app.py

import gradio as gr
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import numpy as np

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-large-v3-turbo"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, 
    torch_dtype=torch_dtype, 
    low_cpu_mem_usage=True, 
    use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
    chunk_length_s=30,
    batch_size=8
)

def transcribe_audio(audio_file, task="transcribe", language="auto", return_timestamps=False):
    if audio_file is None:
        return "No audio file provided."
    
    try:
        generate_kwargs = {
            "task": task,
            "language": None if language == "auto" else language,
        }
        
        if task == "translate":
            generate_kwargs["task"] = "translate"
        
        result = pipe(
            audio_file, 
            return_timestamps=return_timestamps,
            generate_kwargs=generate_kwargs
        )
        
        if return_timestamps and "chunks" in result:
            formatted_result = []
            for chunk in result["chunks"]:
                timestamp = f"[{chunk['timestamp'][0]:.2f}s - {chunk['timestamp'][1]:.2f}s]"
                formatted_result.append(f"{timestamp} {chunk['text']}")
            return "\n".join(formatted_result)
        else:
            return result["text"]
            
    except Exception as e:
        return f"Error processing audio: {str(e)}"

def transcribe_microphone(audio_data, task="transcribe", language="auto", return_timestamps=False):
    if audio_data is None:
        return "No audio recorded."
    
    try:
        sample_rate, audio_array = audio_data
        audio_array = audio_array.astype(np.float32)
        audio_array = audio_array / np.max(np.abs(audio_array))
        
        generate_kwargs = {
            "task": task,
            "language": None if language == "auto" else language,
        }
        
        if task == "translate":
            generate_kwargs["task"] = "translate"
        
        result = pipe(
            {"array": audio_array, "sampling_rate": sample_rate},
            return_timestamps=return_timestamps,
            generate_kwargs=generate_kwargs
        )
        
        if return_timestamps and "chunks" in result:
            formatted_result = []
            for chunk in result["chunks"]:
                timestamp = f"[{chunk['timestamp'][0]:.2f}s - {chunk['timestamp'][1]:.2f}s]"
                formatted_result.append(f"{timestamp} {chunk['text']}")
            return "\n".join(formatted_result)
        else:
            return result["text"]
            
    except Exception as e:
        return f"Error processing audio: {str(e)}"

languages = [
    ("Auto Detect", "auto"),
    ("English", "en"),
    ("Chinese", "zh"),
    ("German", "de"),
    ("Spanish", "es"),
    ("Russian", "ru"),
    ("Korean", "ko"),
    ("French", "fr"),
    ("Japanese", "ja"),
    ("Portuguese", "pt"),
    ("Turkish", "tr"),
    ("Polish", "pl"),
    ("Catalan", "ca"),
    ("Dutch", "nl"),
    ("Arabic", "ar"),
    ("Swedish", "sv"),
    ("Italian", "it"),
    ("Indonesian", "id"),
    ("Hindi", "hi"),
    ("Finnish", "fi"),
    ("Vietnamese", "vi"),
    ("Hebrew", "he"),
    ("Ukrainian", "uk"),
    ("Greek", "el"),
    ("Malay", "ms"),
    ("Czech", "cs"),
    ("Romanian", "ro"),
    ("Danish", "da"),
    ("Hungarian", "hu"),
    ("Tamil", "ta"),
    ("Norwegian", "no"),
    ("Thai", "th"),
    ("Urdu", "ur"),
    ("Croatian", "hr"),
    ("Bulgarian", "bg"),
    ("Lithuanian", "lt"),
    ("Latin", "la"),
]

with gr.Blocks(title="Whisper Large V3 Turbo - Speech to Text") as demo:
    gr.Markdown("# 🎤 Whisper Large V3 Turbo - Speech to Text")
    gr.Markdown("Upload an audio file or record directly to get high-quality transcription using OpenAI's Whisper Large V3 Turbo model.")
    
    with gr.Tab("Upload Audio File"):
        with gr.Row():
            with gr.Column():
                audio_file = gr.Audio(
                    label="Upload Audio File",
                    type="filepath"
                )
                
                task_file = gr.Radio(
                    choices=[("Transcribe", "transcribe"), ("Translate to English", "translate")],
                    value="transcribe",
                    label="Task"
                )
                
                language_file = gr.Dropdown(
                    choices=languages,
                    value="auto",
                    label="Source Language"
                )
                
                timestamps_file = gr.Checkbox(
                    label="Return Timestamps",
                    value=False
                )
                
                submit_file = gr.Button("Transcribe Audio File", variant="primary")
            
            with gr.Column():
                output_file = gr.Textbox(
                    label="Transcription Result",
                    lines=10,
                    max_lines=20
                )
    
    with gr.Tab("Record Audio"):
        with gr.Row():
            with gr.Column():
                audio_mic = gr.Audio(
                    label="Record Audio",
                    sources=["microphone"]
                )
                
                task_mic = gr.Radio(
                    choices=[("Transcribe", "transcribe"), ("Translate to English", "translate")],
                    value="transcribe",
                    label="Task"
                )
                
                language_mic = gr.Dropdown(
                    choices=languages,
                    value="auto",
                    label="Source Language"
                )
                
                timestamps_mic = gr.Checkbox(
                    label="Return Timestamps",
                    value=False
                )
                
                submit_mic = gr.Button("Transcribe Recording", variant="primary")
            
            with gr.Column():
                output_mic = gr.Textbox(
                    label="Transcription Result",
                    lines=10,
                    max_lines=20
                )
    
    submit_file.click(
        transcribe_audio,
        inputs=[audio_file, task_file, language_file, timestamps_file],
        outputs=output_file
    )
    
    submit_mic.click(
        transcribe_microphone,
        inputs=[audio_mic, task_mic, language_mic, timestamps_mic],
        outputs=output_mic
    )
    
    gr.Markdown("### Features:")
    gr.Markdown("- **High Accuracy**: Powered by Whisper Large V3 Turbo model")
    gr.Markdown("- **CPU Optimized**: Optimized for 2-core CPU with 16GB RAM")  
    gr.Markdown("- **Multi-language**: Supports 99+ languages")
    gr.Markdown("- **Translation**: Can translate speech to English")
    gr.Markdown("- **Timestamps**: Optional word-level or sentence-level timestamps")
    gr.Markdown("- **Memory Efficient**: Uses chunked processing for better performance")

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0", 
        server_port=7860,
        show_error=True
    )