app.py

import gradio as gr
import os
from sidlingvo import wav_to_lang
from huggingface_hub import hf_hub_download
import numpy as np

title = "Spoken Language Identification"

description = """
A demo of conformer-based spoken language identification.

Paper: https://arxiv.org/abs/2202.12163

Model: https://huggingface.co/tflite-hub/conformer-lang-id
"""

repo_id = "tflite-hub/conformer-lang-id"
model_path = "models"
hf_hub_download(repo_id=repo_id, filename="vad_short_model.tflite", local_dir=model_path)
hf_hub_download(repo_id=repo_id, filename="vad_short_mean_stddev.csv", local_dir=model_path)
hf_hub_download(repo_id=repo_id, filename="conformer_langid_medium.tflite", local_dir=model_path)

runner = wav_to_lang.WavToLangRunner(
    vad_model_file=os.path.join(model_path, "vad_short_model.tflite"),
    vad_mean_stddev_file=os.path.join(model_path, "vad_short_mean_stddev.csv"),
    langid_model_file=os.path.join(model_path, "conformer_langid_medium.tflite"))

def predict(wav_file):
    top_lang, probs = runner.wav_to_lang(wav_file)
    top_lang_prob = np.max(probs)
    return "Predicted language: " + top_lang + "\nProbability: " + str(top_lang_prob)

if __name__ == "__main__":
    demo = gr.Interface(
        fn=predict,
        inputs=gr.Audio(type="filepath"),
        outputs="text",
        title=title,
        description=description,)
    demo.launch()