import gradio as gr from transformers import pipeline import numpy as np transcriber = pipeline( "automatic-speech-recognition", model="openai/whisper-base.en", return_timestamps=True, ) def transcribe_live(state, words_list, new_chunk): print(f"state: {state}") try: words_to_check_for = [word.strip() for word in words_list.split(",")] except: gr.Warning("Please enter a valid list of words to check for") words_to_check_for = [] stream = state.get("stream", None) previous_transcription = state.get("full_transcription", "") previous_counts_of_words = state.get( "counts_of_words", {word: 0 for word in words_to_check_for} ) if new_chunk is None: gr.Info("You can start transcribing by clicking on the Record button") print("new chunk is None") return state, previous_counts_of_words, previous_transcription sr, y = new_chunk # Convert to mono if stereo if y.ndim > 1: y = y.mean(axis=1) y = y.astype(np.float32) y /= np.max(np.abs(y)) if stream is not None: stream = np.concatenate([stream, y]) else: stream = y try: new_transcription = transcriber({"sampling_rate": sr, "raw": stream}) except Exception as e: gr.Error(f"Transcription failed. Error: {e}") print(f"Transcription failed. Error: {e}") return state, previous_counts_of_words, previous_transcription print(f"new transcription: {new_transcription}") full_transcription_text = new_transcription["text"] full_transcription_text_lower = full_transcription_text.lower() new_counts_of_words = { word: full_transcription_text_lower.count(word) for word in words_to_check_for } new_state = { "stream": stream, "full_transcription": full_transcription_text, "counts_of_words": new_counts_of_words, } print(f"new state: {new_state}") return new_state, new_counts_of_words, full_transcription_text with gr.Blocks() as demo: state = gr.State( value={ "stream": None, "full_transcription": "", "counts_of_words": {}, } ) filler_words = gr.Textbox(label="List of filer words", value="like, so, you know") recording = gr.Audio(streaming=True, label="Recording") word_counts = gr.JSON(label="Filler words count", value={}) transcription = gr.Textbox(label="Transcription", value="") recording.stream( transcribe_live, inputs=[state, filler_words, recording], outputs=[state, word_counts, transcription], stream_every=5, time_limit=60, ) demo.launch(show_error=True)