app.py

import gradio as gr
import whisperx
import torch

# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if device == "cuda" else "int8"

# Load WhisperX model once
model = whisperx.load_model("base", device, compute_type=compute_type)

def transcribe(audio_file, language_code):
    if audio_file is None:
        return "Please upload audio"

    try:
        # Load audio directly from filepath
        audio = whisperx.load_audio(audio_file)

        # Transcribe (disable VAD for stability)
        result = model.transcribe(
            audio,
            language=language_code
        )

        # Load alignment model
        model_a, metadata = whisperx.load_align_model(
            language_code=result["language"],
            device=device
        )

        # Align words
        aligned_result = whisperx.align(
            result["segments"],
            model_a,
            metadata,
            audio,
            device,
            return_char_alignments=False
        )

        # Format output
        output_lines = []
        for seg in aligned_result["segments"]:
            if "words" in seg:
                for word in seg["words"]:
                    start = round(word["start"], 2)
                    end = round(word["end"], 2)
                    text = word["word"]
                    output_lines.append(f"[{start} - {end}] {text}")

        return "\n".join(output_lines)

    except Exception as e:
        return f"Error: {str(e)}"


# Gradio UI
demo = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(type="filepath", label="Upload Audio"),
        gr.Textbox(label="Language Code (en, hi, hi-IN, etc.)", value="en"),
    ],
    outputs=gr.Textbox(label="Word-level Transcription"),
    title="WhisperX Word-level Transcription",
    description="Upload audio + language code → get word timestamps"
)

demo.launch()