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| import gradio as gr | |
| from speechbrain.inference.VAD import VAD | |
| import torch | |
| import torchaudio | |
| import tempfile | |
| import os | |
| # Initialize the VAD model | |
| vad = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty") | |
| def perform_vad(audio_input): | |
| """ | |
| This function takes an audio tuple from Gradio, saves it to a temporary file, | |
| runs VAD, and then cleans up. | |
| """ | |
| if audio_input is None: | |
| return "Please upload an audio file.", None | |
| sample_rate, waveform_data = audio_input | |
| # --- START OF FIX --- | |
| # Convert numpy array to a torch tensor | |
| waveform_tensor = torch.from_numpy(waveform_data) | |
| # Ensure the tensor is 2D: [channels, num_samples] | |
| if waveform_tensor.ndim == 1: | |
| # If it's a mono file, unsqueeze to add the channel dimension | |
| waveform_tensor = waveform_tensor.unsqueeze(0) | |
| # We should now have a 2D tensor, which is what torchaudio.save expects. | |
| # We no longer add the extra batch dimension. | |
| # --- END OF FIX --- | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmpfile: | |
| temp_audio_path = tmpfile.name | |
| torchaudio.save(temp_audio_path, waveform_tensor, sample_rate, format="wav") | |
| try: | |
| # For VAD processing, we can use the tensor directly | |
| speech_segments = vad.get_speech_segments(waveform_tensor) | |
| if speech_segments.shape[0] == 0: | |
| return "No speech detected in the audio.", None | |
| output_text = "Detected Speech Segments (startTime, endTime in seconds):\n" | |
| output_json = [] | |
| for segment in speech_segments: | |
| start_time = round(segment[0].item(), 3) | |
| end_time = round(segment[1].item(), 3) | |
| output_text += f"- [{start_time}, {end_time}]\n" | |
| output_json.append({"startTime": start_time, "endTime": end_time}) | |
| return output_text, output_json | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}", None | |
| finally: | |
| if os.path.exists(temp_audio_path): | |
| os.remove(temp_audio_path) | |
| # --- Gradio Interface --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# SpeechBrain VAD Demo") | |
| gr.Markdown("Upload an audio file to detect speech segments...") | |
| audio_input = gr.Audio(type="numpy", label="Upload Your Audio") | |
| process_button = gr.Button("Detect Speech") | |
| with gr.Row(): | |
| text_output = gr.Textbox(label="Detected Timestamps") | |
| json_output = gr.JSON(label="JSON Output for Backend") | |
| process_button.click( | |
| fn=perform_vad, | |
| inputs=audio_input, | |
| outputs=[text_output, json_output] | |
| ) | |
| demo.launch() |