | |
| # Function to calculate amplitude envelope for each frame | |
| def amplitude_envelope(audio, frame_length, hop_length): | |
| return np.array([max(audio[i:i+frame_length]) for i in range(0, len(audio), hop_length)]) | |
| # Function to extract Short-Time Fourier Transform | |
| def st_fourier_transform(audio, n_fft, hop_length, type="power"): | |
| stft = librosa.stft(audio, n_fft=n_fft, hop_length=hop_length) | |
| if type=="power": | |
| stft_abs = np.abs(stft) ** 2 # use for a power spectrogram | |
| stft_dB = librosa.power_to_db(np.abs(stft_abs), ref=np.max) | |
| elif type=="amplitude": | |
| stft_abs = np.abs(stft) # use for an amplitude spectrogram | |
| stft_dB = librosa.amplitude_to_db(np.abs(stft_abs), ref=np.max) | |
| else: | |
| raise ValueError(f"Error: type should be 'power' or 'amplitude'") | |
| return stft_dB | |
| def extract_audio_features(file, key): | |
| if key != API_KEY: | |
| raise gr.Error("Invalid API key.") | |
| try: | |
| output = {} | |
| # Load audio with Librosa.load and resample | |
| file, _ = librosa.load(file, sr=sr) | |
| # Calculate amplitude envelope | |
| ae = amplitude_envelope(file, FRAME_LENGTH, HOP_LENGTH) | |
| output['amplitude_envelope'] = ae | |
| # Calculate root-mean-square energy | |
| rms = librosa.feature.rms(y=file, frame_length=FRAME_LENGTH, hop_length=HOP_LENGTH)[0] | |
| output['root_mean_square_energy'] = rms | |
| # Calculate zero-crossing rate using librosa.feature.zero_crossing_rate | |
| zcr = librosa.feature.zero_crossing_rate(y=file, frame_length=FRAME_LENGTH, hop_length=HOP_LENGTH)[0] | |
| output['zero_crossing_rate'] = zcr | |
| # Apply short-time fourier transform | |
| stft = st_fourier_transform(file, FRAME_LENGTH, HOP_LENGTH) | |
| # Extract the first 13 MFCCs | |
| mfccs = librosa.feature.mfcc(y=file, sr=sr, n_fft=FRAME_LENGTH, hop_length=HOP_LENGTH, n_mfcc=13) | |
| output['mfccs'] = mfccs | |
| # Computing first MFCCs derivatives (how they change from frame-frame over time) | |
| delta_mfccs = librosa.feature.delta(mfccs) | |
| output['delta_mfccs'] = delta_mfccs | |
| # Calculate spectral centroid | |
| sc = librosa.feature.spectral_centroid(y=file, sr=sr, n_fft=FRAME_LENGTH, hop_length=HOP_LENGTH)[0] | |
| output['spectral_centroid'] = sc | |
| # Calculate spectral bandwidth | |
| sban = librosa.feature.spectral_bandwidth(y=file, sr=sr, n_fft=FRAME_LENGTH, hop_length=HOP_LENGTH)[0] | |
| output['spectral_bandwidth'] = sban | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| output = f"An error occurred: {e}" | |
| return output | |
| # Create a function to generate a vertically stacked interface | |
| def create_transcription_interface(source): | |
| with gr.Blocks() as interface: | |
| gr.Markdown(""" | |
| Use microphone, upload .wav file. | |
| """) | |
| with gr.Column(): | |
| audio_input = gr.Audio(sources=source, type="filepath", label="Upload Audio") | |
| output = gr.JSON(label="Results") | |
| api_key_input = gr.Textbox(label="API Key", type="password") | |
| audio_input.change(fn=extract_audio_features, inputs=[audio_input, api_key_input], outputs=output) | |
| return interface | |
| # Create two interfaces (one for mic, one for file upload) | |
| mic_transcribe = create_transcription_interface("microphone") | |
| file_transcribe = create_transcription_interface("upload") | |
| demo = gr.TabbedInterface( | |
| [mic_transcribe, file_transcribe], | |
| ["Microphone Input", "Upload .wav file"], | |
| title="Audio Feature Extraction", | |
| ) | |
| demo.launch() |