Update app.py

app.py

@@ -1,3 +1,196 @@
+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
 
-gr.load("models/ivrit-ai/whisper-13-v2-e2").launch()
+DEFAULT_MODEL_NAME = "ivrit-ai/whisper-13-v2-e2"
+BATCH_SIZE = 8
+
+print('start app')
+
+device = 0 if torch.cuda.is_available() else "cpu"
+if device == "cpu":
+    DEFAULT_MODEL_NAME = "ivrit-ai/whisper-13-v2-e2"
+
+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)
+openai_pipe=load_pipeline("openai/whisper-large-v3")
+default_pipe = load_pipeline(DEFAULT_MODEL_NAME)
+
+#pipe = None
+
+
+from gpustat import GPUStatCollection
+
+def update_gpu_status():
+    if torch.cuda.is_available() == False:
+        return "No Nvidia 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)
+        free_memory=gpu_memory[0] /(1024 *1024)
+        used_memory = (gpu_memory[1] - gpu_memory[0]) / (1024 * 1024)
+        
+        gpu_status = f"GPU: {gpu_info} Free Memory:{free_memory}MB   Total Memory: {total_memory:.2f} MB  Used 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()
+    sys_status=gpu_status+"\n\n"+cpu_status
+    return sys_status
+
+def refresh_status():
+    return update_status()
+
+
+@spaces.GPU
+def transcribe(audio_path, model_name):
+    print(str(time.time())+'  start transcribe ')
+    
+    if audio_path is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+    audio_path=audio_path.strip()
+    model_name=model_name.strip()
+    
+    global pipe
+    if model_name != pipe.model.name_or_path:
+        print("old model is:"+ pipe.model.name_or_path )
+        if model_name=="ivrit-ai/whisper-13-v2-e2":
+            pipe=openai_pipe
+            print(str(time.time())+" use openai model " + pipe.model.name_or_path)
+        elif model_name==DEFAULT_MODEL_NAME:
+            pipe=default_pipe
+            print(str(time.time())+" use default model " + pipe.model.name_or_path)
+        else:
+            print(str(time.time())+'  start load model ' + model_name)
+            pipe = load_pipeline(model_name)
+            print(str(time.time())+'  finished load model ' + model_name)
+    
+    start_time = time.time()  # Record the start time
+    print(str(time.time())+'  start processing and set recording start time point')
+    # Load the audio file and calculate its duration
+    audio = mp.AudioFileClip(audio_path)
+    audio_duration = audio.duration
+    print(str(time.time())+'   start pipe ')
+    text = pipe(audio_path, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, 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'}"
+    )
+
+    print(str(time.time())+'   return transcribe '+ text )
+    
+    return text, transcription_time_output
+
+@spaces.GPU
+def handle_upload_audio(audio_path,model_name,old_transcription=''):
+    print('old_trans:' + old_transcription)
+    (text,transcription_time_output)=transcribe(audio_path,model_name)
+    return text+'\n\n'+old_transcription, transcription_time_output
+
+graudio=gr.Audio(type="filepath",show_download_button=True)
+grmodel_textbox=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",
+        )
+groutputs=[gr.TextArea(label="Transcription",elem_id="transcription_textarea",interactive=True,lines=20,show_copy_button=True), 
+           gr.TextArea(label="Transcription Info",interactive=True,show_copy_button=True)]
+
+mf_transcribe = gr.Interface(
+    fn=handle_upload_audio,
+    inputs=[
+        graudio, #"numpy" or filepath
+        #gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        grmodel_textbox,
+    ],
+    outputs=groutputs,
+    theme="huggingface",
+    title="Whisper Transcription",
+    description=(
+        "Scroll to Bottom to show system status.  "
+        "Transcribe long-form microphone or audio file after uploaded audio! "
+    ),
+    allow_flagging="never",
+)
+
+
+demo = gr.Blocks()
+
+
+with demo:
+    gr.TabbedInterface([mf_transcribe, ], ["Audio",])
+    
+    with gr.Row():
+        refresh_button = gr.Button("Refresh Status")  # Create a refresh button
+    
+    sys_status_output = gr.Textbox(label="System Status", interactive=False)
+    
+    
+    # Link the refresh button to the refresh_status function
+    refresh_button.click(refresh_status, None, [sys_status_output])
+
+    # Load the initial status using update_status function
+    demo.load(update_status, inputs=None, outputs=[sys_status_output], every=2, queue=False)
+
+    graudio.stop_recording(handle_upload_audio,inputs=[graudio,grmodel_textbox,groutputs[0]],outputs=groutputs)
+    graudio.upload(handle_upload_audio,inputs=[graudio,grmodel_textbox,groutputs[0]],outputs=groutputs)
+    
+
+# Launch the Gradio app
+demo.launch(share=True)
+
+print('launched\n\n')