app.py

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

# Load model and processor
processor = Wav2Vec2Processor.from_pretrained("Mustafaa4a/ASR-Somali")
model = Wav2Vec2ForCTC.from_pretrained("Mustafaa4a/ASR-Somali")

def transcribe(audio):
    waveform, sample_rate = torchaudio.load(audio)

    if sample_rate != 16000:
        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
        waveform = resampler(waveform)

    inputs = processor(waveform.squeeze(), sampling_rate=16000, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits

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

# Gradio Interface setup
interface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath", label="Upload Somali Audio (.wav)"),
    outputs=gr.Textbox(label="Transcription"),
    title="Somali-speech_to_text",
    description="Upload a Somali speech audio file (mono WAV, 16kHz) and get the text transcription."
)

# Launch the Gradio app and make it publicly available by using 'share=True'
interface.launch()  # Don't use share=True in Hugging Face Spaces