import gradio as gr import torch from faster_whisper import WhisperModel import pandas as pd model_size = "large-v2" # get device device = "cuda:0" if torch.cuda.is_available() else "cpu" if device == "cuda:0": # Run on GPU with FP16 model_whisper = WhisperModel(model_size, device="cuda", compute_type="float16") # or Run on GPU with INT8 # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16") else: # Run on CPU with INT8 model_whisper = WhisperModel(model_size, device="cpu", compute_type="int8") def get_filename(file_obj): return file_obj.name.split("/")[-1] def audio_to_transcript(file_obj): # get all audio segments segments, _ = model_whisper.transcribe(file_obj.name, beam_size=5, vad_filter=True) print("start") start_segments, end_segments, text_segments = list(), list(), list() for segment in segments: start, end, text = segment.start, segment.end, segment.text start_segments.append(start) end_segments.append(end) text_segments.append(text) # save transcript into csv df = pd.DataFrame() df["start"] = start_segments df["end"] = end_segments df["text"] = text_segments print(df) return get_filename(file_obj), df ## Gradio interface headers = ["start", "end", "text"] iface = gr.Interface(fn=audio_to_transcript, inputs=gr.File(label="Audio file"), outputs=[ gr.Textbox(label="Name of the audio file"), gr.DataFrame(label="Transcript", headers=headers), ], allow_flagging="never", title="Audio to Transcript", description="Just paste any audio file and get its corresponding transcript with timeline.", ) iface.launch()