import torch import gradio as gr import ffmpeg import numpy as np import whisper MODEL_NAME = "large-v3" SAMPLE_RATE = 16000 device = "cuda" if torch.cuda.is_available() else "cpu" model = whisper.load_model(MODEL_NAME).to(device) def load_audio(file): try: # This launches a subprocess to decode audio while down-mixing and resampling as necessary. # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. out, _ = ( ffmpeg.input(file, threads=0) .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=SAMPLE_RATE) .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) ) except ffmpeg.Error as e: raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 def transcribe(audio_file, task): if audio_file is None: raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.") try: # Load audio audio = load_audio(audio_file.name) # Transcribe result = model.transcribe(audio, task=task, language="en") # Format output output = "" for segment in result["segments"]: start_time = segment["start"] end_time = segment["end"] text = segment["text"] output += f"[{format_timestamp(start_time)} -> {format_timestamp(end_time)}] {text}\n" return output except Exception as e: raise gr.Error(f"Error processing audio file: {str(e)}") def format_timestamp(seconds): minutes, seconds = divmod(seconds, 60) hours, minutes = divmod(minutes, 60) return f"{int(hours):02d}:{int(minutes):02d}:{seconds:.2f}" # Use specific Gradio components audio_input = gr.components.File(label="Audio file", file_types=["audio"]) task_input = gr.components.Radio(["transcribe", "translate"], label="Task", default="transcribe") output = gr.components.Textbox(label="Transcription with Timestamps") demo = gr.Interface( fn=transcribe, inputs=[audio_input, task_input], outputs=output, title=f"Whisper {MODEL_NAME}: Transcribe Audio with Timestamps", description=( f"Transcribe audio files with Whisper {MODEL_NAME}. " "Upload an audio file and choose whether to transcribe or translate. " "The output includes timestamps for each transcribed segment." ), ) if __name__ == "__main__": demo.launch()