import torch import time import moviepy.editor as mp import psutil import gradio as gr import spaces from transformers import pipeline from transformers.pipelines.audio_utils import ffmpeg_read DEFAULT_MODEL_NAME = "distil-whisper/distil-large-v3" BATCH_SIZE = 8 device = 0 if torch.cuda.is_available() else "cpu" if device == "cpu": DEFAULT_MODEL_NAME = "openai/whisper-tiny" def load_pipeline(model_name): return pipeline( task="automatic-speech-recognition", model=model_name, chunk_length_s=30, device=device, ) pipe = load_pipeline(DEFAULT_MODEL_NAME) @spaces.GPU def transcribe(inputs, task, model_name): if inputs is None: raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") global pipe if model_name != pipe.model.name_or_path: pipe = load_pipeline(model_name) start_time = time.time() # Record the start time # Load the audio file and calculate its duration audio = mp.AudioFileClip(inputs) audio_duration = audio.duration text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] end_time = time.time() # Record the end time transcription_time = end_time - start_time # Calculate the transcription time # Create the transcription time output with additional information transcription_time_output = ( f"Transcription Time: {transcription_time:.2f} seconds\n" f"Audio Duration: {audio_duration:.2f} seconds\n" f"Model Used: {model_name}\n" f"Device Used: {'GPU' if torch.cuda.is_available() else 'CPU'}" ) return text, transcription_time_output from gpustat import GPUStatCollection def update_gpu_status(): if torch.cuda.is_available() == False: return "No Nviadia Device" try: gpu_stats = GPUStatCollection.new_query() for gpu in gpu_stats: # Assuming you want to monitor the first GPU, index 0 gpu_id = gpu.index gpu_name = gpu.name gpu_utilization = gpu.utilization memory_used = gpu.memory_used memory_total = gpu.memory_total memory_utilization = (memory_used / memory_total) * 100 gpu_status=(f"GPU {gpu_id}: {gpu_name}, Utilization: {gpu_utilization}%, Memory Used: {memory_used}MB, Memory Total: {memory_total}MB, Memory Utilization: {memory_utilization:.2f}%") return gpu_status except Exception as e: print(f"Error getting GPU stats: {e}") return torch_update_gpu_status() def torch_update_gpu_status(): if torch.cuda.is_available(): gpu_info = torch.cuda.get_device_name(0) gpu_memory = torch.cuda.mem_get_info(0) total_memory = gpu_memory[1] / (1024 * 1024) used_memory = (gpu_memory[1] - gpu_memory[0]) / (1024 * 1024) gpu_status = f"GPU: {gpu_info}\nTotal Memory: {total_memory:.2f} MB\nUsed Memory: {used_memory:.2f} MB" else: gpu_status = "No GPU available" return gpu_status def update_cpu_status(): import datetime # Get the current time current_time = datetime.datetime.now().time() # Convert the time to a string time_str = current_time.strftime("%H:%M:%S") cpu_percent = psutil.cpu_percent() cpu_status = f"CPU Usage: {cpu_percent}% {time_str}" return cpu_status def update_status(): gpu_status = update_gpu_status() cpu_status = update_cpu_status() return gpu_status, cpu_status def refresh_status(): return update_status() demo = gr.Blocks() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), gr.Textbox( label="Model Name", value=DEFAULT_MODEL_NAME, placeholder="Enter the model name", info="Some available models: distil-whisper/distil-large-v3 distil-whisper/distil-medium.en Systran/faster-distil-whisper-large-v3 Systran/faster-whisper-large-v3 Systran/faster-whisper-medium openai/whisper-tiny, openai/whisper-base, openai/whisper-medium, openai/whisper-large-v3", ), ], outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")], theme="huggingface", title="Whisper Transcription", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper" " checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length." ), allow_flagging="never", ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(type="filepath", label="Audio file"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), gr.Textbox( label="Model Name", value=DEFAULT_MODEL_NAME, placeholder="Enter the model name", info="Some available models: openai/whisper-tiny, openai/whisper-base, openai/whisper-medium, openai/whisper-large-v2", ), ], outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")], theme="huggingface", title="Whisper Transcription", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper" " checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length." ), allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) with gr.Row(): refresh_button = gr.Button("Refresh Status") # Create a refresh button gpu_status_output = gr.Textbox(label="GPU Status", interactive=False) cpu_status_output = gr.Textbox(label="CPU Status", interactive=False) # Link the refresh button to the refresh_status function refresh_button.click(refresh_status, None, [gpu_status_output, cpu_status_output]) # Load the initial status using update_status function demo.load(update_status, inputs=None, outputs=[gpu_status_output, cpu_status_output], every=2, queue=False) # Launch the Gradio app demo.launch(share=True)