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| import gradio as gr | |
| import tensorflow as tf | |
| import librosa | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| # Mel Spectrogram parameters | |
| n_fft = 512 # FFT window length | |
| hop_length = 160 # number of samples between successive frames | |
| n_mels = 80 # Number of Mel bands | |
| fmin = 0.0 # Minimum frequency | |
| fmax = 8000.0 # Maximum frequency | |
| sampling_rate = 16000 | |
| def extract_mel_spectrogram(audio) -> np.ndarray: | |
| spectrogram = librosa.feature.melspectrogram(y=audio, sr=sampling_rate, hop_length=hop_length, | |
| n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, power=2.0) | |
| spectrogram = librosa.power_to_db(spectrogram, ref=np.max) | |
| #spectrogram = np.expand_dims(spectrogram, axis=-1) # Adding channel dimension for the model | |
| return spectrogram | |
| # Download model from Hugging Face Hub | |
| model_path = hf_hub_download(repo_id="kobrasoft/kobraspeech-rnn-cs", filename="kobraspeech.17-40.19.keras") | |
| model = tf.keras.models.load_model(model_path) | |
| def decode_batch_predictions(pred): | |
| input_len = np.ones(pred.shape[0]) * pred.shape[1] | |
| # Use greedy search. For complex tasks, you can use beam search | |
| results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0] | |
| # Iterate over the results and get back the text | |
| output_text = [] | |
| for result in results: | |
| result = label_to_string(result) | |
| output_text.append(result) | |
| return output_text | |
| def transcribe(audio_path): | |
| # Load audio | |
| audio, _ = librosa.load(audio_path, sr=sampling_rate) | |
| # Extract features | |
| features = extract_mel_spectrogram(audio) | |
| # Model expects batch dimension | |
| features = np.expand_dims(features, axis=0) | |
| # Predict | |
| prediction = model.predict(features) | |
| # Assuming you have a method to decode the prediction into text | |
| transcription = decode_batch_predictions(prediction) | |
| return transcription[0] | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=gr.inputs.Audio(source="microphone", type="filepath"), | |
| outputs="text", | |
| title="Kobraspeech RNN ASR demo (cs)", | |
| description="Upload an audio file or record your voice to get the transcription." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |