app.py

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, pipeline

# Set environment variable to skip flash attention issues
os.environ["FLASH_ATTENTION_SKIP_CUDA_BUILD"] = "TRUE"

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16
MODEL_NAME = "openai/whisper-large-v3-turbo"

# Load model - removed attn_implementation for compatibility
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    MODEL_NAME, 
    torch_dtype=torch_dtype, 
    low_cpu_mem_usage=True, 
    use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(MODEL_NAME)

# Create pipeline with explicit tokenizer and feature extractor
pipe = pipeline(
    task="automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    chunk_length_s=10,
    torch_dtype=torch_dtype,
    device=device,
)

@spaces.GPU
def stream_transcribe(stream, new_chunk):
    start_time = time.time() 
    try:
        sr, y = new_chunk
        
        # Convert to mono if stereo
        if y.ndim > 1:
            y = y.mean(axis=1)
            
        y = y.astype(np.float32)
        y /= np.max(np.abs(y))
    
        if stream is not None:
            stream = np.concatenate([stream, y])
        else:
            stream = y
            
        transcription = pipe({"sampling_rate": sr, "raw": stream})["text"]
        end_time = time.time()
        latency = end_time - start_time

        return stream, transcription, f"{latency:.2f}"
    except Exception as e:
        print(f"Error during Transcription: {e}")
        return stream, str(e), "Error"

@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

        # Clean up the temporary file
        os.remove(filename)

        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

        # Clean up the temporary file
        os.remove(filename)

        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 ""

def clear_state():
    return None

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")
        state = gr.State()
        input_audio_microphone.stream(stream_transcribe, [state, input_audio_microphone], [state, output, latency_textbox], time_limit=30, stream_every=2, concurrency_limit=None)
        clear_button.click(clear_state, outputs=[state]).then(clear, outputs=[output])

with gr.Blocks() as file:
    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(sources="upload", type="numpy")
            output = gr.Textbox(label="Transcription", value="")
            latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
        with gr.Row():
            submit_button = gr.Button("Submit")
            clear_button = gr.Button("Clear Output")

        submit_button.click(transcribe, [input_audio_microphone, output], [output, latency_textbox], concurrency_limit=None)
        clear_button.click(clear, outputs=[output])

with gr.Blocks(theme=gr.themes.Ocean()) as demo:
    gr.TabbedInterface([microphone, file], ["Microphone", "Transcribe from file"])

demo.launch()