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, 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])

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() as translate:
#     with gr.Column():
#         gr.Markdown(f"# Realtime Whisper Large V3 Turbo (Translation): \n Transcribe and Translate 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 and Translation", value="")
#             latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
#             target_language_dropdown = gr.Dropdown(
#                 choices=["english", "french", "hindi", "spanish", "russian"],  
#                 label="Target Language",
#                 value="<|es|>"  
#             )
#         with gr.Row():
#             clear_button = gr.Button("Clear Output")

#         input_audio_microphone.stream(
#             translate_and_transcribe, 
#             [input_audio_microphone, output, target_language_dropdown], 
#             [output, latency_textbox], 
#             time_limit=45, 
#             stream_every=2, 
#             concurrency_limit=None
#         )
#         clear_button.click(clear, outputs=[output])

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

demo.launch()