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import gradio as gr
import librosa
import numpy as np
import torch
from transformers import pipeline
language_classes = {
0: "Arabic",
1: "Basque",
2: "Breton",
3: "Catalan",
4: "Chinese_China",
5: "Chinese_Hongkong",
6: "Chinese_Taiwan",
7: "Chuvash",
8: "Czech",
9: "Dhivehi",
10: "Dutch",
11: "English",
12: "Esperanto",
13: "Estonian",
14: "French",
15: "Frisian",
16: "Georgian",
17: "German",
18: "Greek",
19: "Hakha_Chin",
20: "Indonesian",
21: "Interlingua",
22: "Italian",
23: "Japanese",
24: "Kabyle",
25: "Kinyarwanda",
26: "Kyrgyz",
27: "Latvian",
28: "Maltese",
29: "Mongolian",
30: "Persian",
31: "Polish",
32: "Portuguese",
33: "Romanian",
34: "Romansh_Sursilvan",
35: "Russian",
36: "Sakha",
37: "Slovenian",
38: "Spanish",
39: "Swedish",
40: "Tamil",
41: "Tatar",
42: "Turkish",
43: "Ukranian",
44: "Welsh"
}
username = "jpbello" ## Complete your username
model_id = "jpbello/Hubert_emotion-finetuned-common_language"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline("audio-classification", model=model_id, device=device)
# def predict_trunc(filepath):
# preprocessed = pipe.preprocess(filepath)
# truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
# model_outputs = pipe.forward(truncated)
# outputs = pipe.postprocess(model_outputs)
# return outputs
def classify_audio(filepath):
preds = pipe(filepath)
# preds = predict_trunc(filepath)
outputs = {}
for p in preds:
outputs[p["label"]] = p["score"]
return outputs
title = "Language Classification Model"
description = (
"Welcome to the Language Classification Model demo powered by Gradio and Hubert Emotion. "
"This model is trained to identify the language spoken in audio samples, making it a valuable tool "
"for language identification tasks. Upload an audio file, and let the model predict the spoken language "
"with confidence scores. Try it out with our provided example audio files to see the model in action!"
)
filenames = ['EN_0212.wav', "FR_0061.wav", "JP_0100.wav","AR_0019.wav"]
filenames = [[f"./{f}"] for f in filenames]
demo = gr.Interface(
fn=classify_audio,
inputs=gr.Audio(type="filepath"),
outputs=[gr.Label(label="Predictions")],
title=title,
description=description,
examples=filenames,
)
demo.launch() |