| # Import the Gradio library for creating web interfaces | |
| import gradio as gr | |
| # Import the pipeline module from transformers for using pre-trained models | |
| from transformers import pipeline | |
| # Import numpy for numerical operations | |
| import numpy as np | |
| # Initialize the automatic speech recognition pipeline using the Whisper base English model | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") | |
| # Define the transcription function that takes audio input and returns transcribed text | |
| def transcribe(stream,new_chunk): | |
| # Unpack the audio tuple into sample rate (sr) and audio data (y) | |
| sr, y = new_chunk | |
| # Convert the audio data to 32-bit float | |
| y = y.astype(np.float32) | |
| # Normalize the audio data to be between -1 and 1 | |
| y /= np.max(np.abs(y)) | |
| if stream is not None: | |
| stream = np.concatenate([stream, y]) | |
| else: | |
| stream = y | |
| # Use the transcriber to convert audio to text and return the result | |
| return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"] | |
| # Create a Gradio interface for the transcribe function | |
| demo = gr.Interface( | |
| # Specify the function to run | |
| transcribe, | |
| # Define the input component as an audio recorder with microphone source | |
| ["state", gr.Audio(sources=["microphone"], streaming=True)], | |
| # Specify the output component as text | |
| ["state", "text"], | |
| live = True | |
| ) | |
| demo.launch() |