app.py

import gradio as gr
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import librosa

# Model details
models = {
    "m3hrdadfi/wav2vec2-large-xlsr-persian-v3": None,
    "jonatasgrosman/wav2vec2-large-xlsr-53-persian": None,
    "AlirezaSaei/wav2vec2-large-xlsr-persian-fine-tuned": None
}

# Load models and processors
def load_model(model_name):
    model = Wav2Vec2ForCTC.from_pretrained(model_name)
    processor = Wav2Vec2Processor.from_pretrained(model_name)
    return model, processor

def transcribe(audio, model_name):
    if models[model_name] is None:
        models[model_name] = load_model(model_name)
    model, processor = models[model_name]

    audio_data, _ = librosa.load(audio, sr=16000)
    input_values = processor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True).input_values

    with torch.no_grad():
        logits = model(input_values).logits

    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)[0]
    return transcription

# Gradio app
with gr.Blocks(theme="compact") as demo:
    gr.Markdown("""
    <h1 style="color: #4CAF50; text-align: center;">Persian Speech-to-Text Models</h1>
    <p style="text-align: center;">Test the best Persian STT models in one place!</p>
    """)

    with gr.Row():
        audio_input = gr.Audio(source="upload", type="filepath", label="Upload your audio file")
        model_dropdown = gr.Dropdown(
            choices=list(models.keys()),
            label="Select Model",
            value="m3hrdadfi/wav2vec2-large-xlsr-persian-v3"
        )

    output_text = gr.Textbox(label="Transcription", lines=5, placeholder="The transcription will appear here...")

    transcribe_button = gr.Button("Transcribe", variant="primary")

    transcribe_button.click(
        fn=transcribe,
        inputs=[audio_input, model_dropdown],
        outputs=output_text
    )

    gr.Markdown("""
    <footer style="text-align: center; margin-top: 20px;">
        <p>Created with ❤️ using Gradio and Hugging Face</p>
    </footer>
    """)

demo.launch()