Delete app.py

app.py

@@ -1,97 +0,0 @@
-
-
-import spaces
-import torch
-import gradio as gr
-import tempfile
-import os
-import uuid
-import scipy.io.wavfile
-import time  
-import numpy as np
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, WhisperTokenizer, pipeline
-import subprocess
-subprocess.run(
-    "pip install flash-attn --no-build-isolation",
-    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
-    shell=True,
-)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16
-MODEL_NAME = "openai/whisper-large-v3-turbo"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    MODEL_NAME, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="flash_attention_2"
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(MODEL_NAME)
-tokenizer = WhisperTokenizer.from_pretrained(MODEL_NAME)
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=model,
-    tokenizer=tokenizer,
-    feature_extractor=processor.feature_extractor,
-    chunk_length_s=10,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-@spaces.GPU
-def transcribe(inputs, previous_transcription):
-    start_time = time.time() 
-    try:
-        filename = f"{uuid.uuid4().hex}.wav"
-        sample_rate, audio_data = inputs
-        scipy.io.wavfile.write(filename, sample_rate, audio_data)
-
-        transcription = pipe(filename)["text"]
-        previous_transcription += transcription
-
-        end_time = time.time()
-        latency = end_time - start_time
-        return previous_transcription, f"{latency:.2f}"
-    except Exception as e:
-        print(f"Error during Transcription: {e}")
-        return previous_transcription, "Error"
-
-@spaces.GPU
-def translate_and_transcribe(inputs, previous_transcription, target_language):
-    start_time = time.time()
-    try:
-        filename = f"{uuid.uuid4().hex}.wav"
-        sample_rate, audio_data = inputs
-        scipy.io.wavfile.write(filename, sample_rate, audio_data)
-
-        translation = pipe(filename, generate_kwargs={"task": "translate", "language": target_language} )["text"]
-
-        previous_transcription += translation
-
-        end_time = time.time()
-        latency = end_time - start_time
-        return previous_transcription, f"{latency:.2f}"
-    except Exception as e:
-        print(f"Error during Translation and Transcription: {e}")
-        return previous_transcription, "Error"
-
-def clear():
-    return ""
-
-with gr.Blocks() as microphone:
-    with gr.Column():
-        gr.Markdown(f"# Realtime Whisper Large V3 Turbo: \n Transcribe Audio in Realtime. This Demo uses the Checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers.\n Note: The first token takes about 5 seconds. After that, it works flawlessly.")
-        with gr.Row():
-            input_audio_microphone = gr.Audio(streaming=True)
-            output = gr.Textbox(label="Transcription", value="")
-            latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
-        with gr.Row():
-            clear_button = gr.Button("Clear Output")
-
-        input_audio_microphone.stream(transcribe, [input_audio_microphone, output], [output, latency_textbox], time_limit=45, stream_every=2, concurrency_limit=None)
-        clear_button.click(clear, outputs=[output])
-
-
-
-demo.launch()