app.py

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}")

# def 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)